Enterprise Application Integration (EAI) and modern iPaaS platforms have become two of the most strategically important – and resource-constrained – functions inside today’s enterprises. As organizations scale SaaS adoption, modernize core systems, and automate cross-functional workflows, integration teams face mounting pressure to deliver faster while upholding strict architectural, data quality, and governance standards.

AI has entered this environment with the promise of acceleration. But CIOs are discovering a critical truth:

Not all AI is built for the complexity of enterprise integrations – whether in traditional EAI stacks or modern iPaaS environments.

Generic coding assistants such as Cursor or Claude Code can boost individual productivity, but they struggle with the pattern-heavy, compliance-driven reality of integration engineering. What looks impressive in a demo often breaks down under real-world EAI/iPaaS conditions.

This widening gap has led to the rise of a new category: Vertical AI Development Agents – domain-trained agents purpose-built for integration and middleware development. Companies like CurieTech AI are demonstrating that specialized agents deliver not just speed, but materially higher accuracy, higher-quality outputs, and far better governance than general-purpose tools.

For CIOs running mission-critical integration programs, that difference directly affects reliability, delivery velocity, and ROI.

Why EAI and iPaaS integrations are not a “Generic Coding” problem

Integrations—whether built on legacy middleware or modern iPaaS platforms – operate within a rigid architectural framework:

• multi-step orchestration, sequencing, and idempotency
• canonical data transformations and enrichment
• platform-specific connectors and APIs
• standardized error-handling frameworks
• auditability and enterprise logging conventions
• governance and compliance embedded at every step

Generic coding models are not trained on this domain structure. They often produce code that looks correct, yet subtly breaks sequencing rules, omits required error handling, mishandles transformations, or violates enterprise logging and naming standards.

Vertical agents, by contrast, are trained specifically to understand flow logic, mappings, middleware orchestration, and integration patterns – across both EAI and iPaaS architectures. They don’t just generate code – they reason in the same structures architects and ICC teams use to design integrations.

This domain grounding is the critical distinction.

The hidden drag: Context latency, expensive context managers, and prompt fatigue

Teams experimenting with generic AI encounter three consistent frictions:

Context Latency

Generic models cannot retain complex platform context across prompts. Developers must repeatedly restate platform rules, logging standards, retry logic, authentication patterns, and canonical schemas.

Developers become “expensive context managers”

A seemingly simple instruction—“Transform XML to JSON and publish to Kafka”—
quickly devolves into a series of corrective prompts:

• “Use the enterprise logging format.”
• “Add retries with exponential backoff.”
• “Fix the transformation rules.”
• “Apply the standardized error-handling pattern.”

Developers end up managing the model instead of building the solution.

Prompt fatigue

The cycle of re-prompting, patching, and enforcing architectural rules consumes time and erodes confidence in outputs.

This is why generic tools rarely achieve the promised acceleration in integration environments.

Benchmarks show vertical agents are about twice as accurate

CurieTech AI recently published comparative benchmarks evaluating its vertical integration agents against leading generic tools, including Claude Code.
The tests covered real-world tasks:

• generating complete, multi-step integration flows
• building cross-system data transformations
• producing platform-aligned retries and error chains
• implementing enterprise-standard logging
• converting business requirements into executable integration logic

The results were clear: generic tools performed at roughly half the accuracy of vertical agents.

Generic outputs often looked plausible but contained structural errors or governance violations that would cause failures in QA or production. Vertical agents produced platform-aligned, fully structured workflows on the first pass.

For integration engineering – where errors cascade – this accuracy gap directly impacts delivery predictability and long-term quality.

The vertical agent advantage: Single-shot solutioning

The defining capability of vertical agents is single-shot task execution.

Generic tools force stepwise prompting and correction. But vertical agents—because they understand patterns, sequencing, and governance—can take a requirement like:

“Create an idempotent order-sync flow from NetSuite to SAP S/4HANA with canonical transformations, retries, and enterprise logging.”

…and return:

• the flow
• transformations
• error handling
• retries
• logging
• and test scaffolding

in one coherent output.

This shift – from instruction-oriented prompting to goal-oriented prompting—removes context latency and prompt fatigue while drastically reducing the need for developer oversight.

Built-in governance: The most underrated benefit

Integrations live and die by adherence to standards. Vertical agents embed those standards directly into generation:

• naming and folder conventions
• canonical data models
• PII masking and sensitive-data controls
• logging fields and formats
• retry and exception handling patterns
• platform-specific best practices

Generic models cannot consistently maintain these rules across prompts or projects.

Vertical agents enforce them automatically, which leads to higher-quality integrations with far fewer QA defects and production issues.

The real ROI: Quality, consistency, predictability

Organizations adopting vertical agents report three consistent benefits:

1. Higher-Quality Integrations

Outputs follow correct patterns and platform rules—reducing defects and architectural drift.

2. Greater Consistency Across Teams

Standardized logic and structures eliminate developer-to-developer variability.

3. More Predictable Delivery Timelines

Less rework means smoother pipelines and faster delivery.

A recent enterprise using CurieTech AI summarized the impact succinctly:

“For MuleSoft users, generic AI tools won’t cut it. But with domain-specific agents, the ROI is clear. Just start.”

For CIOs, these outcomes translate to increased throughput and higher trust in integration delivery.

Preparing for the agentic future

The industry is already moving beyond single responses toward agentic orchestration, where AI systems coordinate requirements gathering, design, mapping, development, testing, documentation, and deployment.

Vertical agents—because they understand multi-step integration workflows—are uniquely suited to lead this transition.

Generic coding agents lack the domain grounding to maintain coherence across these interconnected phases.

The bottom line

Generic coding assistants provide breadth, but vertical AI development agents deliver the depth, structure, and governance enterprise integrations require.

Vertical agents elevate both EAI and iPaaS programs by offering:

• significantly higher accuracy
• higher-quality, production-ready outputs
• built-in governance and compliance
• consistent logic and transformations
• predictable delivery cycles

As integration workloads expand and become more central to digital transformation, organizations that adopt vertical AI agents early will deliver faster, with higher accuracy, and with far greater confidence.

In enterprise integrations, specialization isn’t optional—it is the foundation of the next decade of reliability and scale.

Learn more about CurieTech AI here.

For CIOs, the conversation around AI has moved from innovation to orchestration, and project management, long a domain of human coordination and control, is rapidly becoming the proving ground for how intelligent systems can reshape enterprise delivery and accelerate transformation.

In boardrooms across industries, CIOs face the same challenge of how to quantify AI’s promise in operational terms: shorter delivery cycles, reduced overhead, and greater portfolio transparency. A 2025 Georgia Institute of Technology-sponsored study of 217 project management professionals and C-level tech leaders revealed that 73% of organizations have adopted AI in some form of project management.

Yet amid the excitement, the question of how AI will redefine the role of the project manager (PM) remains, as does how will the future framework for the business transformation program be defined.

A shift in the PM’s role, not relevance

Across industries, project professionals are already seeing change. Early adopters in the study report project efficiency gains of up to 30%, but success depends less on tech and more on how leadership governs its use. The overwhelming majority found it highly effective in improving efficiency, predictive planning, and decision-making. But what does that mean for the associates running these projects?

Roughly one-third of respondents believed AI would allow PMs to focus more on strategic oversight, shifting from day-to-day coordination to guiding long-term outcomes. Another third predicted enhanced collaboration roles, where managers act as facilitators who interpret and integrate AI insights across teams. The rest envisioned PMs evolving into supervisors of AI systems themselves, ensuring that algorithms are ethical, accurate, and aligned with business goals.

These perspectives converge on a single point: AI will not replace PMs, but it will redefine their value. The PM of the next decade won’t simply manage tasks, they’ll manage intelligence and translate AI-driven insights into business outcomes.

Why PMOs can’t wait

For project management offices (PMOs), the challenge is no longer whether to adopt AI but how. AI adoption is accelerating, with most large enterprises experimenting with predictive scheduling, automated risk reporting, and gen AI for documentation. But the integration is uneven.

Many PMOs still treat AI as an add-on, a set of tools rather than its strategic capability. This misses the point since AI is about augmenting judgment and automation. The organizations gaining a real competitive advantage are those embedding AI into their project methodologies, governance frameworks, and performance metrics with this five-point approach in mind.

1. Begin with pilot projects

Think small, scale fast. The most successful AI integrations begin with targeted use cases that automate project status reports, predict schedule slippage, or identify resource bottlenecks. These pilot projects create proof points, generate enthusiasm, and expose integration challenges early.

2. Measure value, not just activity

One common pitfall is adopting AI without clear performance metrics. PMOs should set tangible KPIs such as reduction in manual reporting time, improved accuracy in risk forecasts, shorter project cycle times, and higher stakeholder satisfaction. Communicating these outcomes across the organization is just as important as achieving them. Success stories build momentum, foster buy-in, and demystify AI for skeptical teams.

3. Upskill PMs

AI will only be as valuable as the people who use it. Nearly half of the surveyed professionals cited lack of a skilled workforce as a barrier to AI integration. Project managers don’t need to become data scientists, but they must understand AI fundamentals, how algorithms work, where biases emerge, and what data quality means. In this evolving landscape, the most effective PMs will combine data literacy with human-centered leadership, including critical thinking, emotional intelligence, and communication.

4. Strengthen governance and ethics

Increasing AI raises pressing ethical questions, especially when algorithms influence project decisions. PMOs must take the lead in establishing AI governance frameworks that emphasize transparency, fairness, and human oversight. Embedding these principles into the PMO’s charter doesn’t just mitigate risk, it builds trust.

5. Evolve from PMO to BTO

The traditional PMO focuses on execution through scope, schedule, and cost. But AI-driven organizations are shifting toward business transformation offices (BTOs), which align projects directly with strategic value creation through process improvement in parallel. A PMO ensures projects are done right. A BTO ensures the right projects are done. A crucial element of this framework is the transition from a Waterfall to an Agile mindset. The evolution of project management has shifted from rigid plans to iterative, customer-centric, and collaborative methods, with hybrid methodologies becoming increasingly common. This Agile approach is vital for adapting to the rapid changes brought by AI and digital disruption.

The new PM career path

By 2030, AI could manage most routine project tasks, such as status updates, scheduling, and risk flagging, while human leaders focus on vision, collaboration, and ethics. This shift mirrors past revolutions in project management from the rise of Agile to digital transformation, but at an even faster pace. But as organizations adopt AI, the risk of losing the human element persists. Project management has always been about people and aligning interests, resolving conflicts, and inspiring teams. However, while AI can predict a delay, it can’t motivate a team to overcome it. The PM’s human ability to interpret nuance, build trust, and foster collaboration remains irreplaceable.

A call to action

AI represents the next frontier in enterprise project delivery, and the next decade will test how well PMOs, executives, and policymakers can navigate the evolution of transformation. To thrive, organizations must invest in people as much as in platforms, adopt ethical, transparent governance, foster continuous learning and experimentation, and measure success by outcomes rather than hype.

For CIOs, the mandate is clear: lead with vision, govern with integrity, and empower teams with intelligent tools. AI, after all, isn’t a threat to the project management profession. It’s a catalyst for its reinvention, and when executed responsibly, AI-driven project management will not only deliver operational gains but also build more adaptive, human-centered organizations ready for the challenges ahead. By embracing it thoughtfully, PMs can elevate their roles from administrators to architects of change.

Artificial intelligence is the most transformative technology shift since the birth of cloud computing.

Two decades ago, cloud platforms changed how enterprises thought about infrastructure. Right now, as you’re reading this, AI platforms are changing how enterprises think about intelligence.

The parallels between the two are well worth highlighting. In the early 2000s, CIOs debated whether to build their own data centers or trust a shared platform like AWS. Now, 20 years on, they’re asking a similar question: should we build our own large language models, or build on them?

I believe that the lesson from the cloud era still applies. Competitive advantage comes from leveraging the platforms that already exist and innovating on top of them rather than owning the infrastructure. Let’s get into why that’s the case.

The cloud’s first lesson: Leverage, don’t reinvent

When the first generation of cloud services appeared, their broadest appeal was speed. Developers could launch applications in minutes instead of months.

However, while speed was the most obvious appeal here, the cloud’s real breakthrough was strategic. By handing off infrastructure management, companies could redirect their energy toward experience and innovation.

The enterprises that tried to replicate the “hyperscalers” by building their own clouds from scratch discovered how hard it was to keep up with the pace of platform evolution. Costs ballooned at the same time that velocity disappeared. Those who embraced the leverage model (using shared platforms as a foundation) moved faster and spent less.

AI is now at the same crossroads. The instinct to build proprietary models from the ground up feels familiar, but it’s no more the right move than it was with cloud. Large language models have become a new layer of digital infrastructure that is analogous to compute and storage in the cloud era. They are utilities that are powerful, scalable and continuously improving through collective use.

I believe that owning the plumbing no longer differentiates you, and that it never did. The question for leaders isn’t “Can we build our own model?” It’s “What unique value can we deliver by building upon one?”

The power of open ecosystems

The rise of cloud was never about one product. It was about an ecosystem that invited participation. I worked at AWS, and I can tell you that its greatest innovation was an architecture that encouraged others to build on top of it. Every API call became a building block for something new.

AI platforms are following the same pattern. Tools like OpenAI, Anthropic and others are offering open interfaces and SDKs that turn intelligence into an accessible service. This openness fuels compounding innovation in the form of an ecosystem that every developer, data scientist and business analyst can contribute to.

Enterprises that align with open ecosystems benefit from shared progress. They can experiment without owning the entire stack and move faster as the underlying technology improves. Closed systems, though, tend to stagnate. When innovation depends solely on internal capacity, growth slows, costs rise and talent disperses.

From what I’ve seen across my career, the future belongs to platforms that treat users as co-creators. Products and ecosystems scale exponentially because every user is also a contributor!

The feedback flywheel

Feedback is one of technology’s most underappreciated engines of progress. I remember AWS famously saying that 90% of its roadmap came directly from customer requests. When I was there, I saw firsthand how each improvement drove more usage, which generated more feedback, which drove more innovation.

AI systems are built on the same dynamic. Reinforcement learning, fine-tuning and user telemetry all feed the model’s evolution. Every query, correction or prompt becomes a signal that refines the next response.

This feedback flywheel is now extending into enterprise AI adoption. Each workflow, chat interaction and model output is an opportunity to learn. The organizations that intentionally design feedback loops to flow between users, data and developers evolve their systems faster than those treating AI as a static tool. The former will become industry leaders while the latter lags behind.

What does this look like it practice? Teams must instrument AI use cases with metrics, monitor accuracy and context, and close the loop quickly when things go wrong. Feedback is a strategy for continuous learning, not some trivial support function.

The most advanced AI organizations are the ones with the tightest feedback loops, not the biggest models.

Platform thinking inside the enterprise

What does all of this mean for CIOs and technology leaders? It means applying the principles of platform thinking within your own walls.

I tell my clients to start by viewing their enterprise not as a collection of systems, but as a platform others can build upon. Create reusable AI capabilities like data pipelines, governance frameworks and integration patterns that different business units can safely leverage. Encourage decentralized innovation by giving teams the guardrails and APIs to experiment.

In the cloud era, self-service infrastructure changed how developers worked. In the AI era, self-service intelligence is doing the same. Marketing teams generate insights from unstructured data, HR automates knowledge discovery for onboarding, finance uses AI-powered forecasting to model business outcomes, and so on and so forth. Each function builds on a shared foundation while adding its own flavor of domain expertise.

CIOs play the critical role of orchestrator. Their job is to ensure interoperability, security and ethical use while enabling freedom at the edge. That balance between control and creativity will define the next generation of enterprise leaders.

Avoiding the reinvention trap

There’s a natural temptation to build everything in-house, especially in technology-driven organizations. It feels safer and more controllable, but history shows how easily that instinct can slow progress.

I’ve seen enterprises that tried to build their own private clouds fail to match the scale or speed of public ones. The same is true of AI. Training proprietary models consumes extraordinary compute and talent, while the underlying platforms advance faster than any single company can replicate.

The smarter move is to differentiate at the application layer through data strategy, user experience and domain-specific integration. Build the intelligence that understands your business while also relying on established platforms for the generic cognition that everyone needs.

The organizations that thrive will be those that orchestrate AI across their ecosystems, not those that try to reinvent it in isolation.

The leadership imperative

AI represents a once-in-a-generation shift. However, like every major shift before it, the winners will be those who learn the right lessons from history.

The cloud taught us that leverage beats ownership, ecosystems beat silos and feedback beats static roadmaps. AI simply brings those lessons into a new domain.

For CIOs and senior technology leaders, the mandate is clear: build architectures that learn and that use open ecosystems to accelerate progress. Make feedback a cultural habit instead of an afterthought. Focus your talent on solving unique business problems instead of replicating what the platforms already provide.

The question isn’t whether AI will transform your enterprise; it already is. The question is whether you’ll build on the right platform to make that transformation sustainable, ethical and fast.

I believe that the future belongs to leaders who understand that innovation is about what you enable, not ‘just’ about what you own.

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Banks have always been technology pioneers, yet many are now prisoners of their own legacy. Despite spending more on IT than any other major industry and funneling over $2.8 trillion into digital transformation since 2011, too many retail banks still can’t deliver the seamless digital experiences customers expect.

The loyalty crisis: Spending more, delivering less

My company, Baringa, recently surveyed 4,000 customers and 400 banking executives across the UK and US, revealing a widening disconnect between customer expectations and what banks can deliver.

More than one in three customers (35%) have switched banks in the past five years, most in search of better digital experiences, not better rates. And 68% of banking executives admit that their existing technology architecture actively hinders their ability to meet customer needs.

Mobile is now the dominant channel, with 45% of customers using it as their primary means of banking. Yet, it’s also the most requested area for improvement, with 44% wanting a better mobile experience. Customers want personalized, intuitive and secure interactions but instead, they encounter friction.

The result? Diminishing loyalty in an age when switching bank accounts is as simple as a few taps on a screen.

Legacy technology: The hidden barrier to progress

The problem isn’t a lack of investment. Yes, the cost is high, but effective treatment strategies are available to manage this condition. It’s the age and complexity of the systems beneath the surface that is the true problem. Our survey found that 63% of banks still rely on code written before the year 2000, while 67% say their entire technology stack would fail if the oldest systems stopped working. Even more worryingly, 77% report that only “one or two people” in their organization still have the skills to maintain this code and most are nearing retirement.

In other words, critical national infrastructure in banking runs on software designed before the internet age. This outdated technology creates three compounding problems:

• Operational fragility. Legacy code and unsupported platforms make outages and compliance failures more likely. One executive described systems still reliant on 8-inch floppy drives for critical updates, a vivid metaphor for how far behind the curve some institutions remain.
• Run-cost burden. According to Gartner, over 75% of IT budgets in many financial institutions are consumed by maintaining these old systems, starving innovation budgets and slowing transformation.
• Inhibited agility. Modernization programs overrun as banks struggle to deal with legacy architecture and data complexities. Indeed, 94% of large banking transformations exceed planned timelines, leaving customer improvements delayed and diluted.

The result is a vicious cycle. Every dollar spent patching and upgrading outdated systems is a dollar diverted from the modernization that could restore customer loyalty.

Breaking the cycle: A new technology blueprint

There is a path forward, but it demands decisive action. From our work across global banking and markets, we consistently see these issues and we believe these can be addressed over the long term with the following three strategies.

Refocus: Lead with purpose, not platforms

Banks need to start with truly understanding why (customer needs) and how their customers want to interact (experience) with their services, then define how they are going to differentiate. Technology alone will not win back loyalty. Sometimes, the greatest return comes from improving service, trust or personalization rather than layering on more tech.

Research from Forrester shows that banks leading in personalized digital experiences achieve up to 25% higher retention and a 20% uplift in cross-sell success. Conversely, institutions that rush infrastructure spend without redefining customer value risk building faster versions of the same old experience.

Replace or renovate: Build the modern digital spine

For many banks, the technological foundations are simply too old to adapt. If two-thirds of institutions say their operations would cease if legacy systems failed, the cost of inaction now exceeds the cost of replacement.

The answer lies in defining a technology strategy around a digital spine. A modular architecture that allows agility, integration and personalization at scale and is centered around three design principles:

• Build the core technology and data spine internally to retain strategic differentiation and control.
• Buy external solutions for commodity or repeatable processes that don’t define the customer experience.
• Integrate third-party and marketplace services for specialized or fast-evolving capabilities, enabling banks to scale quickly without adding new legacy dependencies.

This build-buy-integrate approach allows banks to modernize strategically and maintain control where it matters, while reducing cost and delivery risk elsewhere.

It’s also how challenger banks are winning. Monzo, for instance, built its business on this philosophy, focusing on customer differentiation through a lightweight, API-driven core. As its ex-CEO, TS Anil, recently noted, Monzo has become “a scaling, profitable digital bank with a world-class user experience that customers don’t just like, but love.”

The culture shift: Continuous transformation

Finally, transformation can no longer be treated as a one-off program. Modernization must become a continuous capability, not a project with an end date. For banks to break free of legacy constraints, the following considerations are essential:

• Transformation never ends. Change on this scale will be a multiyear, multidimensional journey. Change leaders should aim to secure a consistent stream of investment that allows the organization to build enduring capabilities. Every technology and data initiative should align with long-term strategic goals, creating compounding value across the organization.
• Full organizational shift. Transformation is everyone’s responsibility. While technology drives change, this transformation can’t be owned by IT alone. From boardroom to back office, everyone needs to be committed to making change happen. When transformation becomes embedded in organizational DNA rather than delegated to technical teams, banks can sustain the pace of change their customers demand.

The bottom line

Banks stand at a crossroads. 68% of executives acknowledge that legacy technology is holding them back. Every quarter spent maintaining outdated systems compounds risk, cost and customer attrition.

But those that act now and redefine their customer proposition, rebuild their digital spine and embed continuous change, will turn technology from a constraint into a competitive edge.

The future belongs to banks that leave legacy behind and build loyalty by design.

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The increasing hype around AI has exceeded any other technology shift, perhaps ever. This has been met with a corresponding amount of investment. Gartner estimates worldwide spending on AI through 2025 will be nearly $1.5 trillion. Despite the staggering amount, most organizations continue to grapple with a chronic gap between promise and realized value.

The most widely recognized data point to support this comes from an MIT report from earlier this year that reveals 95% of gen AI pilots fail. A McKinsey study also found that nearly 80% of companies use gen AI, yet almost as many report no significant impact to the bottom line.

But there’s proof AI is working, if modestly. The 2025 Cisco AI Readiness Index shows 13% of all companies get consistently measurable returns from AI. So while this is a minority, leading organizations are starting to see value. But the origins of that value increasingly stem from leadership clarity, strategic alignment, and execution, not the technology itself. The Cisco AI Readiness Index measured this and found 99% of companies that have realized value from AI have a well-defined strategy that embraces change, and includes formal programs to help employees get comfortable with the new technology.

Today’s CEOs and CIOs face a generational inflection point. They must redefine success for AI not as a means of cost-cutting, but as a driver for capacity creation, innovation, and human-centric outcomes. The path forward requires breaking down work, reassessing where automation helps, and empowering talent to focus on growth and transformation.

Setting the stage: AI promise vs. reality

Among many CIOs, the biggest challenge is the C-Suite and board know they need AI but aren’t sure what for. This creates unrealistic expectations that AI will be a panacea to all problems and send productivity skyrocketing. When the outcomes are unrealistic, a successful project may be deemed unsuccessful because the initial goals were incorrect. A contributing factor to this problem is that many business units don’t have the KPIs to create the business metrics to measure.

An example of this is with contact centers where AI agents could be used for agent coaching, virtual agents, scheduling, scoring calls, note taking and more. Measuring the value of these can be difficult, so many businesses have defaulted to cutting costs by reducing agents. This can backfire if not done in a measured way. Klarna, for instance, eliminated about 700 agents, saw customer service scores tank, and then hired people back. This wasn’t a technology problem but rather leadership didn’t put the right plan in place to understand the impact.

Diagnosing the problem: Tech limitations or leadership gaps?

Issues with achieving ROI on AI and automation technology efforts are often confused with the diagnosis of the issue at hand. A lack of tech readiness concerning integrating complex data and scaling automation remains a challenge. More often than not, however, the issue that gets acted upon may be less with the technology itself and more with the way technology efforts are governed and led. This would indicate the obstacles that exist to technology’s successful implementation lie more with the board than the code and cloud setup. Failures happen because business leaders prioritize expediency driven by market hype instead of thinking about genuine business transformation.

It’s also important that prior to AI projects being kicked off, there’s clear business alignment and an understanding of the metrics being measured to calculate ROI. Projects launched as isolated technology experiments without a well-defined business case tied to a strategic priority are hard to measure and often vague in value, leading to projects that are easy to cut.

The leadership inflection point: Beyond cost cutting

The business world sits at a leadership inflection point where for the first time, workforce transformation will be more than just human. With the rapid adoption of AI and autonomous agents, leaders now face complex decisions about how value is derived and maintained within their organizations. This shift requires cutting costs but also reimagining the relationship between human skills and the technical capabilities of AI, which ensures organizational cultures and processes can adapt to this new era of hybrid workforces.

“Everyone’s been racing to build more AI models, compute, and agents, but the real bottleneck to enterprise AI adoption isn’t supply, it’s that enterprises don’t know where or how to use AI to do real work,” says Greg Shewmaker, CEO of enterprise intelligence company r.Potential. “We believe the missing piece is the coordination layer between human and digital work, where you can capture actual workforce demand, generate realistic and deployable configurations of human and AI capabilities, and tie them to real business outcomes. If we don’t get this right, the next wave of automation won’t just reshape companies, it’ll destabilize work itself.” 

His point underscores what many executives miss: AI’s promise isn’t just a technological or operational challenge, it’s an existential leadership one. As the boundaries blur between tasks suited for humans and those automated by machines, IT and business leaders will need to focus on maximizing value creation through deep integration of people, technology, and culture.​

So it becomes critical for the C-suite to reconsider timelines related to investments and expand capacity in accordance with tech and market needs. This shift in human capital management involves being able to forecast the future workforce and deploy human resources in sync with machine-based intelligence. Innovation should take precedence that’s less about adding to current performance and more about ensuring organizations can remain agile and ready to facilitate innovation in terms of related infrastructure and preparedness to reinvent themselves.

Breaking down work and value creation

Understanding the key components of work is essential to developing understandable ROI from AI. Any returns must consider the adoption of technology and the transformation of processes. The goal should be to leverage AI to amplify human effort in areas that require human judgment, empathy, and creativity rather than in areas where there’s only repetition of tasks, thereby assigning human resources to higher-value supervisory and human roles where they can be most productive and valuable.

The key to success is to refocus on enabling talent to drive revenue growth and innovation through AI. So the goal is to apply AI strategically to liberate highly-skilled people from working on the 80% of any job that can, should, and must be automated so they can focus on the last 20%, which drives new revenue growth, customer loyalty, and innovation breakthroughs.

The AI era will reshape every industry, and if CIOs and CEOs aren’t evolving, the AI investment will be wasted. The key to realizing real value in AI is to ensure leadership is future-ready and embraces new skills and change. It’s not about being the best coder in the room but instilling the right leadership structure. This involves a leadership mentality that uses AI to further human-centric goals and not simply fill an operational spreadsheet with AI data. This requires strict ethics and governance modeled in every aspect of decision-making, and firm alignment on the business side where every AI project has a defined purpose for the organization.

Si se cogiesen todas las verduras que se tiran cada año en España, se podrían hacer millones de platos de sopa. En concreto, saldrían 390 millones, como calcula Too Good To Go aplicando unas cuantas recetas a los 117 millones de kilos de verduras que acaban en el contenedor. Es un ejemplo concreto de un problema que tiene una escala mucho mayor, tanto en kilos como en alcance geográfico. Ya en 2023 la consultora McKinsey estimaba que, de todos los alimentos que se producían en el mundo, acababa en la basura entre el 30 y el 40%. El problema está repartido por toda la cadena de valor y no se limita solo a lo que ocurría una vez entraban en el hogar del consumidor.

Todo esto genera costes. Uno es el económico. McKinsey señalaba entonces que suponía unas pérdidas de unos 545.000 millones de euros al año. Otro es el medioambiental, porque para producir esos alimentos que no se consumen se genera una huella de carbono que no tiene ninguna contrapartida positiva. Al tiempo, una vez que llegan a los vertederos se convierten en un problema nuevo, con una cuenta nueva de costes para el medio ambiente. Y a todo a ello hay que sumar la cuestión social, ya que se está tirando comida en un planeta en el que todavía muchas personas pasan hambre.

Pero esto no es solo una cuestión de sostenibilidad, de responsabilidad social corporativa o de los departamentos de innovación y logística o de estrategia de negocio, también es una cuestión en la que la tecnología tiene mucho que decir. Entra ya dentro del ámbito de influencia del CIO, aunque no siempre se tenga presente a primera vista cuando se abordan estas cuestiones.

“Ese es el punto. No pensamos en tecnología cuando el tomate se pone pocho, pensamos en la parte humana”, responde Antonio Díaz Otero, gerente de cuentas estratégicas de Phenix España, startup que trabaja en soluciones que reducen el desperdicio alimentario en todos los eslabones de la cadena alimentaria. Lo humano importa, pero también lo tecnológico. La aproximación al problema es muy de procesos, “muy ingenieril, por así decirlo”, y requiere una estrategia TI que toque todas las fases, desde que el producto sale de la tierra hasta que el consumidor final lo tiene en su nevera. “Hay que pensar a lo largo de la vida de ese producto, que va perdiendo valor. Se trata de extraer el mayor valor posible”, resume el experto.

La investigación de McKinsey ya advertía que se podía reducir el desperdicio entre un 50 y un 70% con una mejor metodología, que tocase desde las mejoras de las técnicas de cultivo hasta la gestión de los procesos de venta y los tiempos de llegada del alimento al consumidor final.

El papel de la tecnología

Muchas compañías han aplicado ya la innovación contra el desperdicio alimentario para perfilar mejores productos y convencer a la ciudadanía de su potencial, diseñando desde neveras a hornos más innovadores hasta creando soluciones que permiten acceder a alimentos que están ya en los límites de su vida útil. Pero esta no es una revolución que toque solo al momento del consumo, sino que impacta también en las fases previas. La estrategia de TI permite optimizar procesos y reducir el desperdicio alimentario en la cadena de producción.

“La tecnología hoy día nos ayuda en dos niveles, preventivo y reactivo”, confirman desde el equipo de Sostenibilidad de Nestlé España. En el primero, usan “software estadístico para mejorar la precisión del forecast”, lo que reduce “el sesgo humano y el optimismo” para centrarse en lo que dice el histórico de datos, la estacionalidad y las tendencias y evitar así la sobreestimación. En el segundo, monitorizan stocks en tiempo real. “Esto permite detectar productos que potencialmente podrían caducar y lanzar así acciones rápidas, como promociones, para evitar que se conviertan en desperdicio”, indican.

En resumidas cuentas, la tecnología posibilita que las compañías del sector puedan conocer mejor los procesos y saber qué está ocurriendo, para tomar decisiones más informadas y acertadas. Al aplicarla a las diferentes fases, se van atajando potenciales focos de desperdicio, desde la propia producción a los procesos de venta.

En Nestlé usan “software estadístico para la previsión y Power BI para análisis y seguimiento de vida útiles” y evalúan incorporar inteligencia artificial “en los pronósticos de demanda y mejorar aún más la precisión”. “Nuestro objetivo principal al aplicar la tecnología era abordar problemas muy concretos relacionados con el desperdicio alimentario a lo largo de toda la cadena de valor”, explican. Así, trabajaron primero la trazabilidad y la visibilidad de cada etapa, para lograr “anticipar con mayor precisión situaciones que puedan derivar en pérdidas”. “En segundo lugar, necesitábamos disponer de información en tiempo real que nos permitiera comparar la previsión de ventas con los niveles reales de stock”, señalan, para poder detectar qué no se va a vender a tiempo y activar mecanismos que atajen que se convierta en un potencial desperdicio.

“En conjunto, la tecnología nos permitió transformar un proceso tradicionalmente reactivo en un modelo predictivo y eficiente, en el que la toma de decisiones se adelanta a los problemas y reduce de manera significativa el desperdicio alimentario”, resumen.

“La fórmula del éxito es un compendio de servicio y tecnología”, señala Díaz Otero, que lista la automatización, el procesado de datos y su analítica, la parametrización de procesos, la estadística avanzada, el análisis continuo o la inteligencia artificial como las herramientas clave que ayudan a comprender qué está ocurriendo. “Llegamos a ser como el canario en la mina”, indica, ya que gracias a las TI se logra ver los problemas antes de que ocurran.

Y esto es especialmente importante en un sector, como es el de la alimentación, en el que los márgenes pueden ser muy ajustados. Ocurre con la distribución, ya que los supermercados afrontan márgenes muy bajos y lograr una buena eficiencia es clave para una mejor rentabilidad económica. “El diablo está en los detalles”, recuerda el experto. Saber que algo va a caducar y darle vidilla a sus ventas o gestionar mejor los frescos (que son muy populares en España, pero tienen un ciclo muy corto) logra optimizar los datos económicos. Un mix de tecnología y buenas prácticas consigue una “mejora continua”.

El reto de la compliance normativa

La reducción del desperdicio alimentario no tiene un impacto directo notable, todavía, en las decisiones de compra de la ciudadanía. De hecho, el I Estudio Triodos Bank Conductas sostenibles de la población española concluye que se desperdicia aún muchos alimentos en los hogares españoles (y más que se hará en la campaña navideña) y que solo el 37,1% de las personas tiene en cuenta “el impacto ambiental y social de los alimentos que compra y consume”. Pero si aún no es un factor decisivo en cómo se ordena la cesta de la compra, sí es uno que la industria de la alimentación ha empezado a tener muy presente en los últimos años.

Díaz confirma que existe un interés claro en estos temas. “La situación ha cambiado radicalmente”, explica. Las empresas del sector se enfrentan a un “tsunami legislativo” sobre desperdicio alimentario, que obliga de una manera o de otra a actuar. Aun así, el experto insiste que esta es también “una oportunidad para la mejora”.

La Unión Europea cuanta ya con una normativa que crea un marco común, que no solo marca patrones de actuación contra el desperdicio alimentario sino también contra el textil. Este mes de septiembre, el Parlamento aprobó un paquete legislativo, que ha establecido objetivos vinculantes que tendrán que ser introducidos en las normas de cada uno de los Estados miembros antes del 31 de diciembre de 2030. En el procesamiento y fabricación de alimentos, se debería reducir en un 10% el desperdicio. En “comercio minorista, los restaurantes, los servicios de alimentación y los hogares”, será un 30%.

En el caso español, se aplica también la Ley de Prevención de Pérdidas y Desperdicio Alimentario, que obliga a prevenirlo y a dar salida a los excedentes (por ejemplo, con donaciones) antes de que se conviertan en simple basura. “La tecnología facilita el cumplimiento de la Ley 1/2025 de prevención del desperdicio alimentario en España”, confirman desde Nestlé, ya que les deja realizar medición y trazabilidad, ser proactivos, ganar transparencia, tener “flexibilidad escalable” o crear planes de acción automatizados. “En conjunto, la tecnología convierte la gestión del desperdicio en un proceso predictivo, eficiente y conforme a la ley, garantizando reducción de pérdidas y cumplimiento normativo”, indican.

Nuevos productos, nuevas oportunidades

El CIO y su departamento se convierten así en una palanca para afrontar los retos del presente y lograr mejorar la eficiencia para ser más sólidos de cara al futuro. Gracias a las tecnologías punteras, “se puede reducir bastante” el desperdicio alimentario, como confirma Díaz. En su caso, están viendo una reducción media del 50% en el primer año, que llega al 80% en algunos casos.

Pero, además, enfrentarse al desperdicio puede ser una palanca indirecta para la innovación. En el caso de los supermercados, especialmente en un mercado atomizado en el que hay compañías de ámbito regional mucho más pequeñas que las grandes multinacionales, es el empujón para la digitalización, con todo lo que esto abre. En paralelo, y más de forma general para la industria, este conocimiento optimizado de lo que está ocurriendo en sus líneas de producción permite encontrar potenciales nuevas ideas, mejorando el aprovechamiento de recursos. Nestlé ha convertido los posos del café de su fábrica de Girona en materia prima para biocombustible.

As enterprises accelerate their digital transformation journeys, they are facing a growing challenge around modernizing infrastructure while maintaining control, compliance, and performance across increasingly distributed environments. The convergence of IT and OT systems, historically managed in silos, can become key to unlocking new operational agility, real-time insights, and secure automation. However, with the creation of data shifting from centralized data centers to the edge, the conventional approach of implementing sovereign clouds may no longer work, and a new dimension of sovereignty is emerging that demands a fundamental change in cloud strategy.

In mission-critical use cases across aerospace, defense, industrial/manufacturing, energy, and healthcare, the stakes are high. These industries require infrastructure that not only meets performance and reliability standards but also complies with strict regulatory requirements. Traditional cloud models, built around centralized architectures, can fall short in delivering the regional autonomy and processing locality that are needed by these organizations.

Data everywhere, decisions at the edge

Today’s enterprise environments will increasingly be influenced by data that is generated and processed at the edge, such as on factory floors, in remote installations, and across mobile platforms. This shift is driven by the need for low-latency decision-making, bandwidth efficiency, and operational resilience. AI, automation, and real-time analytics are fueling this transformation, making edge-native infrastructure a strategic imperative.

However, sovereignty now is no longer just about where data is stored. It’s also about where data is processed and how decisions are made. Enterprises must now consider inference sovereignty (AI models processing sensitive data locally), operational sovereignty (autonomous systems complying with local laws), and telemetry sovereignty (governing metadata flows to central systems). These factors are reshaping how organizations design, deploy, and govern their cloud infrastructure.

The challenge: Fragmentation, compliance, and control

As industries look to the growth of the intelligent edge, enterprises will continue to face significant obstacles. These challenges include:

• Fragmented IT/OT environments hinder visibility and automation.
• Legacy systems resist integration with cloud-native platforms.
• Regulatory requirements demand strict control over data residency and processing locality.
• Vendor lock-in and opaque infrastructure limit flexibility and innovation.
• Internal expertise gaps, slow adoption of edge-native architectures, and AI governance.

These issues are especially acute in industries where uptime, compliance, and security are non-negotiable.

The opportunity: Sovereign cloud at scale

To meet changing demands, enterprises are turning to private and hybrid cloud solutions that span core, edge, and far-edge environments. Sovereign cloud infrastructure enables organizations to maintain full control over data, workloads, and compliance, without compromising scalability or performance.

At Wind River, we’ve architected our cloud platform from the ground up to support sovereign deployments. Our commercially hardened stack, Wind River Cloud Platform (based on the open source StarlingX), Wind River Analytics, and Wind River Conductor, empowers enterprises to enforce sovereignty policies not only over where data resides, but also over where and how it is processed, analyzed, and acted upon.

This ensures compliance, operational autonomy, and security across highly distributed, mission-critical environments. Sovereignty must extend seamlessly across core and edge, supporting unified deployments from the largest data center to the smallest, most remote facility.

The path forward: Strategy, technology, and partnership

To succeed, enterprises must take a structured approach. It will be important to unify IT and OT systems to enable seamless data flow and governance. They must plan to adopt cloud-native platforms that support containerized workloads and open interfaces. It will be helpful to deploy private/hybrid cloud infrastructure to balance control with scalability. Additionally, enterprises should look to implement edge-native solutions for latency-sensitive, mission-critical operations. Simultaneously, it will be essential to enforce sovereignty policies across data residency, processing locality, and AI inference governance.

And just as important, companies must choose the right partners, such as technology providers with deep expertise in intelligent edge, cloud-native software, and mission-critical deployments. Sovereign-ready platforms, like those from Wind River, offer the flexibility, transparency, and control needed to build cloud infrastructure on your own terms.

As the next wave of innovation moves to the edge, sovereignty can be critical to enterprise success. Those who embrace this shift will be better positioned to lead in a world where control, compliance, and confidence are not optional but essential.

Start Your Sovereign Cloud Journey

From edge to core, Wind River enables unified, scalable, and consistent operations — wherever data resides. Build, manage, and secure your cloud infrastructure today with Wind River. Learn more at: https://www.windriver.com/products/sovereign-cloud

About the Author

Sandeep Modhvadia

Chief Product Officer

As Wind River Chief Product Officer, Sandeep Modhvadia is responsible for driving product strategy and product management, playing a critical role in Wind River’s leadership advancing the software-defined future of mission-critical systems.

He has over two decades of technology and product management experience. Most recently at Acuity Brands, he led product management for its cloud software business as part of the Intelligent Space Group, focused on sustainability, automation, and optimization of buildings. Before Acuity, he founded and led the product and solutions team for the Google Cloud Automotive and Manufacturing group, launching its platforms for connected vehicles and connected factories. Prior to Google, he held multiple product, sales, and marketing leadership positions at Microsoft.

He holds a BSc degree in computer science from University College London.

The new CIO mandate

As CIOs enter 2026, the urgency surrounding AI adoption and data sovereignty has reached a critical turning point. Artificial intelligence is now embedded in every strategic conversation—from operational efficiency to board-level risk—and data sovereignty has become the linchpin of regulatory readiness, security posture, and competitive differentiation. The question is no longer whether to pursue AI-driven transformation. It’s how to do so with enough speed, compliance, and architectural integrity to keep pace with an increasingly volatile digital landscape.

Why speed to sovereignty matters

Industry research reveals a striking pattern. While 95% of enterprise leaders plan to build their own AI and data platformwithin the next thousand days, only 13% are currently on track. Those who are succeeding are realizing up to five times the ROI of their peers, largely because they have established sovereign, AI-ready foundations that unify data, governance, and operational control. 

With sovereignty now a proxy for resilience, hundreds of enterprises will make this foundational decision every day over the next 1,000 days. The value is clear—and the gap between those who act quickly and those who delay will widen rapidly. You can assess your own readiness in 15 seconds to understand where you stand. 

The forces increasing the pressure on CIOs

CIOs today face a convergence of challenges that elevate sovereignty from a strategic initiative to a mission‑critical requirement. Most organizations are burdened by fragmented data estates spanning legacy systems, multi‑cloud deployments, and siloed analytics environments, each of which hinders AI readiness. 

At the same time, global regulatory mandates—including the EU AI Act, U.S. Executive Orders, and increasing data localization requirements—demand transparent, governed, and explainable AI systems. These regulatory demands come on top of talent bottlenecks: Some sources estimate that globally, AI-talent demand exceeds supply by more than 3:1. 

Finally, many enterprises are moving away from the old model of “move data to AI” toward “bring AI to governed data,” embedding models and inference engines directly inside controlled, compliant environments, reducing exposure and accelerating outcomes.

A 120-day path to data and AI sovereignty

To respond to these pressures, forward‑leaning enterprises are adopting a structured 120-day path that delivers fast, stable, and compliant AI readiness:

Days 0–30: Establish a unified AI and data foundation capable of connecting major data sources, enforcing consistency, and enabling analytics without excessive data movement.

Days 30–90: Introduce governance and policy controls, including encryption, lineage, auditability, and regulated access frameworks.

Days 90–120: Begin secure AI operationalization by integrating model preparation, vector indexing, inference pipelines, and hybrid-cloud controls within the governed perimeter.

This rapid cadence shifts sovereignty from aspiration to reality and enables enterprises to compete in the agentic AI era. 

The CIO’s four sovereignty challenges—and how leading organizations are solving them

As organizations accelerate their AI roadmaps, four challenges consistently define the maturity curve:

1. Modernizing legacy systems without disrupting mission-critical applications
2. Achieving unified hybrid orchestration across on-premises systems, multiple clouds, and edge environments
3. Automating data preparation and AI lifecycle management so adoption does not overwhelm IT operations
4. Upskilling teams in vector search, embedding pipelines, hybrid operations, and autonomous AI operations

Enterprises overcoming these barriers are investing in unified architectures, intelligent automation, and continuous capability development.

From foundation to long-term advantage

Establishing sovereign AI and data foundations is not merely a compliance exercise; it is a long‑term competitive strategy. EDB’s findings show that the “Deeply Committed” 13% are not just experimenting—they are unlocking scalable AI and data platforms that yield 5x the ROI.

Such foundations enable enterprises to:

• Scale securely in hybrid environments 
• Adapt quickly to evolving regulations and market demands 
• Maintain operational continuity and resilience 

This is the architecture of the next decade of advantage.

The bottom line for CIOs

2026 will be the inflection point for AI and data, and sovereignty is the operating model of this era. Competitive pressure is generating a widening innovation gap between leaders and followers—up to 87% of enterprises risk falling behind if they do not commit.

Enterprises that build governed, AI‑ready foundations within months rather than years will lead the next wave of technological and competitive transformation. In 2026, sovereignty delivered at speed is the bedrock of innovation, resilience, and sustainable advantage.

To learn more, visit us here. 

Organizations face rising pressure to deliver measurable results from technology investments. Yet, despite expanding their software portfolios, many struggle to realize their full value. Fragmented systems, siloed data, and isolated AI efforts often stall transformation. The real opportunity lies in uniting these investments into a connected ecosystem that powers daily operations while unlocking new levels of agility, resilience, and growth.

SAP helps companies derive business value with SAP Business Suite, our integrated offering of SAP business applications, data, and AI, all powered by the underlying SAP Business Technology Platform (SAP BTP). The combined power of integrated applications, harmonized data, and AI provides customers with a transformation engine that creates a cycle of innovation and improvement. It enables access to real-time data insights, streamlined and integrated workflows, consistency across processes, collaboration, and enhanced market responsiveness.

Transforming technology spend to business outcomes

In July 2025, IDC published a whitepaper to understand and report the business value that current SAP customers get from their use of SAP BTP in conjunction with key SAP business applications, specifically SAP Cloud ERP, SAP SuccessFactors, and SAP Intelligent Spend including SAP Ariba. IDC interviewed 15 SAP customers who use SAP BTP in conjunction with SAP S/4HANA, SuccessFactors, or Ariba.

The findings revealed that organizations using SAP BTP benefit from significant improvements in operational quality and efficiency, developer productivity, data-driven decision-making, and business agility. In fact, on average, interviewed organizations reported achieving annual benefits worth $13.88 million per organization ($259,400 per 1,000 employees) in the following areas.

Application development and automation benefits

Interviewed organizations using SAP BTP reported a 164% increase in SAP application extensions, delivered 41% faster. On average, SAP Build, SAP BTP’s unified application development and process automation solution, enabled development teams to achieve 46% higher productivity. With integration across SAP and third-party systems, customers can rapidly develop custom applications and automate core business processes such as onboarding, compliance tracking, and spend management. IDC reported customers saying:

Foundation for impactful new customer-facing solutions (SAP S/4HANA):

“Our smart metering wouldn’t have been possible without SAP BTP. We even use AI integrated with S/4HANA Customer Service to classify and route customer inquiries based on metadata and sentiment, which would not have been feasible without SAP BTP.”

Flexibility of low-code environment for specific functionalities (SAP Ariba):

“SAP BTP helps with integrations to some of our other systems, ensuring master data consistency. Also, SAP BTP’s low-code environment empowers different departments to build micro-apps that help manage spending more effectively than before.” 

Integration benefits

SAP BTP’s single, scalable AI assisted integration solution, SAP Integration Suite, unifies fragmented data and applications, enabling real-time connectivity across SAP and third-party systems. Study participants reported a 29% efficiency gain for teams managing business processes and a 43% faster resolution of process errors, as integration and automation reduced manual effort. With a unified integration layer, organizations gain real-time analytics, improved collaboration, and faster decision-making—turning complexity into operational efficiency and innovation. IDC reported customers saying:

Ease of integrating SAP Ariba to broader SAP ecosystem (SAP Ariba): 

“One of the most valuable aspects of SAP BTP has been its ability to integrate Ariba with S/4HANA … without SAP BTP, the integration would have taken significantly longer and required more resources.”

Compliance benefits from automation (SAP SuccessFactors): 

“We’ve automated the management of high-risk licenses with SAP BTP, simplifying the process of tracking qualifications and training for our transient workforce. This saves time and ensures compliance.”

Platform benefits

IDC’s research shows that SAP BTP delivers platform-wide benefits that strengthen performance, resilience, and business impact across SAP environments. Improved integration, monitoring, and automation reduced unplanned downtime by 90% on average. In fact, study participants completed 187% more innovative projects, delivered 44% faster. These gains—spanning reliability, innovation, and agility—enabled faster decision-making, improved customer experiences, and new revenue opportunities, while driving cross-functional value for both IT and business teams. IDC reported customers saying:

Foundation for positive interactions with customers (SAP S/4HANA): 

“We use SAP BTP integrated with S4/HANA customer service to classify and give metadata on customer questions and emails. We even have information about customer mood—are they mad?—then that gets more urgency. None of this would be possible without SAP BTP.” 

Foundation for real-time decision-making (SAP Ariba): 

“Before SAP BTP, our data modeling process took so long that we relied on more static, snapshot-style analysis rather than a continuous stream of data. Now, with SAP BTP providing real-time data updates and forecasts, users can make decisions with more information at their fingertips.”

SAP application optimization benefits

Study participants reported significant application-specific benefits from using SAP BTP across SAP Cloud ERP, SAP SuccessFactors, SAP Intelligent Spend, and SAP Ariba. On average, IDC calculated customers will realize three-year benefits worth $32.68 million per organization ($610,500 per 1,000 employees), with total three-year investment costs of $5.31 million per organization ($99,200 per 1,000 employees) based on a net-present value financial calculation. These benefits and costs would yield an average three-year ROI of 516%, with payback happening on average, after eight months, demonstrating the significant value they are realizing by using SAP BTP. IDC reported customers saying:

Cost savings through better understanding of spending: 

“SAP BTP with SAP Ariba helps us manage our vendor relationships and monitor how much money we’re spending, ensuring we’re not duplicating capabilities with vendor spend. Additionally, it allows us to take advantage of early pay discounts and other cost-saving opportunities.”

Get started today

Customers who unite their investments into a connected ecosystem of applications, data, and AI unlock new levels of agility, resilience, and growth. SAP BTP achieves this for SAP Business Suite and is intentionally engineered to integrate, automate, extend, and build AI-powered business applications and processes across the enterprise. Read dozens of customer examples, across all sizes and industries, who are using SAP BTP to unlock innovation together with their SAP Business Suite applications. 

And if you’re inspired to learn more, get started today with the following resources: 

• Watch the IDC webinar with Tony Olvet
• Read the full IDC white paper
• Read the IDC summary infographic
• Learn more about SAP BTP  

Compliance is no longer the brake on digital transformation. It is the steering system that determines how fast and how far innovation can go. In sectors such as healthcare, insurance, manufacturing, and banking, regulation defines how fast and how far innovation can progress. When compliance becomes an architectural principle rather than a procedural constraint, it transforms from a cost center to a competitive edge.

But in the past decade, leading enterprise transformation across these industries, I’ve learned that compliance isn’t the enemy of innovation. It’s the foundation of digital confidence. When handled strategically, compliance can evolve from a passive checklist into an active driver of resilience, trust and growth.

The enterprises that thrive in today’s regulated world share a common trait: they design their technology, data and culture to make compliance an enabler, not a barrier.

The compliance paradox

Across regulated industries, the paradox is striking. Regulations grow more complex each year, yet the demand for agility and innovation grows just as fast.

• In healthcare, HIPAA, FDA and CMS guidelines shape how patient data flows and how AI models can be used in clinical or administrative decisions.
• In insurance, frameworks such as NAIC, SOC 2 and emerging state-level data protection acts determine how claims, underwriting and member engagement systems are designed.
• In manufacturing, ISO standards and environmental disclosures require traceability across the entire production lifecycle.
• And in banking, AML, KYC, Basel III and now AI-model-risk rules require transparency at every level of algorithmic decision-making.

Each industry has its own acronym soup of regulation, but the underlying challenge is the same: enterprises must prove what they know, how they know it and how responsibly they use it. For CIOs, this means leading ecosystems that are innovative, interoperable and fully auditable simultaneously.

From burden to differentiator

In one large healthcare transformation I led, the audit process for claims and provider data reconciliation took more than a month and consumed hundreds of manual hours. By embedding audit trails directly into workflow engines and metadata layers, we reduced preparation time by 70% and achieved complete transparency for regulators and internal reviewers.

This experience reinforced a key lesson: compliance should be built into the architecture, not appended after deployment.

I’ve seen similar results in other sectors.

• In insurance, predictive underwriting models were facing long delays due to regulatory explainability reviews. We built an AI governance layer that automatically tracked model lineage, dataset evolution and decision thresholds. The review cycle was shortened from six weeks to two and the same system later became the benchmark for model transparency across the enterprise.
• In manufacturing, a digital twin initiative used IoT data to monitor production quality. Initially designed for efficiency, it later became the foundation for audit-ready traceability; every material change, machine calibration and test record became part of a verifiable digital thread.
• And in banking, I’ve seen model-risk governance evolve from compliance paperwork into real-time dashboards. These systems can generate “trust reports” visualizing every variable used by credit or fraud models and making them defensible before regulators even ask.

These examples prove a point: compliance, when operationalized, becomes a differentiator. It transforms oversight into foresight.

Why the mindset must shift

Technology rarely fails because of a lack of innovation. It fails when organizations lack the governance maturity to scale innovation responsibly.

Too often, compliance is viewed as a bottleneck. It’s a scalability accelerator when embedded early.

According to Gartner, organizations with mature data-governance practices are three times more likely to achieve measurable business outcomes from AI programs. McKinsey’s analysis shows that AI deployments in regulated sectors with built-in compliance design achieve 20–30% faster adoption and reduce audit findings by half.

The shift begins when leaders see compliance not as external policing but as internal assurance. A well-designed governance framework turns regulation into predictability. Predictability, in turn, builds trust, and trust is what enables adoption at scale.

In one cross-industry transformation roundtable I facilitated, a manufacturing CIO said something that stayed with me: “Compliance doesn’t slow us down. It prevents us from having to stop.” That insight captures the new reality. In regulated industries, digital maturity is measured not by how quickly you deploy AI, but by how confidently you can defend and explain it.

Governance as a growth engine

When governance and compliance converge, they unlock a feedback loop of trust. Consider a payer-provider network that unified its claims, care and compliance data into a single “truth layer.” Not only did this integration reduce audit exceptions by 45%, but it also improved member-satisfaction scores because interactions became transparent and consistent.

• In manufacturing, integrated governance platforms now allow plant managers to monitor non-conformance trends and compliance risks in real time. Instead of waiting for a quarterly audit, teams can act within hours, preventing both downtime and regulatory penalties.
• In banking, machine-learning models for AML detection can now explain why a transaction was flagged, not just that it was. This explainability builds regulator confidence, which in turn accelerates approval for new AI-based risk tools.

The pattern is consistent: when compliance data feeds into operational decision-making, it creates a growth multiplier. Transparency isn’t just a legal requirement; it’s a market advantage. When governance and compliance share data pipelines instead of separate dashboards, they move from passive monitoring to active performance management, transforming risk control into business acceleration.

The CIO’s leadership imperative

No transformation from compliance to confidence happens without leadership alignment. The CIO sits at the intersection of technology, policy and culture and therefore carries the greatest influence over whether compliance is reactive or proactive.

Here are four imperatives every CIO in a regulated enterprise should champion:

1. Treat governance as architecture, not administration

Governance is not documentation. Its design. CIOs must ensure that auditability, traceability and explainability are engineered into systems from day one.

For example, instead of creating external audit logs, modern architectures can use blockchain-based or immutable metadata records to self-document every change. In my experience, systems built this way reduce compliance reporting time by 40–50% while improving internal confidence in data quality.

2. Unite data, risk and compliance under a single operating model

Many enterprises still treat compliance as a department instead of a discipline. The CIO must align data governance, risk management and IT controls into one cohesive framework.

Cross-functional governance councils that include compliance officers, business heads and data owners help make compliance a shared accountability not an afterthought.

3. Humanize compliance through transparency

Technology maturity alone is not enough. The workforce must trust the system. When employees understand how AI or analytics systems make decisions, they become more confident using them.

In one insurance contact center, we trained representatives on how the AI recommendation engine worked. Within two months, adoption rose 37% and call-resolution accuracy improved significantly. Transparency builds human alignment.

4. Champion ethical AI as the next compliance frontier

AI ethics is no longer philosophical; it’s operational. The CIO must ensure algorithms are tested for fairness, bias and explainability before deployment. Tools like Google’s What-If Tool and IBM’s AI Fairness 360 provide practical methods for continuous assurance.

As regulatory frameworks like the EU AI Act and US Algorithmic Accountability Act evolve, ethical compliance will define enterprise reputation. CIOs who prepare early will not just pass audits they’ll earn stakeholder trust.

Measuring Progress: CIOs should define success not only by audit completion rates but by trust readiness metrics, for example, governance-maturity scores, audit-cycle speed or AI-model explainability indexes. These indicators convert compliance from a legal requirement into a performance KPI, signaling to boards and regulators that trust is being operationalized.

Ultimately, the modern CIO’s role extends far beyond systems integration. It’s about trust integration connecting people, policy and platforms under a single banner of accountability.

From compliance to confidence

Confidence is not the absence of regulation; it’s mastery of it. A confident enterprise doesn’t fear audits because its systems are inherently explainable. It doesn’t delay innovation because its teams understand how to govern data responsibly. It doesn’t treat compliance as a paperwork exercise; it sees it as a performance framework. Consider what “confidence” looks like across industries:

• In healthcare, it’s the ability to trace every AI-supported clinical recommendation back to source data.
• In insurance, it’s the assurance that pricing or claim decisions can be justified algorithmically.
• In manufacturing, it’s having a digital thread that ties every product to its quality, safety and sustainability metrics.
• In banking, it’s demonstrating that customer risk models are explainable, unbiased and resilient under regulatory scrutiny.

Confidence grows when leadership builds systems that are transparent by design, not by request.

 This shift is gaining policy traction worldwide. The EU AI Act requires enterprises to maintain verifiable documentation on AI systems’ training data, bias tests and human oversight. Similarly, the proposed U.S. Algorithmic Accountability Act pushes organizations to conduct regular impact assessments. Together, these frameworks formalize what leading CIOs already practice: governance as a continuous, auditable process rather than a reactive audit cycle.

According to Deloitte’s 2025 outlook, 70% of CEOs in regulated industries now see “digital trust” as a direct growth lever. Companies that combine compliance automation with clear governance frameworks experience 20% higher stakeholder trust ratings and outperform peers on market reputation. In practical terms, moving from compliance to confidence means:

• Embedding trust checkpoints into product development life cycles.
• Establishing AI assurance frameworks that test every model for fairness, accuracy and auditability.
• Building explainable data architectures where every decision is traceable.
• Creating a culture of shared accountability between compliance, data and product teams.

The result is not just regulatory alignment, it’s operational resilience and reputational strength.

The future of regulated transformation

As AI reshapes every sector, regulation will continue to evolve faster than technology stacks. Enterprises that succeed will be those that internalize compliance as part of their DNA.

In healthcare, this means using AI responsibly to support clinical and administrative workflows. In insurance, it means linking predictive analytics to transparent customer journeys. In manufacturing, it means aligning IoT and sustainability reporting under one trusted data fabric. In banking, it means moving from algorithmic opacity to algorithmic accountability. The future will belong to organizations that govern as they innovate.

CIOs are at the epicenter of this shift. CIOs are now the custodians of digital trust, responsible not only for running systems but for ensuring that every line of code and every algorithm earns confidence from regulators, customers and employees. The real competitive edge in a regulated world isn’t speed or scale. It’s trust engineered through transparency and sustained by governance-driven leadership.

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Caught between change and stability, many companies find themselves hesitating on how to square the two. The pace of change is increasing in the age of AI, and the weight of making inspired choices has only become more critical. GS Caltex, one of Korea’s leading refining companies, faced the same dilemma and recently embraced a new guiding principle of good risk taking — a phrase reportedly often heard in GS Caltex meetings, and initially proposed by company CEO Hur Sae-hong. “Once the word ‘good’ was added to ‘risk-taking,’ a culture began to spread where people are willing to attempt any challenge,” says CIO, CDO, and DX Center head Lee Eunjoo.

Amid growing uncertainties around crude oil prices and product demand, intensifying competition over production scale, and demographic decline, the value of good risk taking is pushing the company to pursue new opportunities and innovation. And a changing mindset is reshaping the organization from within.

The AI platform changing the enterprise

Even without any top-down mandate, it’s common at GS Caltex to see not just IT but LOB teams in production, sales, finance, legal, PR, and HR building and using AI agents in their day-to-day work. Finance, for instance, recently built an FAQ agent and asked Lee’s team to review it. “It’s incredibly rewarding to see employees actively using the new technologies provided by the DX Center.”

So far, they’ve created more than 50 agents, including ones that support pre-job safety briefings for partner company staff, review crude oil purchase contracts, automate a complex medical expense reimbursement process, and automatically classify and analyze gas station customer feedback.

All of these agents were developed on AiU, the company’s in-house gen AI service platform launched in June this year, which combines AI with yu, the Korean word for oil, and is also a play on “AI for you,” reflecting its role as AI tailored to each employee.

Lee says AiU is the clearest expression of the company’s approach to transformation. “It’s not just about DX anymore but DAX, combining digital with AI transformation,” she says. “From our production sites to headquarters, we’re rolling out initiatives that let every employee experience it all side by side. That’s how we’re reshaping ourselves into an energy company that uses AI broadly and with confidence.”

A secret to its rapid success is because no one feels pressured to build a perfect agent. “People are much more willing to try things and experiment,” says Lee. From the DX Center’s standpoint, that mindset has made it possible to support a growing number of AI projects with a relatively small team. “Plus, the AiU playground lets employees build and test agents themselves, which makes AI feel far more approachable and familiar in their day-to-day work,” she adds.

An AI agent platform might sound like something only developers can use, but AiU is designed so non-experts can easily work with it. The experience isn’t very different from ChatGPT as GS Caltex deliberately embedded AiU into the side of core business systems that employees check every day, so they’d naturally encounter and use AI in their daily workflows. Even if they don’t build agents themselves, employees can still ask the AI questions using internal company data, and search across both external information and internal systems at once.

It’s only been a few months since AiU officially launched, and around 85% of employees are now regular users, and nearly the entire workforce has tried it at least once. “Most of our production and technical staff work in a mobile-only environment without desktops,” Lee says. “The fact 95% of them have already used AiU shows just how fast the platform is spreading.”

Sowing seeds of success

AiU drew strong interest from employees even during its pilot stage. The DX Center began discussing AI service adoption in 2023, and in 2024, the team built a pilot service on AWS in just a few days. Although it was an early version with only basic UI, more than 300 employees participated and shared the features and requirements they needed. This underscored just how many people were eager to bring AI into their work.

Through this pilot, the DX Center was able to clearly identify what kinds of problems employees wanted to solve with AI, and which capabilities they needed most. The team then considered whether to adopt an external solution or develop one in house. In the end, they chose to build on MISO, the AI transformation platform developed by the GS Group, and add GS Caltex–specific capabilities on top. The entire development took about six months.

In designing AiU’s technical architecture, Lee focused most heavily on minimizing dependence on any single LLM. The platform supports multiple models that employees can choose from, including OpenAI and Anthropic.

“AI moves incredibly fast, so we built the system in a way that lets us easily plug in better technologies as they come along,” she says. “The AI layer will keep changing, but the internal data and applications underneath it will remain our core assets, which is why we’ve focused on strengthening the underlying infrastructure. That’s where our DAX philosophy — advancing digital and AI transformation together — comes into play.”

But AiU has done more than speed up AI adoption. It’s also put new life into existing systems. GS Caltex already had an internal enterprise search platform, but over time, its accuracy and usability declined, and usage dropped. AiU stepped in to augment that system with AI. Employees can now search M365 documents, work rules, and HR information in one go, and have the results summarized for them by the AI.

“All we really did was layer AI on top of what we already had to make it a little easier to use,” Lee says. “But in the end, that AI layer ended up reviving a service that was close to being forgotten.”

The growth engines behind the projects

Rolling out and scaling new IT technologies like AI across an entire organization isn’t easy. It’s common to see transformation stall at the slogan stage, held back by resistance to new tools or the simple reality that people are too busy to change how they work.

GS Caltex, however, has avoided treating DX as a one-off initiative. Instead, the company has built three pillars to sustain company-wide change over the long term: culture, performance management, and education.

The first step was to build a bottom-up DX culture. Traditional IT projects often begin with large-scale planning, writing RFPs, and selecting external vendors — a process so long that customer needs frequently change before anything goes live.

GS Caltex chose a different path: a fast-execution model focused on solving customer needs in real time. Even a small app or a single dashboard is recognized as DX, and each attempt is treated as valuable. One example is an app that automatically collects and organizes external news, built by a frontline business team not the IT department.

As these small wins accumulated, a voluntary culture of digital innovation took root. Since the establishment of the DX Center in 2019, GS Caltex has carried out hundreds of projects this way.

Behind this transformation is a high level of organizational acceptance. No matter how well something is built, if colleagues don’t respond favorably, it doesn’t advance. That hasn’t been a problem at GS Caltex, though, largely due to the embedded good risk taking philosophy.

“DX inevitably involves a certain level of risk,” says Lee. “For good risk taking to really work, you need to understand the level of risk and have leaders actively backing it. We have that kind of culture in place.”

After joining GS Caltex, Lee learned a new approach to positive communication. Rather than focusing on fixing problems, the company emphasizes recognizing small achievements, celebrating them together, and then building on that foundation to find areas to improve. “I’ve personally experienced the value of a positive feedback culture,” she says. “A culture that openly recognizes achievements has become a natural driving force encouraging frontline employees to participate in DX.”

This philosophy has been embedded into reward and performance management systems, including a performance innovation committee, which selects outstanding DX projects initiated by business teams and presents awards. And presentations are delivered not by team leaders but by the frontline employees who actually led the work. The monthly selected cases are then published on the company’s internal website, making sure their contributions are visibly acknowledged.

These practices give other employees confidence to do the same, and thus fuels wider voluntary participation. The committee also actively shares failure cases. By openly discussing what was attempted in each project and what could be improved, the company aims to turn failure into an opportunity for learning.

Lee says that GS Caltex only recognizes outcomes that can be proven in financial terms. Common IT metrics such as conversion rates or click-through rates, often used as proxy indicators, aren’t treated as final measures of success. Instead, the company tracks more meaningful indicators such as productivity gains that drive innovation, cost reductions, and improvements in customer satisfaction. These results are all centrally managed through the company-wide performance management system.

But it’s education that the DX Center prioritizes most. Rather than relying on a small group of experts, GS Caltex has chosen a strategy of cultivating hundreds of frontline DX specialists and sees strong results. The more business-side DX experts there are who can use digital tools to directly solve on-site problems, the faster digital adoption spreads. So once technology takes hold in the field, the DX organization provides the necessary development environment and additional support.

This training initiative, called the digital academy, runs as full-day programs ranging from a single day up to three months. It focuses on reskilling and deepening professional expertise to develop DX talent. The curriculum includes low-code developer tracks and in-house DX expert courses, enabling frontline employees to learn technologies themselves and apply them directly to their work. Topics include RPA, Tableau, Python, AI, and data science. Most notably in recent months, every executive has gone through gen AI training themselves, setting the tone from the top and actively championing a culture of continuous learning.

From IT support to proactive DX engine

Two years into her tenure, Lee is now reimagining how DX governance works. Historically, the DX organization operated in reactive mode, fielding requests from business units as they came in. Now, it’s flipping the script. That means taking the lead on company-wide DX priorities, vetting technologies for maturity and feasibility, and consolidating redundant projects.

One clear target is to streamline the system portfolio. Lee also plans to retire underutilized systems and those where operating costs outweigh the value they deliver, cutting waste while boosting efficiency.

At the same time, GS Caltex is leaning into global outsourcing. The company is building a distributed operations model, partnering with offshore teams not just for IT infrastructure, but for internal systems spanning HR, procurement, legal, and beyond. The savings are being funneled back into critical areas, like bolstering disaster recovery capabilities to strengthen business continuity, and reinforcing the DX foundation to deliver more reliable support across the organization.

AI, of course, remains a top priority, and internal demand is surging. “Employees, especially senior leaders, want services that pull together even more data,” Lee says. “Down the road, I’d like AiU to evolve to the point where you can ask what’s been happening with a particular customer lately, and instantly get a unified view of what division A is working on, what division B needs, and live customer inquiries all in one snapshot.”

La campaña de Navidad ha sido el tradicional motor económico para muchos sectores, que veían cómo se concentraba en ese período el grueso de sus ventas. Ahora, la Navidad sigue siendo altamente relevante, pero el período de ventas arranca antes y se ha hecho más complejo. Si hace un par de décadas nadie celebraba en España (y en Europa en general) el Black Friday, ahora es uno de los momentos candentes del año. Es uno de los grandes días de consumo.

Un estudio de Ipsos para Amazon concluye que el 72% de la población española adelantará sus compras navideñas al Black Friday y otro de la OCU que el 78% de la ciudadanía acabará haciéndose con algún producto. El gasto medio oscila, tomando los baremos que dan las diferentes estimaciones, en una horquilla que va de los 201 a los 230 euros. Se comprará mucho y se someterá a los sistemas a mucho estrés, de ahí que el Black Friday no solo importe a los departamentos de marketing y ventas. También lo hace para el CIO.

Las compañías lo tienen cada vez más en cuenta. “La concienciación ha aumentado mucho”, explica Stefan Kühn, especialista en documentación informática de FNT Software. “Cada año vemos en las noticias interrupciones del servicio muy sonadas y las empresas comprenden el daño financiero y reputacional que estos incidentes pueden causar”, suma. Y, “aunque hay margen de mejora”, las empresas se preparan antes y con más conciencia de lo que se les viene encima. “La resiliencia del Black Friday no se construye en noviembre”, advierte Kühn. Lo ven quienes lo observan desde fuera, como las empresas que les dan servicios, pero también los CIO que lo trabajan desde dentro.

Un trabajo de meses

“Cada preparación de Black Friday subes un peldaño”, sintetiza Kiko León Barroso, CIO de IskayPet. “Casi empiezas a prepararlo al día siguiente de haber terminado el año anterior, cuando haces un análisis posmortem con los aprendizajes”, indica. El trabajo supone meses de ajustes, mejoras y refuerzos. En Amazon, revisan y analizan en verano, intensifican pruebas en otoño y se ponen en máximos las semanas previas, “endurecemos la seguridad, congelamos cambios de alto riesgo e implementamos las mejoras que han superado todos los controles”, apunta Merce Mariño, directora de Tecnología de AWS España.

Los sistemas TI deben afrontar una avalancha. “Black Friday es, junto con Prime Day, uno de los mayores picos del año”, confirma Mariño, que habla de “millones de sesiones, realización de pedidos y publicación de ofertas, además de las operaciones de inventario y logística”.  Es, además, el pistoletazo de salida para unas semanas muy intensas. ¿Se puede esperar un momento de descanso tras el viernes de compra? “En la práctica, no. Las campañas se encadenan y la continuidad operativa es permanente: Black Friday, Cyber Monday y la campaña navideña”, explica Sergio Peinado, CIO-director de Transformación Digital y Tecnología en Ontime. “La mayor transformación no ha sido solo tecnológica, también cultural. El ‘pico’ ya no es una excepción, sino parte del modelo”, suma.

“El ‘pico’ ya no es una excepción, sino parte del modelo”, reconoce el CIO de Ontime, Sergio Peinado

Los grandes retos del Black Friday

Todo esto convierte a la campaña de Black Friday en un momento de elevada exigencia, en la que se juegan demasiadas papeletas para afrontar una sobrecarga técnica.

 “Lo que suele fallar primero no es un servidor o una base de datos específicos. Es la falta de visibilidad sobre cómo está todo conectado”, indica Kühn. Ese el talón de Aquiles de las empresas, ya que lleva a que el personal TI tenga “dificultades para comprender de dónde proviene el cuello de botella, cómo dependen los componentes entre sí o qué efectos secundarios podría tener un cambio rápido”. “Según mi experiencia, el verdadero punto débil no es la tecnología en sí, sino la ausencia de una visión clara y unificada de toda la infraestructura”, señala.

Peinado suma que “lo más difícil no es la tecnología, sino la gestión de la incertidumbre”. “Es una combinación de volumen, incertidumbre y criticidad”, explica.

Por tanto, la estrategia más eficiente pasa por prevenir antes que curar, testear mucho y dejar todo bien atado antes de que llegue el momento de enfrentarse al frenesí de compras. Mariño explica que hacen pruebas de carga y simulaciones de estrés con las que analizan el estado de sus rutas críticas. “Si algo no alcanza el objetivo, se refuerza”, indica. “Además, contamos con planes de ‘degradación elegante’: si un componente ‘no esencial’ sufre, la plataforma prioriza la disponibilidad y el checkout para mantener el flujo de compra”, suma.

Al final, la tecnología debe conseguir obrar casi algo digno de magia, que en el momento en el que los sistemas afrontan una avalancha de consumidores todo funcione sin problemas. “Lo más complejo es lograr que, aun con un tráfico muy superior al habitual, la experiencia ‘se sienta normal’”, señala Mariño. “El objetivo es que, aunque por detrás haya diez veces más actividad, el cliente navegue, añada al carrito y pague con la misma fluidez de un día cualquiera”, añade. Ellos usan una “arquitectura elástica y distribuida”. “Aquí entran en juego el CDN para contenido estático y dinámico (Amazon CloudFront), el balanceo inteligente de carga (Elastic Load Balancing) y el autoescalado en compute (Amazon EC2 Auto Scaling, AWS Fargate sobre ECS o EKS para contenedores)”, indica, reforzando también la respuesta de sus bases de datos para que sobrevivan a los picos.

“La tecnología debe conseguir obrar casi algo digno de magia, que en el momento en el que los sistemas afrontan una avalancha de consumidores todo funcione sin problemas”, Merce Mariño, directora de Tecnología de AWS

La presión se nota en el canal online, pero también en las tiendas físicas. Allí el personal de tienda debe gestionar ese aumento de ventas, pero para ello necesitan que la tecnología les dé respuesta. León Barroso explica que, más allá de lo que el cliente directo ve, está todo lo que cubre lo que no se ve, desde el cloud a los sistemas de envíos de mensajes de marketing pasando por la logística que permitirá sincronizar ventas en los diferentes canales y llevar los productos al comprador final. Predecir a qué se va a enfrentar su almacén ayuda a que luego pueda “funcionar con su productividad habitual”.

“Las herramientas clave no son las más sofisticadas, sino las que dan visibilidad, control y capacidad de reacción: observabilidad avanzada, cloud-native y automatización de extremo a extremo”, resume Peinado.

El papel de la IA  

¿Y qué papel ocupa en todo esto la inteligencia artificial? La IA se ha integrado ya en las campañas de marketing y en el análisis de patrones. Así, por ejemplo, desde The Valley recomiendan sacarle provecho como guía que anticipa comportamientos y herramienta que optimiza campañas, escogiendo los mensajes más relevantes y posicionando mejor a la marca.

Sin embargo, sería un error limitar la IA solo a lo que puede hacer a nivel marketing y comunicación. Desde el área de tecnología también se emplea para predecir demanda de producto, identificar potenciales fallos, personalizar experiencias o reforzar la seguridad.  “La IA está muy presente y actúa en varias capas”, confirma Mariño. Aunque, eso sí, Peinado recuerda que “no hay que olvidar que la IA es tan buena como la calidad del dato”. No todos los sectores tienen los datos óptimos para sacarle todo ese buen partido.

“La resiliencia del Black Friday no se construye en noviembre”, advierte Stefan Kühn

Momento candente de amenazas

Sobrevivir a los picos de consumo es fundamental para llegar con bien al final de la campaña de Black Friday, pero ese no es el único punto caliente en una temporada que está repleta de retos. Uno de ellos es la ciberseguridad. “La seguridad no puede ser una cuestión secundaria”, recuerda Kühn, que habla de que “el Black Friday es un objetivo perfecto para los ciberdelincuentes”.

Las estadísticas lo confirman. Según investigaciones de NordVPN, las tiendas falsas crecieron en un 250% en estos días previos a la campaña y el phishing y otras estafas que llegan a los usuarios finales alcanza cifras “sin precedentes”. Al fin y al cabo, esto no es más que una extensión de la tónica del resto del año. Según Signicat, una de cada cinco altas de clientes es fraudulenta, el 59% de las empresas se ha enfrentado a intentos de fraude de identidad exitosos y el 22% de los ingresos anuales se va ya a prevenirlos. La Navidad y el Black Friday son puntos calientes para el fraude en pagos, por algo tan básico como el propio flujo de compras se dispara.

Pero todo esto es algo que tienen muy presente los CIO, que listan las amenazas a las que se enfrentan estos días. “En campañas así aumentan los intentos de ataques tipo DDoS, scraping automatizado, de inyección y fraude online”, apunta Mariño. Amazon refuerza su estructura con una larga serie de soluciones propias. “Todo esto se acompaña de simulacros operativos y runbooks claros, con equipos de respuesta 24/7, de modo que cualquier incidente se detecte y mitigue en minutos”, indica la experta.

Aun así, y por mucho que este sea un momento caliente, no es único. “Realmente puede pasar en cualquier momento”, recuerda León Barroso, “y tienes que estar siempre prevenido y preparado. Es la prioridad cero”.

“Esos días buscas tener planes B, C y D para casi todo”, reconoce Kiko León Barroso, CIO de IskayPet

Sobrevivir al Black Friday pasa por el día después

Si durante el resto del año los consumidores no suelen tomarse muy bien que sus compras lleguen tarde o que se extravíen, las cosas se vuelven todavía más complejas durante el Black Friday y la campaña de Navidad. Lo que se compra es, por así decirlo, más sensible, ya que se aprovecha para hacerse con regalos o productos necesarios para las fiestas, y la tolerancia a los errores se desploma. Al tiempo, la cantidad de paquetes y gestiones que deben asumir las empresas se dispara. Que todo fluya es fundamental y, ahí también, los CIO tienen un papel crucial.

Se necesita afinar muy bien para que todo funcione. León Barroso señala que hay que adelantarse a los cuellos de botella, para que no se acaben pasando al transportista. Postergarlos al día después aplazaría el problema, que seguiría estando ahí. Se necesita ser capaz de flexibilizar, de buscar soluciones. “Esos días buscas tener planes B, C y D para casi todo”, apunta. Lo que para los compradores resulta simple tiene, en realidad, mucha infraestructura detrás. “Queremos que lo que se ve parezca sencillo, aunque por detrás haya una operación tecnológica y de datos a gran escala”, apunta Mariño. “Todo está orquestado por software y datos: desde que el usuario hace clic y hasta que recibe el paquete”, explica. “La idea es que la tienda no se caiga, las ofertas sean claras y la logística responda a tiempo”, resume.

La prueba de estrés de estas fechas es también un aviso a navegantes. “El día después es un indicador estratégico del nivel de madurez digital”, señala Peinado, uno que cuenta en qué nivel está la transformación digital de la compañía. Si las cosas fallan, está avisando de que se necesita todavía hacer ajustes y mejoras. “El Black Friday no es la excepción, es un recordatorio. Nuestro sistema debe soportar cualquier escenario, no uno puntual. El objetivo real es operar con elasticidad y resiliencia continua los 365 días”, advierte.

世界を見渡せば、UberやLyftといったライドシェアサービスが既存の交通システムを破壊的に革新してきましたが、日本市場においては事情が異なります。「白タク行為」を原則禁止とする厳しい法的規制が存在するためです。この独自の環境下で、日本のタクシー産業は既存のタクシー事業者とIT企業が手を組むという、世界でも稀な「協調型」の進化を遂げてきました。

現在、日本のタクシーアプリ市場は単なる配車ツールの枠を超え、深刻化する人口減少やドライバー不足、そして急増するインバウンド需要という社会課題を解決するための巨大な「モビリティ・プラットフォーム」へと変貌しようとしています。本稿では、規制と技術が交錯する日本のタクシーアプリ市場の特異な構造、主要プレイヤーによる激しいシェア争い、そして自動運転やMaaS（Mobility as a Service）を見据えた未来図について、詳細に解説します。

独自の進化を遂げた市場構造と規制の壁

日本のタクシーアプリ市場を理解する上で避けて通れないのが、日本特有の法規制と市場の成り立ちです。海外で普及しているライドシェアは、一般ドライバーが自家用車を用いて有償で顧客を運ぶモデルですが、日本では道路運送法により、こうした行為は「白タク」として原則禁止されています。旅客運送を行うには、厳格な安全管理と運行管理が求められる「緑ナンバー」の取得が必須であり、これが長らく海外勢の参入障壁となると同時に、既存のタクシー業界を守る防波堤となってきました。

公正取引委員会が2025年に公表した調査においても、「ライドヘイリングアプリ」の定義について、デジタル技術を用いて乗客と供給側をマッチングするサービスとしつつも、その供給主体はあくまで既存の法的枠組みの中にあるタクシー事業者や、特定の条件下で認められた自家用有償旅客運送車両であることが再確認されています。この法的背景があるため、日本のタクシーアプリは、既存産業を破壊するのではなく、既存のタクシー会社の業務効率を最大化させる「DX（デジタルトランスフォーメーション）パートナー」として発展してきました。

アプリの導入は、タクシー事業者にとって革命的な変化をもたらしました。従来の無線配車や流し営業に頼っていたビジネスモデルに対し、アプリは「最も近くにいる車両」をアルゴリズムで瞬時に割り出すことで、空車時間を劇的に削減します。また、事前に目的地や運賃が確定するシステムは、日本語が不慣れな外国人観光客や、地理に不案内なドライバー双方のストレスを解消し、新たな顧客層の開拓に寄与しています。特に地方の中小タクシー会社にとって、自社単独では不可能な高度な配車システムやキャッシュレス決済基盤を、プラットフォームに参加することで安価に利用できる点は、経営存続に関わる大きなメリットとなっています。

しかし、この強固な規制の壁も、社会的な要請によって徐々に、かつ確実に変化の兆しを見せています。過疎地や観光地における深刻な移動手段の不足、「交通空白地帯」の拡大を受け、政府は「自家用車活用事業」や「自家用有償旅客運送」の適用範囲拡大に向けた議論を加速させています。都市部では既存のタクシー産業を核としつつ、地方部では自治体やNPOが主体となってアプリを活用した自家用車による運送サービスを展開するなど、地域特性に合わせた多層的なモビリティ・エコシステムの構築が模索されているのが現状です。

GO・S.RIDE・Uber・DiDi――四つ巴の競争と差別化戦略

現在、日本のタクシーアプリ市場は、圧倒的なシェアを持つトッププレイヤーと、独自の強みを持つチャレンジャーたちが入り乱れる群雄割拠の様相を呈しています。その中心に君臨するのが「GO」です。GOは、旧JapanTaxiとDeNA系の「MOV」という二大勢力が統合して誕生したアプリであり、その規模は他を圧倒しています。47都道府県のうち45都道府県をカバーし、アプリ利用時間ベースでは主要5アプリの合計の約8割を占めるというデータからも、もはや「社会インフラ」としての地位を確立していると言っても過言ではありません。

GOの強みは、日本交通をはじめとする大手タクシー会社との強固な提携網による「車両数の多さ」だけではありません。AIを活用した高度な需要予測システムにより、ドライバーに対して「これから需要が発生する場所」をナビゲートする機能や、雨天時や混雑時に追加料金を払うことで優先的に配車を受ける機能など、需給バランスを動的に調整する仕組みを実装しています。これにより、ユーザーには「捕まりやすさ」を、ドライバーには「実車率の向上」を提供し、双方の利便性を高めることで市場のスタンダードとなりました。

一方で、首都圏などで独自の存在感を放つのが「S.RIDE」です。ソニーグループのAI技術と、都内の有力タクシー会社5社（グリーンキャブ、国際自動車、寿交通、大和自動車交通、チェッカーキャブ）がタッグを組んだこのサービスは、「Simple」「Smart」「Speedy」をコンセプトに掲げています。アプリを起動してワンスワイプするだけで配車が完了するという極めてシンプルなUIは、一分一秒を争うビジネスパーソンから絶大な支持を得ています。東京の街中を走るタクシーの多くがS.RIDE加盟社であるため、都心部における配車スピードには定評があり、大都市特化型の戦略でGOに対抗しています。

海外からの黒船として注目された「Uber Taxi」と「DiDi」も、日本市場に合わせたローカライズ戦略で地位を築いています。Uberは、世界中で展開するライドシェアのブランド力を武器にしつつも、日本ではあくまでタクシー会社との提携モデルに徹しています。特筆すべきは、そのグローバルな知名度を活かしたインバウンド戦略と、地方自治体との連携です。長野県白馬村や京都府京丹後市など、海外観光客が多く訪れる地域や交通課題を抱える自治体と提携し、外国人観光客が普段使い慣れているアプリでそのまま日本のタクシーを呼べる利便性を提供しています。

ソフトバンクグループと中国DiDiの合弁である「DiDi Mobility Japan」は、大阪などの関西圏や福岡などの大都市を中心にシェアを広げてきました。中国市場で培った強力なAI技術によるダイナミック・プライシング（変動運賃制）の試験運用や、PayPayなどの決済プラットフォームとの連携による大規模なポイント還元キャンペーンなど、ユーザーの実利に直結する施策を得意としています。

このように、ユーザーから見た競争軸は、かつての「単に呼べるかどうか」という段階から、配車スピード、決済の簡便さ、ポイント還元、そしてインバウンド対応といった「体験の質」へとシフトしており、地域や用途によって複数のアプリを使い分けるユーザーも増えています。

自動運転とMaaSが拓く、次世代の移動社会

日本のタクシーアプリ市場は今、既存のタクシー配車の効率化というフェーズを終え、自動運転技術やMaaSとの融合による次世代の移動社会構築という新たなフェーズへと突入しています。その最前線にあるのが、自動運転技術との連携です。2024年末、Googleの親会社Alphabet傘下のWaymoが東京での国際テストを開始するというニュースは、業界に衝撃を与えました。GOや日本交通と連携し、まずは有人運転によるマッピングデータの収集から開始するこのプロジェクトは、将来的な「ロボタクシー」の実用化を見据えた重要な一歩です。

自動運転技術の導入は、日本のタクシー業界が抱える最大の課題である「ドライバー不足」に対する究極の解となり得ます。しかし、複雑な日本の道路事情において完全無人化を即座に実現することは困難です。そのため、当面はアプリをハブとして、有人タクシーと自動運転車を適材適所で配車するハイブリッドな運用が模索されることになります。アプリ事業者は、配車プラットフォームとしての枠組みを超え、自動運転車の運行管理や旅客対応を担う「オペレーション・プラットフォーム」としての機能も求められるようになるでしょう。

また、タクシーアプリはMaaS（Mobility as a Service）の中核としての役割も期待されています。鉄道、バス、シェアサイクル、そしてタクシーといった異なる交通モードをシームレスに繋ぎ、ルート検索から予約、決済までを一元化する「移動のスーパーアプリ」化への動きです。特に、地方部における公共交通の維持が困難になる中で、タクシーアプリは「ラストワンマイル」を埋める重要なインフラとなります。免許を返納した高齢者の通院や買い物を支援するためのデマンド型交通として、あるいは観光客が駅から目的地までスムーズに移動するための手段として、アプリの役割は公共性を帯びてきています。

もちろん、課題も山積しています。プラットフォームの影響力が強まるにつれ、手数料設定の透明性やデータの取り扱いに関する公正性が問われるようになり、規制当局による監視の目も厳しくなっています。また、テクノロジーがいかに進化しても、最終的にサービスを提供するドライバーの労働環境改善や処遇向上なくしては、業界の持続的な発展は望めません。

総じて、日本のタクシーアプリ市場は、厳しい規制環境を逆手に取り、既存事業者とテック企業が高度に融合することで独自の進化を遂げた稀有な事例です。GOを筆頭とする国内プレイヤーが強固な基盤を築き、そこに海外の巨人や最先端技術が組み合わさることで、日本の移動体験は進化を続けています。その変貌は、日本の社会課題解決の試金石として、今後も世界から注目され続けることでしょう。

Este 25 de noviembre, Madrid volvió a acoger una nueva gala de entrega (la cuarta) de los prestigiosos premios CIO 100 en España, que organiza la cabecera editorial CIO para distinguir la labor de los líderes de TI, de seguridad de la información (CISO) y de empresas y organismos públicos en materia de modernización tecnológica y la innovación.

El encuentro, patrocinado por Cognizant, Dell Technologies, NetApp, HP y Kyocera, aglutinó a unas 120 personas de los que más de 100 eran líderes de TI de las principales empresas y organismos públicos del país. La gala se celebró coincidiendo con el 20 aniversario del nacimiento de la cabecera CIO en España en el Hotel Mandarin Oriental Ritz y fue presentada por Fernando Muñoz, director del CIO Executive de Foundry en el país, y Esther Macías, directora editorial del grupo que también edita en España la decana COMPUTERWORLD.

“Hoy nos reunimos para celebrar la innovación, el talento y, sobre todo, el liderazgo que está transformando el mundo tecnológico en nuestro país. Y lo hacemos en un momento muy especial para CIO ESPAÑA, la publicación de referencia del grupo Foundry de y para los líderes de TI empresarial, porque esta acaba de cumplir 20 años de vida”, señaló Esther Macías, recordando los orígenes de esta cabecera nacida en Estados Unidos en 1987. “Estos han sido unos años apasionantes en los que nunca hemos perdido de vista nuestro propósito: proporcionar a nuestra audiencia y grandes protagonistas, los CIO, información contrastada y trabajada sobre los grandes proyectos y estrategias de TI del ámbito empresarial y del sector público”, añadió, afirmando que las ediciones de CIO que tiene el grupo Foundry en el mundo suman unos dos millones de visitas mensuales.

“La tecnología no avanza sola: la impulsan las personas. Personas como vosotros, que hacéis posible el cambio en vuestras organizaciones y en la sociedad”, indicó por su parte Fernando Muñoz, quien no dudó en afirmar que, entre tantas transformaciones, hay algo que no cambia: “El nivel excepcional de los líderes que hoy reconocemos. Vuestras historias reflejan no solo excelencia técnica, sino también visión, resiliencia y humanidad. Hablan de equipos guiados con propósito, de decisiones valientes, y de la capacidad de convertir la incertidumbre en oportunidad. En un mundo cada vez más digital, el liderazgo auténtico, el que inspira, escucha y construye, es más importante que nunca. Y eso es lo que hoy celebramos aquí”.

Estos son los premiados y finalistas de la edición 2025 en España de los CIO 100 Awards.

CIO del año: Dimitris Bountolos (Ferrovial)

Dimitris Bountolos, CIO de Ferrovial, fue el ganador de la categoría estrella de los premios, la distinción al mejor líder de TI del año, un reconocimiento que pone en valor la excelencia, la visión y el liderazgo de aquellos directivos que marcan el rumbo de la transformación tecnológica en nuestro país.

Con el Plan de Transformación Digital Horizon, Bountolos ha demostrado que la tecnología solo genera valor cuando está plenamente integrada en la estrategia y la operación del negocio. El jurado ha valorado especialmente su modelo de colaboración top-down y bottom-up que une al comité de dirección y toda la organización, lo que garantiza una adopción real de las iniciativas y sitúa la tecnología en el centro de la estrategia corporativa.

El galardón lo entregó Manuel Ávalos, director general de Cognizant para España, Portugal e Italia.

Los finalistas de esta categoría fueron:

• Jordi Roda, CIO de Decathlon, quien con su visión de la tecnología como game changer, ha impulsado la evolución de una cadena de retail a una plataforma integral de servicios deportivos.
• Ángel Blanco, CIO de Quirónsalud, quien ha logrado liderar en el grupo sanitario una transformación sin precedentes del modelo asistencial, incorporando la inteligencia artificial como motor del cambio.
• Miguel Ángel Iglesias, CIO de Seat & Cupra, que, en un contexto desafiante para el sector de la automoción, marcado por la electrificación, la transformación de las fábricas y la irrupción de la inteligencia artificial, ha impulsado una iniciativa de TI que ha redefinido el modelo de relación entre IT y negocio en el grupo, acelerando la transformación global de la compañía.

CISO del año: Izaskun Onandia (ITP Aero)

La directora asociada de seguridad y cumplimiento de información de ITP Aero, Izaskun Onandia, fue la ejecutiva elegida como CISO del año en los CIO 100 Awards por su capacidad de integrar la seguridad física, la ciberseguridad, la seguridad de la información y la gobernanza del dato, aportando una visión transversal y adaptada a cada entorno.

La directiva no sólo ha logrado incorporar la gobernanza de la inteligencia artificial en la compañía, promoviendo un uso seguro, ético y responsable de estas tecnologías, sino que su visión innovadora y su capacidad para anticiparse a los retos de la era digital la posicionan como una referente en la gestión integral de la seguridad y la información.

José Luis Basabe Echezarraga, director asociado de Infraestructura de ITP Aero, fue quien recogió el premio de Izaskun Onandia ante la imposibilidad de la directiva de acudir a la gala.

En la categoría de CISO del año, una función directiva clave en un momento en el que la ciberseguridad se ha convertido en un eje esencial para la confianza, la continuidad y la innovación de las organizaciones, y que reconoce a quienes lideran con visión estratégica, impulsando la resiliencia y el crecimiento desde la protección digital, destacaron como finalistas:

• Javier Torres Alonso, CISO de Allfunds, por la profunda transformación de la arquitectura de seguridad de la entidad, logrando modernizar sus capacidades de monitorización y respuesta ante incidentes.
• Juan Manuel Bahamonde, CISO de Metro Food Sourcing, por haber liderado con éxito la implantación del framework de seguridad en las empresas de las oficinas de comercio internacional del grupo METRO/MAKRO con una filosofía de gestión apoyada en la colaboración y el pragmatismo.

Empresa del año: Coca-Cola Europacific Partners

La historia de transformación tecnológica y organizativa presentada por Coca-Cola Europacific Partners ha hecho merecedora a esta compañía del premio a Empresa del año en los CIO 100 Awards. En apenas una década, ha pasado de ser una compañía local en España a operar en 31 países de Europa y Asia-Pacífico. Desde la integración de las embotelladoras españolas en 2013, pasando por su expansión europea en 2016, hasta su llegada a Asia y Oceanía en 2021 y Filipinas en 2024, la empresa ha afrontado enormes retos tecnológicos, de integración y de adopción.

Actualmente, lidera una migración estratégica a la nube, incluyendo CRM, productividad, fuerza comercial y próximamente su ERP SAP, con una visión a siete años. Una plataforma global, una respuesta local y un enfoque en las personas —con iniciativas como la IA Academy y la AI Incubator— definen su apuesta por una tecnología al servicio de la cultura. Una transformación constante que une innovación, escalabilidad y cercanía en todos sus mercados.

David Marimón, vicepresidente y CIO para Iberia & IT Head Customer Service & Supply Chain de Coca-Cola Europacific Partners, recogió el galardón de manos de Fernando Muñoz, director del CIO Executive de Foundry España.

La de Empresa del año es una categoría que reconoce a aquellas organizaciones que han hecho de la tecnología y la innovación el motor de su transformación, empresas que evolucionan, inspiran y marcan el camino hacia el futuro. Los finalistas de esta categoría son:

• Air Europa, por haber puesto en marcha un ambicioso Programa de Estrategia de Datos 2023–2027, con el objetivo de convertir el dato en un auténtico activo estratégico, lo que está transformando su cultura y su manera de trabajar, además de aportar mayor valor para el negocio y los clientes.
• CEPSA GLP-GASIB, por haber llevado a cabo en los últimos años una profunda transformación tecnológica y cultural con el propósito de innovar con impacto, lo que le ha hecho ser una empresa 100% cloud, con un puesto de trabajo digital y con proyectos que aceleran la transformación digital y contribuyen a la descarbonización.
• Zurich Insurance, que con una filosofía ‘business-led, tech-powered’, ha transformado su modelo en torno a dos ejes: la One Customer House, centrada en la relación con el cliente, y el Intermediaries Service Center, que optimiza el soporte a la mediación, además de haber desarrollado una fábrica digital, basada en tecnología de nube nativa e IA generativa, permite ajustar precios en minutos, automatizar siniestros o asistir a agentes y clientes con lenguaje natural, entre otros grandes proyectos.

Organismo público del año: Lantik SAMP-Diputación Foral de Bizkaia

La categoría Organismo Público del Año reconoce a aquellas instituciones que han demostrado una visión ejemplar en la modernización del sector público, impulsando proyectos transformadores con impacto real en la ciudadanía. En este caso, la Diputación Foral de Bizkaia, a través de Lantik, ha resultado ganadora por haber liderado una apuesta pionera por las tecnologías cuánticas, basada en el liderazgo público, la colaboración y el impacto social.

La propuesta integra laboratorios, programas de adopción temprana y un ecosistema donde confluyen Administración, industria, centros tecnológicos y universidades. Bizkaia ha logrado impulsar un nuevo sector estratégico y su propuesta BIQAIN sitúa la innovación, la sostenibilidad y la competitividad en el centro de un modelo referente en España y Europa.

Valentín García, director de Innovación de Lantik, recogió el premio, que entregó Esther Macías, directora editorial de CIO y COMPUTERWORLD en España.

En esta categoría quedaron finalistas:

• La Generalitat de Catalunya, por haber impulsado una transformación integral hacia una Administración plenamente omnicanal, situando a la ciudadanía en el centro, con una estrategia que avanza hacia servicios más proactivos, accesibles y personalizados y un modelo basado en datos, colaboración y una clara visión de futuro.
• El Hospital Universitario 12 de Octubre, por haber llevado a cabo la mayor transformación digital hospitalaria en España, coincidiendo con la puesta en marcha de uno de los mayores proyectos de obra civil de Europa, una iniciativa con impacto directo en la mejora de la atención a los pacientes y que reconoce el compromiso ejemplar de los equipos clínicos y tecnológicos de la Comunidad de Madrid.
• La Agencia Madrid Digital, por haber liderado la modernización de la Administración de la Comunidad de Madrid mediante soluciones tecnológicas que simplifican y agilizan la relación con ciudadanos, empresas y empleados públicos, siendo un referente en servicios públicos digitales seguros, accesibles y orientados al futuro.

Premio al mejor proyecto de IA y automatización inteligente: Cruz Roja Española

En la categoría que distingue a las organizaciones que han sabido integrar la inteligencia artificial y la automatización avanzada para transformar procesos, ampliar capacidades y generar un impacto real, seguro y responsable en sus operaciones y en la sociedad, resultó ganadora Cruz Roja Española, por la solución Simula tu entrevista de trabajo, que utiliza inteligencia artificial generativa para mejorar la empleabilidad de personas con mayores dificultades de acceso al mercado laboral. Se trata de un simulador de entrevistas realista, accesible y personalizado que ofrece retroalimentación inmediata y contribuye a una inclusión sociolaboral efectiva.

Javier Cressi, responsable de los Servicios multicanal de orientación para el empleo de Cruz Roja Española, recogió el premio de manos de Isabel Reis, directora general de Dell Technologies para España y Portugal.

Los finalistas de esta categoría fueron:

• Ferrovial, por AlexandrIA, un programa innovador que preserva y activa el conocimiento crítico de la organización mediante agentes de IA especializados.
• Renfe, por su Laboratorio de IA, un modelo de innovación abierta, gobernanza ética y tecnología avanzada para convertir ideas en soluciones reales, que sitúa al operador como un referente en el uso responsable y estratégico de la inteligencia artificial.
• World2Meet, por su proyecto IPA, que impulsa un ecosistema integral de automatización inteligente que combina datos, IA generativa y soluciones avanzadas para transformar procesos en toda la organización, consolidando la empresa como un actor ágil e innovador en el sector turístico.

Premio al mejor proyecto de seguridad y resiliencia: Renfe

En la categoría de los CIO 100 que reconoce a aquellas organizaciones que, con visión y determinación, han logrado proteger sus entornos, garantizar la continuidad de sus operaciones y fortalecer la confianza de sus usuarios frente a los desafíos de la era digital, ha resultado ganadora Renfe. El jurado ha seleccionado esta candidatura por el ambicioso plan de modernización de estaciones y trenes de Cercanías emprendido por el operador ferroviario, que integra conectividad, ciberseguridad y digitalización. Con inversiones históricas y un despliegue tecnológico sin precedentes, Renfe ha logrado fortalecer la seguridad de sus infraestructuras, mejorar la experiencia de millones de pasajeros y consolidar la resiliencia operativa de su red ferroviaria, estableciendo un referente de innovación y servicio público.

Sonia Segade, CIO de Renfe, y Carmelo Plana, responsable de Administración y Operación de Comunicaciones en la organización, recogieron el premio, que entregó Fernando Muñoz, del CIO Executive.

Quedaron finalistas en esta categoría:

• El Banco Santander, por haber desarrollado un enfoque integral para proteger la experiencia de sus clientes frente al fraude digital y haber creado un ecosistema capaz de anticipar ataques sofisticados, empoderar a los usuarios y fortalecer la resiliencia interna.
• Nationale-Nederlanden, por un proyecto (IT Assurance) con el que ha transformado la manera en que se construye la seguridad y la calidad en el desarrollo de sistemas, basado en automatización, observabilidad y colaboración.

Premio al mejor proyecto de infraestructura digital y cloud: Merlin Properties

Merlin Properties ha sido la compañía ganadora en esta categoría, que distingue a aquellas organizaciones que han llevado la tecnología más allá de la infraestructura tradicional, transformando sus sistemas en plataformas ágiles, seguras y escalables y a quienes, con visión estratégica y excelencia técnica, han convertido la nube y la infraestructura digital en palancas de innovación, eficiencia y resiliencia, sirviendo de base para el futuro de sus negocios y de la sociedad.

En concreto, el jurado ha valorado positivamente cómo la empresa ha logrado desplegar en España una red de data centers sostenibles, hiperconectados y preparados para la inteligencia artificial, fortaleciendo la infraestructura digital del país. Un proyecto que combina máxima disponibilidad, neutralidad climática y eficiencia energética, ofreciendo entornos escalables y seguros para clientes y empresas y que se consolida como un modelo de innovación tecnológica alineado con los desafíos de la economía digital y la sostenibilidad.

Álvaro Ontañón, CIO de Merlin Properties, recogió el premio, entregado por Maite Ramos, directora general de NetApp en España y Portugal.

Los finalistas de esta categoría fueron:

• GALP, por haber recorrido un viaje ejemplar hacia la adopción de la nube, afrontando retos complejos en eficiencia financiera, control de costes y creación de valor.
• MAPFRE, por su Plataforma de Ingeniería Zeus, con la que ha logrado unificar y modernizar su ecosistema tecnológico global, acelerando la entrega de valor al negocio y mejorando de manera significativa la experiencia de sus desarrolladores.
• Nationale-Nederlanden, por haber migrado completamente su infraestructura a un entorno 100% cloud, adoptando un enfoque ‘Cloud First’ que acelera la innovación y mejora la resiliencia.

Premio al mejor proyecto de innovación y excelencia digital: Astrazeneca España

Aquellas organizaciones que han elevado la transformación digital a un nuevo nivel, impulsando la evolución de sus negocios y de toda la sociedad mediante soluciones visionarias, modelos avanzados de gestión del dato y experiencias digitales que marcan un antes y un después son candidatas a ser premiadas en esta categoría. En este caso, la ganadora fue Astrazeneca, por su programa OneSource, que impulsa una profunda transformación empresarial y sitúa a la organización en el camino hacia convertirse en una compañía plenamente orientada al dato.

Con un catálogo de negocio unificado, una arquitectura avanzada, paneles intuitivos y soluciones basadas en inteligencia artificial, AstraZeneca impulsa un nuevo modelo de decisión, conocimiento y capacidades digitales a escala corporativa.

Antonio Velasco, CIO de AstraZeneca España, recogió el galardón de manos de José María Tavera, miembro del jurado de los CIO 100 Awards España 2025.

Los finalistas de esta categoría fueron:

• La Agencia Madrid Digital, por Cuenta Digital, un proyecto de referencia nacional que redefine la relación entre la ciudadanía y la administración pública, una plataforma 100% cloud, basada en datos e inteligencia artificial, que centraliza servicios, simplifica trámites y ofrece una experiencia moderna, accesible y segura.
• Decathlon España, por la Transformación digital de la experiencia deportiva omnicanal, un proyecto que aspira a diluir por completo las fronteras entre el mundo físico y el digital para crear una experiencia deportiva sin interrupciones.
• Ferrovial, por RIaaS (Research Intelligence as a Service), una plataforma avanzada de inteligencia y gestión del conocimiento que integra agentes inteligentes, modelos de lenguaje, datos externos y automatización avanzada.

Premio al mejor proyecto de talento digital y puesto de trabajo: Iberdrola

Iberdrola resultó ganadora de esta categoría que reconoce a las organizaciones que han impulsado nuevas formas de trabajar, potenciando las capacidades digitales de sus equipos y construyendo entornos laborales más inteligentes, colaborativos y orientados al futuro.

En concreto, el jurado valoró cómo la energética ha logrado reinventar el puesto de trabajo a escala global, empoderando a sus equipos con soluciones digitales de nueva generación y consolidando un modelo centrado en las personas y la productividad.

Recogió el premio Ana Arjonilla, responsable global de Gestión del Cambio Digital en Iberdrola. Se lo entregó Mar Hurtado de Mendoza, miembro de jurado de los CIO 100 Awards y vicepresidenta global de Reclutamiento y de IE University.

Los finalistas fueron:

• CEPSA GLP-GASIB, por el proyecto ‘Workplace 360: Personas en el Centro de la Transformación’, todo un reto por transformar la experiencia digital del empleado mediante un puesto de trabajo unificado, automatizado y accesible, que impulsa la colaboración y la eficiencia en toda la organización.
• Dräger Hispania por el proyecto Beloira, que implica la creación de un ecosistema de trabajo sostenible e innovador que integra espacios versátiles, tecnología avanzada y bienestar, redefiniendo la cultura colaborativa de la compañía.
• La Organización Mundial del Turismo, por democratizar la formación en turismo a través de Tourism Online Academy, una plataforma global de la academia ONU Turismo, multilingüe y accesible, que impulsa el talento digital en todo el mundo.

Premio al mejor proyecto de sostenibilidad e inclusión: Zurich

La categoría rinde homenaje a una iniciativa que, por su visión, impacto y compromiso, se alza por sí misma como referente indiscutible en sostenibilidad e inclusión. Se trata del proyecto presentado por Zurich, llamado Productos IT para la Resiliencia ante Eventos Meteorológicos, una propuesta que combina innovación tecnológica y propósito social para fortalecer la protección frente a los riesgos climáticos y apoyar a empresas y comunidades vulnerables.

Con una aproximación ‘Tech-powered, business-led’, Zurich ha desplegado soluciones avanzadas que permiten anticipar, evaluar y mitigar el impacto de los fenómenos extremos, democratizando el acceso a la información, elevando la capacidad de respuesta y situando la inclusión en el centro de su estrategia. Una iniciativa que encarna, con claridad y excelencia, el espíritu de esta categoría.

Recogió el premio Ricard Guash, CIO de Zurich Insurance, que le fue entregado por Juan Antonio Relaño, miembro de jurado de los CIO100 Awards y CIO de Bosch España.

Cuatro ediciones en España de los ‘Óscar de la industria tecnológica’

Los premios CIO 100, conocidos como los ‘Óscar de la industria tecnológica’, son un prestigioso reconocimiento a la excelencia en TI empresarial. La tradición de distinguir a CIO visionarios comenzó en Estados Unidos hace más de tres décadas y ahora se celebra anualmente en los principales mercados tecnológicos, incluidos España, el Reino Unido, Singapur, Australia, Alemania, Corea del Sur e India.

En esta ocasión, el jurado de los premios ha estado integrado por Fernando Muñoz, director del CIO Executive de Foundry España; Esther Macías, directora editorial de Foundry España (grupo editor de CIO y COMPUTERWORLD); Romy Tuin, directora global de Contenidos para Eventos y jefa de Operaciones de Eventos en España y Reino Unido en Foundry; el histórico CIO, ya retirado, José María Tavera, que lideró la estrategia de TI de gigantes como Telefónica o Acciona; Juan Antonio Relaño, CIO de Bosch y ganador de la categoría CIO del año de la edición 2024 de los CIO 100 Awards Spain; la exCIO Kelly Olsen; Madhu Bhabuta, CIO fraccional en Freeman Clarke, el equipo más grande y experimentado de líderes de TI en el Reino Unido; y Mar Hurtado de Mendoza, vicepresidenta global de reclutamiento en IE University y profesora adjunta de esta escuela de negocio.

Con 2026 a la vuelta de la esquina, es tiempo de que las compañías preparen los desafíos del nuevo año. Uno de los principales será el cambio de año en los sistemas de facturación: la implementación del sistema de verificación antifraude Verifactu. No es extraño ver menciones a este cambio como el paso a la facturación electrónica. Pero llamarlo así puede llevar a confusión, porque se trata de dos elementos distintos, regidos por normativas distintas.

Lucía Pérez y Meritxell Yus, abogadas especializadas en Fiscalidad Indirecta de Cuatrecasas, son tajantes. “No debe confundirse la obligación de facturación electrónica B2B con la normativa sobre sistemas informáticos de facturación” —el conocido como VeriFactu— “que establece la obligación del cumplimiento de los requisitos de integridad, conservación, accesibilidad, legibilidad, trazabilidad e inalterabilidad de los registros de los sistemas informáticos de facturación. Ambos proyectos se encuentran regulados en normativa diferentes con un distinto objetivo”. Si bien convivirán y se complementarán, buscan distintas finalidades, explican. “La facturación electrónica regula el formato y la transmisión estructurada de las facturas entre empresarios y profesionales y su finalidad principal es reducir la morosidad comercial, mientras que la normativa de sistemas de facturación se centra en la calidad, seguridad y fiabilidad de los datos generados por los programas de facturación, con la finalidad de reducir el fraude”.

El paso a Verifactu

La primera en entrar en vigor es la Ley 11/2021, de 9 de julio, de medidas de prevención y lucha contra el fraude fiscal, conocida popularmente como Ley Antifraude. Esta regulación transpone una directiva europea que quiere limitar las prácticas de evasión fiscal. Aunque el texto habla de concentrar los esfuerzos en las grandes fortunas, sus implicaciones afectan a todo tipo de profesionales, de grandes empresas a personas que cotizan como pequeñas autónomas. La normativa incorpora distintas medidas para lograr el cumplimiento de los requerimientos fiscales, entre las que se plantea un modelo basado en el control de los softwares de contabilidad y gestión, que quedan obligados a ajustarse a determinados requisitos.

Al calor de esta normativa se ha desarrollado el reglamento Verifactu, que establece los requisitos que deben contemplar los programas de facturación. Entre otros, se contempla que, al expedir una factura, se genere o guarde una copia o se mande un resumen, directamente, a la Agencia Tributaria. Además, se deberá incluir un QR en la factura para poder verificarla con la administración. Negro sobre blanco, el cambio en la regulación implica que ya no se podrá enviar las facturas en un PDF ni hacer los libros de contabilidad sobre un Excel, sino que habrá que emplear un sistema de facturación homologado. Para controlar que el modelo sobre el que se haga cumple los necesarios requisitos se ha desarrollado el sistema del mismo nombre, que se puede incorporar a softwares de facturación ya existentes y que está también integrado en la aplicación informática gratuita desarrollada por la AEAT.

La nueva normativa, dice Estefanía Gambin, “implica un cambio técnico relevante para los equipos de TI”

Es decir: si bien este nuevo modelo va hacia la facturación electrónica, no se refiere a esta normativa, sino a la integración de un sistema antifraude. “Verifactu no es factura electrónica, y este matiz es importante”, desarrolla Álvaro Villa, director general de Alegra España. “Es un sistema antifraude que obliga a que el software de facturación cumpla criterios muy estrictos de integridad, trazabilidad e inalterabilidad del dato”, resume. A nivel de TI tiene un impacto significativo: “No cambia la interfaz, pero sí cambia la arquitectura del sistema”, explica. Coincide Estefanía Gambin Altare, country success manager en Pleo. “Esta normativa implica un cambio técnico relevante para los equipos de TI”, defiende. “Los sistemas deben ser capaces de trabajar con formatos estructurados, integrarse con los sistemas contables y asegurar la transmisión segura y puntual de los datos a Hacienda”. Gambin lo define como “un reto técnico”, que “con el apoyo adecuado no tiene por qué ser una carga”.

Entre los trabajos en TI a los que obligará el reglamento Verifactu, continúa Villa, está la auditoría y adaptación de ERP, CRM, TPV y desarrollos propios a los nuevos requisitos, pero también la integración de hash encadenado y QR y el compromiso de trazabilidad completa y exportación en el formato exigido por la AEAT. Habrá que realizar integraciones vía API y definir si la empresa operará en modo VeriFactu o no VeriFactu; y asegurar operaciones sin interrupciones, “porque cualquier incidencia ahora tiene también un impacto en cumplimiento”. Lejos de tratarse de una acción puntual, este cambio requerirá de un seguimiento continuo, explica. “Lo más práctico es tratar Verifactu como un programa permanente de cumplimiento digital, no como un proyecto puntual. Esto implica gobernanza de datos, revisiones periódicas y formación continua para que Finanzas, TI y asesores estén alineados”. Gambin lo afronta de forma similar. “No es un cambio puntual que se resuelve una vez y listo. Es un proceso vivo que requerirá actualizaciones continuas y capacidad de adaptación”.

“Lo más práctico es tratar Verifactu como un programa permanente de cumplimiento digital, no como un proyecto puntual”, señala Álvaro Villa

En cuanto a su impacto en la relación entre los departamentos de TI y finanzas, la directiva de Pleo avanza que el equipo financiero se verá relegado de tareas repetitivas para enfocarse en aportar valor real al negocio. Villa lo desarrolla. “VeriFactu nace como una iniciativa fiscal, pero se implementa a través de tecnología. Eso obliga a TI y Finanzas a trabajar de forma más coordinada que nunca”. Entre otros, augura un impulso a las decisiones compartidas sobre software, integraciones y políticas de registro; la fluidez en el lenguaje común en torno a la trazabilidad, integridad y auditoría de los datos; y una menor carga manual en finanzas y más control preventivo gracias a la automatización. “En la práctica, TI y finanzas pasan de colaborar a copilotar el cumplimiento antifraude dentro de la organización”.

Conviene también repasar el calendario de implementación. A partir del 1 de enero de 2026, todas las personas jurídicas deberán usar sistemas de facturación adaptados, mientras que el resto de profesionales y personas autónomas tienen hasta el 1 de julio del próximo año para integrar estas herramientas.

Facturación electrónica

Aunque Verifactu podría considerarse como un primer paso hacia la factura electrónica —especialmente para aquellos negocios que no estaban aún utilizando sistemas de este tipo—, este modelo de facturación es obligatoria para el trabajo con la administración desde 2013. En los próximos años está previsto que se extienda, gracias a la conocida como Ley Crea y Crece. Esta normativa de 2022 “establece la obligación de emitir, remitir y recibir facturas electrónicas en las operaciones entre empresarios y profesionales”, recuerdan Yus y Pérez, aunque aún está pendiente de desarrollo reglamentario. “A fecha de hoy, por lo tanto, todavía no está definido con detalle el alcance de esta medida y su fecha de entrada en vigor”.

La normativa trabaja en una línea semejante a la de la Ley Antifraude, buscando “combatir la morosidad comercial, reforzar la transparencia en los pagos y acelerar la digitalización de las relaciones empresariales, promoviendo procesos más eficientes, trazables y automatizables”, destacan las abogadas de Cuatrecasas. Por el momento, en el último borrador publicado, se contempla que la factura en operaciones B2B deberá ser un mensaje electrónico estructurado conforme a determinados requerimientos. “El envío de un PDF no resultará suficiente, pues se exigen datos estructurados interoperables procesables automáticamente por los sistemas”, resumen. Está además prevista una solución pública de facturación de la propia AEAT, que actuará como repositorio de las facturas electrónicas y coexistirá con plataformas privadas de intercambio de datos. “Estas plataformas deberán garantizar la interconexión e interoperabilidad gratuitas entre ellas”.

“El envío de un PDF no resultará suficiente, pues se exigen datos estructurados interoperables procesables automáticamente por los sistemas”, indican Lucía Pérez y Meritxell Yus

Aunque al no haberse publicado el desarrollo normativo no se puede hablar de un calendario completo, se estima que las grandes empresas, con un volumen de operaciones superior a 8 millones de euros, tendrán 12 meses desde su publicación para adaptarse, mientras que para el resto este plazo se extenderá hasta los 24 o incluso 36 meses.

컨설팅 기업의 시니어 전문가로 리테일 대기업의 DX 프로젝트에 합류했을 때, 첫 회의는 예상대로 흘러갔다. 회의 내용이 ‘더 많은 도구가 필요하다’는 데 집중된 것이었다. 효율성을 논의할 때마다 대화는 또 다른 벤더 데모나 플랫폼 제안으로 이어졌고, 워크플로우용 SaaS부터 고객 서비스를 위한 챗봇, 분석 업무를 위한 AI 대시보드까지 끊임없이 도구 목록이 늘어났다.

이는 많은 조직이 습관적으로 보이는 반응이다. DX의 의미가 새로운 도구를 도입하는 것으로 여겨지고, 경영진은 구매한 기술의 개수로 성과를 판단하는 경우가 많다. 그러나 회의 참여자들이 ‘현재 부족한 시스템’을 열정적으로 나열하는 모습을 보며 문득 의문이 떠올랐다. 과연 우리는 비즈니스를 전환하고 있는 것일까, 아니면 그저 도구 상자만 더 키우고 있는 것일까? 하는 질문이었다.

이런 패턴을 이미 여러 차례 목격해 왔다. 좋은 의도로 플랫폼을 계속 추가했지만, 결국 더해지는 건 역량이 아니라 복잡성이었다. 그래서 당시 기준으로는 다소 역행하는 제안을 꺼냈다. 더 추가하기 전에, 먼저 무엇을 뺄 수 있는지 살펴보자는 것이었다.

이 제안에 회의실은 잠시 조용해졌고 몇몇은 놀란 표정을 지었지만, 그 대화는 지금까지 참여했던 프로젝트 가운데 가장 효과적인 DX 프로그램으로 이어지는 계기가 됐다.

과도한 도구 사용의 숨은 비용

오늘날 기업에서 ‘도구 확산(tool sprawl)’은 눈에 잘 드러나지 않지만 막대한 비용을 초래하는 요인이다. 여러 산업에서 조직은 통합 속도를 따라가지 못할 만큼 중복 소프트웨어를 빠르게 쌓아 올리고 있고, 이로 인해 혼란이 발생하고 데이터가 중복되며 구독 비용이 증가하고 있다.

해당 리테일 기업도 예외는 아니었다. 각 업무 부서가 선호하는 도구를 제각각 들여오면서, 프로젝트 관리 보드, 고객 피드백 수집 도구뿐만 아니라 협업 앱만 해도 6개가 넘었다.

프로젝트에 참여한 첫 달에 해당 기업의 디지털 생태계를 전면 조사했는데, 결과는 예상을 훨씬 뛰어넘었다. 조직 전체에서 실제 사용 중인 애플리케이션이 142개에 달했고, 협업용 도구만 해도 12개가 넘었다. 일부 부서는 동일한 프로세스를 처리하기 위해 3가지 도구를 동시에 사용해 기록이 중복되고, 책임 소재가 불분명해지는 경우도 나타났다.

워크숍에서 한 팀장은 “적절한 파일을 찾는 게 새 직원을 찾는 것보다 어렵다”라고 농담처럼 말했다. 가볍게 한 말이었지만, 업무에서 겪는 좌절감이 정확하게 드러났다. 생산성을 끌어올려야 할 기술이 오히려 사람들의 속도를 늦추고 있었던 것이다.

이런 혼란을 해결하기 위해 많은 리더가 새로운 소프트웨어를 덧붙이는 방식으로 대응한다. 그러나 하버드비즈니스리뷰가 지적하듯, 도구가 많아진다고 생산성이 자동으로 높아지는 것은 아니며, 오히려 혼란만 가중시킬 수 있다.

추가보다 감축을 선택

문제 해결을 위해 2가지 간단한 질문에서 시작했다.

1. 사람들이 실제로 매일 사용하는 도구는 무엇인가?
2. 각 도구가 제공하는, 측정 가능한 가치는 무엇인가?

이 두 질문이 대화를 완전히 바꿔 놓았다. ‘빠져 있는 것은 무엇인가’에서 ‘실제로 잘 작동하는 것은 무엇인가’를 묻기 시작했다.

이후 직원을 대상으로, 사용 중인 도구의 유용성과 도입 용이성을 익명으로 평가하는 세션을 여러 차례 진행했다. 결과는 예상보다 더 명확했다. 전체 도구의 약 40%는 활성 사용자가 15%에도 미치지 못했다. 일부는 파일럿용으로만 도입돼 확장하지 못한 도구였고, 또 일부는 이미 엔터프라이즈 시스템에서 제공하는 기능과 그대로 중복됐다.

예를 들어, 3개 부서가 각각 별도의 설문 조사 도구를 유료로 사용하고 있었지만, 사실 CRM만으로도 피드백 수집 기능을 충분히 처리할 수 있었다. 또 다른 분석 플랫폼은 재무팀이 내부적으로 이미 만들고 있는 보고서와 거의 동일한 결과를 내고 있었다.

이 내용을 공유하자 한 임원이 “그렇다면 우리 소프트웨어의 3분의 1을 줄여도 아무도 눈치채지 못한다는 건가요?”라고 물었다. 이에 “눈치채긴 할 겁니다. 좋은 방향으로요”라고 답했다.

이 대화가 첫 통합 파일럿 프로젝트로 이어졌다. 주요 대상은 부서별 파편화가 심했던 협업 도구와 분석 시스템이었다. 다만 기존 시스템에 익숙하던 일반 사용자들이 반발하고 나섰다. 이에 사용자 경험을 중심으로 대응해, 도구 간소화가 로그인 횟수를 줄이고, 데이터를 일관되게 하며, 응답 속도를 높인다는 점을 구체적으로 보여줬다. 그렇게 조금씩 공감대가 형성되기 시작했다.

기술 스택 간소화

가장 먼저 착수한 일은 협업 환경의 표준화였다. 13개에 달하던 커뮤니케이션 플랫폼을 4개로 줄이고, 단일 로그인 체계를 도입했으며, 승인된 도구들을 사내 인트라넷과 연동했다. 이제 팀은 필요한 정보를 찾기 위해 여러 채팅 창, 업무 추적 도구, 문서 포털 사이를 오갈 필요가 없어졌다.

다음으로는 분석과 보고 체계를 정비했다. 여러 BI 도구에 흩어져 있던 대시보드를 남기는 대신, 핵심 지표를 하나의 통합 화면으로 모았다. 또 데이터 거버넌스 체크리스트를 마련하고, 보고서 스케줄링을 자동화해 수작업으로 일일이 맞춰보는 과정을 없앴다.

이 작업에는 약 5개월이 걸렸지만, 효과는 즉각적이었고 수치로도 명확하게 드러났다.

• 첫 해 소프트웨어 라이선스 비용 25% 절감
• 남은 도구의 활성 사용률 38% 증가
• 벤더 연동 수를 절반으로 줄이며 보안 구조 간소화
• 시스템 간 이동이 줄어든 덕분에 직원 만족도 향상, 특히 매장 관리자들의 평가가 뚜렷하게 개선

몇 달 후, 한 부서장이 “이제 필요한 정보를 찾으려고 세 사람에게 전화할 필요가 없어졌다”라고 말했을 때가 가장 인상적인 순간이었다. 그 말을 듣는 순간, 도구 간소화 전략이 확실히 효과를 냈다는 걸 알 수 있었다.

이 경험은 연구 결과와도 일치한다. 리워크드 조사에 따르면, 여러 협업 도구를 동시에 사용해야 하는 직원은 하루에 최대 한 시간의 생산성을 잃고 있다. 실제로 확인한 데이터도 이 추정치와 거의 정확히 맞아떨어졌다.

도구 간소화 전략에서 얻은 교훈

돌이켜보면 특히 놀라운 점은 비용을 얼마나 절감했는지가 아니라, 조직의 시야가 얼마나 선명해졌느냐였다. 도구 간소화는 자연스럽게 조직 시스템을 일관되게 만들었다. 중복된 도구가 사라지자 팀들은 소프트웨어가 아니라 실제 협업을 통해 더 많이 소통하기 시작했다. 모두가 동일한 데이터와 시스템을 사용하자 의사결정 속도도 훨씬 빨라졌다.

한 임원은 “안개가 걷힌 느낌”이라고도 표현했다. 단순히 도구가 줄어든 것이 아니라, 일하는 목적이 다시 또렷해졌다는 의미였다.

이 프로젝트에서 어려웠던 부분은 기술적 실행이 아니었다. DX가 곧 ‘도구를 추가하는 일’이라는 인식을 바꾸는 과정이었다. 오랫동안 조직은 도입한 기술의 개수로 디지털 성숙도를 판단해왔다. 그러나 하버드비즈니스리뷰는 생산성을 만들어내는 것이 도구 자체가 아니라 사람과 프로세스라고 지적한 바 있다. 필자의 경험 역시 이 주장과 거의 유사했다.

해당 프로젝트를 통해 얻은 주요 교훈은 다음과 같다.

• 간소화는 혁신을 가능하게 한다. 기반이 불안정하면 혁신은 일어나지 않는다. 통합은 운영뿐만 아니라 정신적인 여유도 확보해 준다.
• 도구 거버넌스는 필수다. 새로운 플랫폼은 반드시 존재 이유를 설명할 수 있어야 하며, 기존 시스템 대비 어떤 가치를 추가하는지 분명해야 한다.
• 문화가 변화를 받아들이는 힘을 결정한다. 도구를 없애는 결정이 왜 필요한지 이해하지 못하면, 직원은 이를 위협으로 느낄 수 있다. 투명하게 소통하고 이해시키는 것이 결정적인 차이를 만든다.

DX는 더 많은 기술을 빠르게 도입하는 경쟁이 아니다. 올바른 기술이 조직에 지속적인 가치를 제공하도록 만드는 일에 가깝다.

장기적인 기반 구축

도구 통합 단계를 마친 뒤, 해당 리테일 기업의 IT 로드맵은 완전히 달라졌다. 서로 단절된 수십 개의 플랫폼을 유지하는 대신, 통합 최적화와 직원 교육, 분석 역량 강화에 더 투자하기 시작했다. 시스템 기반을 안정적으로 다져놓자, 이후에는 AI 기반 수요 예측, 고도화된 개인화, 프로세스 자동화 같은 신기술을 도입하더라도 확신을 갖고 추진할 수 있었다.

많은 조직이 이 변화를 간과한다. 새로운 도구를 좇는 일이 언제나 매력적으로 보이지만, 실제 전환은 사람과 프로세스가 함께 작동하는 방식을 개선하는 데서 시작된다.

1년 뒤, 해당 기업의 CIO와 다시 만난 자리에서 그는 “새 AI 도구 도입이 아니라 불필요한 요소를 제거한 것이 가장 잘한 결정이었다”라고 말했다. 이 말은 혁신의 역설을 정확히 보여준다. 진전은 때로 무언가를 더하는 것이 아니라 덜어내는 데서 시작된다.

* Pawan Deep Singh는 딜로이트 소속 컨설턴트다. 그러나 본 글은 딜로이트 및 특정 고객사의 입장이 아니다. dl-ciokorea@foundryco.com

국내 IT 및 비즈니스 관계자 60여 명이 참석한 가운데, 이번 행사에서는 AI 에이전트 기반의 업무 환경 재설계부터 자동화된 운영 모델, 영상·언어·보안 데이터를 통합적으로 활용하는 새로운 접근까지 기업의 일하는 방식 전반을 AI 중심으로 다시 구성하는 실질적 전략들이 공유됐다.

IDC는 최근 발표한 보고서에서, 앞으로 디지털 트랜스포메이션이 단순한 기술 도입을 넘어 ‘AI를 통해 비즈니스 가치를 직접 만드는 단계’로 진입하고 있다고 분석했다. 특히 IDC는 AI 에이전트가 여러 시스템과 데이터를 스스로 연결해 의사 결정을 수행하는 집단 지능 구조가 2026년 기업 운영의 표준이 될 것이라고 전망하면서, “이는 개념 증명(PoC) 중심의 도입 단계가 끝나고 AI가 실제 운영에 진입하고 있다는 신호”라고 설명했다.

기조연설에서 한국IDC 김경민 이사는 이런 변화 속에서 CIO의 역할이 근본적으로 재정립되고 있다고 강조했다. 김경민 이사는 CIO의 역할이 기술 관리자를 넘어 조직의 가치·인재·운영 체계를 설계하는 ‘디지털 오케스트레이터’가 될 것이라고 언급하며, AI 도입 여부가 아니라 AI가 만들어내는 비즈니스 가치를 어떻게 측정하고 확장할 것인지를 책임지는 위치가 될 것이라고 진단했다. 김 이사는 또한 아시아 태평양 지역과 한국에서 AI 투자가 각각 3.3배, 6.2배 증가하고 있다면서, “2026년 이후 기업의 경쟁력은 에이전트 AI를 얼마나 빠르게 전사 프로세스에 내재화하느냐로 결정될 것”이라고 전망했다.

행사에서는 다양한 분야의 기업이 AI 기반 혁신을 현실에 적용하고 있는 전략과 방향성을 엿볼 수 있었다. 먼저 노션의 프로덕트 오퍼레이션 리드 리처드 강은 기업이 겪는 근본적인 문제가 업무 도구의 과잉과 정보 파편화라고 진단했다. 문서, 노트, 프로젝트 관리 도구 등이 따로 분리되어 있다 보니 직원이 정보를 찾고 정리하는 데 더 많은 시간을 쓰며, 같은 내용을 여러 공간에서 중복 관리하는 비효율이 발생한다는 것이다.

리처드 강은 “필요한 것은 새 도구가 아니라 흩어진 지식을 한곳에 모아 연결하는 단일 워크스페이스”라고 강조하며, 이런 배경에서 노션이 문서 요약부터 PDF·파워포인트·스프레드시트까지 AI 기반으로 처리할 수 있는 통합 환경을 구축해 왔다고 설명했다. 특히 노션 맞춤형 AI 에이전트가 팀의 규칙과 작업 방식을 학습해 반복 업무를 자동 처리하며, 실제 기업에서 ‘가상 인턴’처럼 질문에 응답하고 문서를 업데이트하는 등 효율을 실질적으로 높일 수 있다고 설명했다.

한편 옴니사의 아시아태평양 시니어 디렉터 제이 사네이는 디지털 근무 환경이 변하고 업무가 공간·디바이스·네트워크를 넘나드는 시대에 기존 방식으로는 운영이 불가능해지고 있다고 강조했다. 사네이는 이런 변화 속에서 AI가 운영 전반을 실시간으로 감지하고 판단하며 실행하는 운영 모델이 필요하다고 설명했다.

옴니사는 이를 위해 AI 기반의 ‘폐쇄 루프(Closed Loop)’ 운영 모델을 적용한 통합 디지털 워크 플랫폼을 제시했다. 사네이는 “과거에는 장애가 발생하면 IT가 티켓을 열고, 원인을 찾고, 패치를 적용해야 했다. 하지만 이제는 AI가 자체적으로 신호를 감지하고, 원인을 진단하고, 조치를 실행하고, 결과까지 검증한다. 사람보다 훨씬 빠르고 정확하게 움직이는 ‘AI 운영 엔진’이 기업의 기본 인프라가 될 것”이라고 진단했다.

강시호 델 테크놀로지스 이사는 윈도우 10 지원 종료와 AI 확산이 맞물리며 AI PC 전환이 더 이상 선택이 아닌 필수가 됐다고 강조했다. 그는 “온디바이스로 실시간 추론, 요약, 코딩, 보안 분석을 처리해야 하는 시대에 기존 PC로는 감당할 수 없다”라며, 기업의 대부분 PC가 앞으로 AI 처리 전용 엔진(NPU)을 탑재한 AI PC로 재편될 것이라고 전망했다. 실제로 델의 글로벌 조사에 따르면, 2028년까지 전체 PC의 98%가 AI PC로 전환될 것으로 나타났다.

강시호 이사에 따르면 델 테크놀로지스는 업무 환경 변화에 맞춰 PC부터 주변 기기, 보안, 관리 솔루션까지 워크스페이스 전체를 통합 지원하는 생태계를 제공하고 있다. 강 이사는 “업무 환경이 복잡해질수록 기기와 소프트웨어, 보안 체계가 하나의 흐름으로 작동해야 생산성을 유지할 수 있다. 통합에 중심에는 AI PC가 있으며, 이는 단순한 하드웨어가 아니라 업무·보안·데이터·지능을 모두 담는 플랫폼으로 재정의되고 있다”라고 설명했다.

버카다 코리아의 이상훈 상무는 물리 보안이 더 이상 총무나 시설 관리의 영역이 아니라 CIO가 담당해야 할 새로운 영역임을 강조했다. 기업이 보유한 가장 방대한 비정형 데이터가 CCTV 영상인 동시에 사이버 공격 표면도 물리보안 영역에 집중돼 있기 때문이다. 이상훈 상무는 “이를 CIO의 관리 영역으로 다루지 않으면 보안 측면에서도, 데이터 측면에서도 리스크를 방치하는 것”이라고 지적했다.

이상훈 상무는 기업 내 CCTV·출입통제·센서 시스템이 10년 넘게 정체돼 있어, 기업이 가진 가장 큰 비정형 데이터 자원이 제대로 활용되지 못하고 있다고 언급했다. 버카다는 이를 해결하기 위해 물리보안 장비를 하나의 클라우드 기반 플랫폼으로 통합하고, 영상·이미지·텍스트를 이해하는 멀티모달 파운데이션 모델을 적용해 영상을 검색 가능한 데이터로 전환했다고 소개했다. 또한 이를 POS·ERP·생산관리 시스템과 연동해 민원 대응 시간을 줄이고 운영 효율을 높이고 있다고 덧붙였다. 이 상무는 “AI가 가장 빠르게 혁신할 분야가 바로 물리 보안”이라며 물리 보안이 기업의 비정형 데이터 전략과 디지털 전환의 핵심 축이 될 것이라고 내다봤다.

딥엘의 최용수 AE(Account Executive)는 글로벌 비즈니스에서 언어가 단순한 역량 문제가 아니라 정보 접근성과 의사결정 속도를 좌우하는 요소가 됐다고 설명했다. 그는 국내 기업의 글로벌 커뮤니케이션 문제를 ‘언어 장벽’이 아닌 ‘언어 누수’라고 진단하며, 글로벌 회의에 참석해도 참석자마다 이해도가 달라 정보가 온전히 전달되지 않고, 미세한 차이가 조직 전체의 실행력 저하로 이어지고 있다고 지적했다.

최용수 AE는 이 문제를 해결한 기업에서 회의 문화가 바뀌고 있다는 점을 강조했다. 딥엘이 개발한 실시간 AI 음성 번역 솔루션은 회의 참석자들의 발언을 즉시 번역해 모두가 동일한 정보를 얻도록 지원하고 있다. 최 AE는 “작은 차이가 실시간 회의에서는 크게 번질 수 있다”라고 강조하면서, 해당 솔루션을 활용한 기업에서 질문과 토론이 늘고 정보 흐름이 명확해지는 등 회의 문화가 개선되는 효과를 경험하고 있다고 밝혔다.

마지막 세션에서는 경제학자 김영익 교수가 2026년 글로벌 및 국내 경제 전망의 주요 흐름을 짚었다. 그는 2008년 금융위기와 2020년 팬데믹 이후 각국 정부와 중앙은행이 재정 확장과 초저금리 정책으로 경기를 떠받쳐 왔지만, 그 후유증으로 정부·기업·가계 부채가 시스템 전반에 쌓였다고 분석했다. 이에 따라 향후 5년간 세계 경제 성장률은 평균 3% 안팎에 머물 것으로 전망했다.

김 교수는 “저성장이 심화되면 공급 과잉이 발생하고, 결국 모든 기업이 시장에 남을 수는 없는 환경이 된다”라며, 앞으로 기업이 선택과 집중, 구조조정, 인재 다양성과 조직의 복원력 확보를 위한 전략을 마련해야 한다고 조언했다. 김 교수는 특히 불확실성이 커지는 시대일수록 조직 구성의 다양성과 유연성이 기업 생존을 좌우할 것이라고 강조했다.
yuseong.kim@foundryco.com

When I joined a major retail digital transformation project as a senior specialist with a consulting firm, the first meeting went exactly as I expected: “We need more tools.” Every discussion about efficiency seemed to end with another vendor demo or platform suggestion — a new SaaS for workflow, a chatbot for customer service, an AI dashboard for analytics.

It’s a reflex many organizations have developed. Digital transformation has become synonymous with tool adoption and executives often measure progress by the number of technologies purchased. But as I listened to the project sponsors enthusiastically list the “missing systems,” one question came to mind: Were we transforming the business or simply expanding the toolbox?

I had seen this pattern before — a well-intentioned accumulation of platforms that ultimately increased complexity rather than capability. So I proposed something that sounded contrarian at the time: Instead of adding more, let’s first see what we can remove.

The suggestion earned a few surprised looks, but that conversation became the turning point of one of the most effective transformation programs I’ve been part of.

The hidden cost of tool sprawl

In the modern enterprise, tool sprawl is a quiet but costly epidemic. Across industries, organizations accumulate overlapping software faster than they can integrate it, creating confusion, duplicating data and raising subscription costs.

The retail client I supported was no exception. Over time, every department had picked its own favorites — project management boards here, customer feedback tools there and a half-dozen collaboration apps in between.

During my first month, I conducted an audit of the digital ecosystem. The result was startling: 142 separate applications in active use across the organization, with more than a dozen dedicated to collaboration alone. Some departments used three tools to manage the same process, creating duplicate records and confusion about ownership.

In one workshop, a team leader joked that “finding the right file is harder than finding a new employee.” It was meant humorously, but it captured the frustration perfectly. Instead of driving productivity, technology was slowing people down.

Leaders often try to fix this kind of chaos by layering on new software. But as Harvard Business Review notes, more tools don’t automatically equal more productivity — they often amplify the noise.

Choosing subtraction over addition

I started with two simple questions:

1. What tools do people actually use every day?
2. What measurable value does each tool provide?

Those questions changed the conversation. Instead of “what’s missing?” we began asking “what’s working?”

We hosted a series of discovery sessions where employees anonymously rated the tools they used by usefulness and ease of adoption. The results were eye-opening. Nearly 40% of tools had fewer than 15% active users. Some were bought for pilot programs that never scaled; others replicated capabilities already available in enterprise systems.

For example, three departments paid for separate survey tools even though their CRM could already handle feedback collection. Another analytics platform generated reports nearly identical to what finance could produce internally.

When I presented the findings, one executive asked, “So you’re saying we could cut a third of our software and no one would notice?”

I replied, “You might notice — in a good way.”

That comment led to our first pilot consolidation: collaboration and analytics, the two most fragmented areas. The pushback came not from IT, but from end-users attached to their preferred systems. We countered that by focusing on the user experience. We showed how simplification would mean fewer logins, consistent data and faster response times. Slowly, the idea gained traction.

Streamlining the stack

Our first step was to standardize the collaboration environment. We reduced 13 communication platforms down to four, implemented single sign-on and integrated the approved tools with the company’s intranet. Teams no longer had to jump between chat windows, task trackers and document portals to find information.

Next, we rationalized analytics and reporting. Instead of maintaining multiple dashboards across BI tools, we centralized key metrics into one unified view. We created a data-governance checklist and automated report scheduling to eliminate manual reconciliation.

The process took about five months, but the results were immediate and measurable:

• 25% reduction in software licensing costs within the first year.
• 38% increase in active usage across remaining tools.
• Simplified security posture, reducing the number of vendor integrations by half.
• Improved employee satisfaction scores, especially among frontline retail managers who no longer needed to toggle between systems.

One of my favorite moments came a few months later when a department head told me, “I can finally find what I need without calling three people.” That’s when I knew simplification had worked.

Research supports this experience. According to Reworked.co, employees forced to juggle multiple collaboration tools lose up to an hour of productive time per day. Our data lined up almost exactly with that estimate.

Lessons from a less-is-more approach

Looking back, what surprised me most wasn’t how much we saved, but how much clarity we gained. Simplification forced alignment. Once redundant tools disappeared, teams began to talk more — not through software, but through collaboration. Decisions became faster because everyone used the same data and systems.

One executive commented that it felt as if “the fog had lifted.” They weren’t just working with fewer tools — they were working with purpose.

The hardest part of the project wasn’t technical execution. It was changing the perception that transformation equals accumulation. For years, digital maturity was measured by how many tools an organization deployed. But as Harvard Business Review points out, tools don’t create productivity — people and processes do. Our experience mirrored that insight exactly.

Here’s what I learned:

• Simplification enables innovation. You can’t innovate when your foundation is unstable. Consolidation frees up both mental and operational capacity.
• Tool governance matters. Every new platform should justify its existence — what value does it add beyond what’s already in place?
• Culture drives adoption. Removing tools can feel threatening unless employees understand the why. Transparent communication makes all the difference.

Digital transformation isn’t a race to deploy more technology. It’s a discipline of ensuring the right technology delivers consistent value.

Building for the long term

After the consolidation phase, the client’s IT roadmap looked dramatically different. Instead of dozens of disconnected platforms, they invested in optimizing integrations, training and analytics literacy. By strengthening the foundation first, they could later introduce new technologies — AI forecasting, advanced personalization and process automation — with confidence that the systems underneath would support them.

That’s a crucial shift many organizations overlook. As tempting as it is to chase the next big tool, real transformation comes from improving how people and processes work together.

A year later, when I reconnected with the client’s CIO, she told me, “Our best decision wasn’t the AI tool we added. It was everything we removed before it.”

That’s the paradox of transformation: progress sometimes starts by taking things away.

[Note: The views expressed in this article are my own and do not represent the views of Deloitte or its clients.]

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In 2016, 7-Eleven began a digital transformation aimed at redefining convenience. The starting point was loyalty. “Step one was to build a product discipline, bring the technology in house, and reduce reliance on third parties,” says Scott Albert, VP and head of store and enterprise products.

Two years later, the Texas-based retailer reapplied the product playbook, now powering store systems across more than 13,000 US and Canadian locations. “We moved from projects — start date, end date — to product: continuous improvement and iteration,” Albert says. “From outputs to outcomes, co-owned with design and engineering.”

Albert knows the terrain. A company veteran who cut his teeth in operations, he led product for loyalty and now oversees digital product for store systems, fuel, restaurant concepts, and merchandising, evidence of how far the model has scaled.

Setting the foundation

The idea was straightforward but the shift wasn’t. “It was tough early on because it meant change,” Albert says. “The business was used to saying, ‘I need X.’ Often that wasn’t the real problem. Our job was to get underneath, understand the problem, design a solution for now and the future, and then iterate.”

It takes several ingredients to solve big problems, like customer research, business process knowledge, data, and technology, so it’s natural that product teams are cross-functional. But that structure can also create competing priorities if not managed correctly. While the setting is convenience retail, the lesson applies to any CIO shifting from project-based delivery to product-driven transformation. “Success depends not on org charts, but on cross-functional trust, buy-in, and commitment,” he says.

That structure set the foundation, and the real breakthroughs came from applying product thinking to their daily work.

Product thinking in action

“For me and my team, the customer is the store associate,” Albert says. That focus shaped priorities to remove low-value tasks, surface just-in-time insights, and let systems work for people, not the other way around.

The team learned this firsthand on midnight store walks. In one New York City visit, they noticed a new associate glued to her phone. “We thought she was distracted,” Albert says. “Turns out she’d recorded her trainer so she could remember.” That single observation sparked a redesign of training to move job aids and how-to videos from a back-room PCs to mobile devices on the floor, embedded in the flow of work.

The same product instinct of watching users, identifying friction, and iterating has carried into 7-Eleven’s AI initiatives. AI-assisted ordering, for example, reduced what was once up to 30 hours a week of manual work to under an hour a day, freeing up associates to focus on customers. At scale, those savings add up to more than 13 million hours reclaimed annually, and test-and-learn pilots tying the changes to about $340 million in incremental sales.

The back office has been transformed as well. After migrating store systems to the cloud with its 7-BOSS platform, 7-Eleven layered in “quick cards” that surface AI-generated insights and let associates act in three clicks or less. A clustering model identifies lookalike stores by sales mix, location type, even seasonality, and pushes tailored assortment recommendations. “With three clicks, you can add an item, forecasting kicks in, and delivery happens in days,” Albert says.

Together, these stories trace a clear pattern of observing the customer (in this case the store personnel), solving for their pain points, then amplifying the solution with data and AI. It’s product thinking at work.

Operating like a product company

Behind the scenes, the mechanics mirror digital natives. Teams run in pods with product, engineering, and design as a three-legged stool. Quarterly planning sets direction, but roadmaps flex. “Tell me everything you’ll do next year — that was the old model,” Albert says. “Now we focus on quarters, but sometimes that’s too long. We plan, then adapt.”

Release cadence has accelerated as well, from two or three big bangs a year to monthly releases.

The cultural shift is ongoing funding for work that never ends. “There’s no such thing as done in product,” he says. “We’re on the fifth iteration of our forecasting model. We’ll keep improving.”

Start small, measure hard

Albert’s advice to other tech executives: start small. “Find a problem that matters, build a cross-functional team, measure success, and validate results,” he says. “Then add a second team, a third, and you’re off.”

And above all, measure. “Pick metrics backed by data so no one can debate the results,” he adds.

Nearly 10 years after its first loyalty decision, 7-Eleven’s product mindset now extends far beyond consumer apps. The store itself has become a living product, updated monthly, informed by data, and built around the associate.

For Albert, the real measure of success is to make the system work for the associate, so they can delight customers. “It’s the same product discipline, now applied to every corner of the store, and it’s redefining what convenience looks like at scale,” he says.