In times of disruption, Agile leadership can help CIOs make better, faster decisions — and guide their teams to execute with speed and discipline.

When the first case of COVID hit my home city, it was only two weeks after I’d become president of The Persimmon Group. For more than a decade, I’d coached leaders, teams and PMOs to execute their strategy with speed and discipline.

But now — in a top job for the first time — I was reeling.

Every plan we had in motion — strategic goals, project schedules, hiring decisions — was suddenly irrelevant. Clients froze budgets. Team members scrambled to set up remote work for the first time, many while balancing small children and shared spaces.

Within days, we were facing a dozen high-stakes questions about our business, all with incomplete information. Each answer carried massive operational and cultural implications.

We couldn’t just make the right call. We had to make it fast. And often, we were choosing between a bunch of bad options.

From crisis to cadence

At first, we tried to lead the way we always had: gather the facts, debate the trade-offs and pick the best path forward. But in a landscape that changed daily, that rhythm broke down fast.

The information we needed didn’t exist yet. The more we waited for certainty — or gamed out endless hypotheticals — the slower and more reactive we became.

And then something clicked. What if the same principles that helped software teams move quickly and learn in real time could help lead us through uncertainty?

So we started experimenting.

We shortened our time horizons. Made smaller bets. Created fast feedback loops. We became almost uncomfortably transparent, involving the team directly in critical decisions that affected them and their work.

In the months that followed, those experiments became the backbone of how we led through uncertainty — and how we continue to lead today.

An operating system for change

What emerged wasn’t a formal framework. It was a set of small, deliberate habits that brought the same rhythm and focus to leadership that Agile brings to delivery.

Here’s what that looked like in practice:

Develop a ‘fast frame’ to focus decisions

In the first few months of the pandemic, our leadership meetings were a tangle of what-ifs. What if we lost 20% of planned revenue this year? What if we lost 40%? Would we do layoffs? Furloughs? Salary cuts? And when would we do them — preemptively or reactively?

We were so busy living in multiple possible futures that it was difficult to move forward with purpose. To break out of overthinking mode, we built a lightweight framework we now call our fast frame. It centered on five questions:

1. What do we know for sure?
2. What can we find out quickly?
3. What is unknowable right now?
4. What’s the risk of deciding today?
5. What’s the risk of not deciding today?

The fast frame forced us to separate facts from conjecture. It also helped us to get our timing right. When did we need to move fast, even with imperfect information? When could we afford to slow down and get more data points?

The fast frame helped us slash decision latency by 20% to 30%.

It kept us moving when the urge was to stall and it gave us language to talk about uncertainty without letting it rule the room.

Build plans around small, fast experiments

After using our fast frame for a while, we realized something: Our decisions were too big.

In an environment changing by the day, Big Permanent Decisions were impractical — and a massive time sink. Every hour we spent debating a Big Permanent Decision was an hour we weren’t learning something important.

So we replaced them with For-Now Decisions — temporary postures designed to move us forward, fast, while we learned what was real.

Each For-Now Decision had four parts:

1. The decision itself — the action we’d take based on what we knew at that moment.
2. A trigger for when to revisit it — either time-based (two weeks from now) or event-based (if a client delays a project).
3. A few learning targets — what we hoped to discover before the next checkpoint.
4. An agility signal — how we communicated the decision to the team. We’d say, “This is our posture for now, but we may change course if X. We’ll need your help watching for Y as we learn more.”

By framing decisions this way, we removed the pressure to be right. The goal wasn’t to predict the future but to learn from it faster. By abandoning bad ideas early, we saved 300 to 400 hours a year.

Increase cadence and transparency of communication

In those early weeks, we learned that the only thing more dangerous than a bad decision was a silent one. When information moves slower than events, people fill the gaps with assumptions.

So we made communication faster — and flatter. Every morning, our 20-person team met virtually for a 20-minute standup. The format was simple but consistent:

• Executive push. We shared what the leadership team was working on, what decisions had been made and what input we needed next.
• Team pull. Anyone could ask questions, raise issues or surface what they were hearing from clients.
• Needs and lessons. We ended with what people needed to stay productive and what we were learning that others could benefit from.

The goal wasn’t to broadcast information from the top — or make all our decisions democratically. It was to create a shared operating picture. The standup became a heartbeat for the company, keeping everyone synchronized as conditions changed.

Transparency replaced certainty. Even when we didn’t have all the answers, people knew how decisions were being made and what we were watching next. That openness built confidence faster than pretending we had it all figured out.

That transparency paid off.

While many small consulting firms folded in the first 18 months of the pandemic, Agile leadership helped us double revenue in 24 months.

We stayed fully staffed — no layoffs, no pay cuts beyond the executive team. And the small bets we made during the pandemic helped rapidly expand our client base across new industries and international geographies.

Develop precise language to keep the team aligned

As we increased the speed of communication, we discovered something else: agility requires precision. When everything is moving fast, even small misunderstandings can send people sprinting in different directions.

We started tightening our language. Instead of broad discussions about what needed to get done, we’d ask, “What part of this can we get done by Friday?” That forced us to think in smaller delivery windows, sustain momentum and get specific about what “done” looked like.

We also learned to clarify between two operating modes: planning versus doing. Before leaving a meeting where a direction was discussed, we’d confirm our status:

• Phase 1 meant we were still exploring, shaping and validating and would need at least one more meeting before implementing anything.
• Phase 2 meant we were ready to execute.

That small distinction saved us hours of confusion, especially in cross-functional work.

Precise language gave us speed. It eliminated assumptions and kept everyone on the same page about where we were in the process. The more we reduced ambiguity, the faster — and calmer — the team moved.

Protect momentum by insisting on rest

Agility isn’t about moving faster forever — it’s about knowing when to slow down. During the first months of the pandemic, that lesson was easy to forget. Everything felt urgent and everyone felt responsible.

In software, a core idea behind Agile sprints is maintaining a sustainable pace of work. A predictable, consistent level of effort that teams can plan around is far more effective than the heroics often needed in waterfall projects to hit a deadline.

Agile was designed to be human-centered, protecting the well-being and happiness of the team so that performance can remain optimal. We tried to lead the same way.

After the first few frenetic months, I capped my own workday at nine hours. That boundary forced me to get honest about what could actually be done in the time I had — and prioritize ruthlessly. It also set a tone for the team. We adjusted scopes, redistributed work and held one another accountable for disconnecting at day’s end.

The expectation wasn’t endless effort — it was sustainable effort. That discipline kept burnout low and creativity high, even during our most demanding seasons. The consistency of our rest became as important as the intensity of our work. It gave us a rhythm we could trust — one that protected our momentum long after the crisis passed.

Readiness is the new stability

Now that the pandemic has passed, disruption has simply changed shape — AI, market volatility, new business models and the constant redefinition of “normal.” What hasn’t changed is the need for leaders who can act with speed and discipline at the same time.

For CIOs, that tension is sharper than ever. Technology leaders are being asked to deliver transformation at pace — without burning out their people or breaking what already works. The pressures that once felt exceptional have become everyday leadership conditions.

But you don’t have to be a Scrum shop or launch an enterprise Agile transformation to lead with agility. Agility is a mindset, not a method. To put the mindset into practice, focus on:

• Shorter planning horizons
• Faster, smaller decisions
• Radical transparency
• Language that brings alignment and calm
• Boundaries that protect the energy of the team

These are the foundations of sustainable speed.

We built those practices in crisis, but they’ve become our default operating system in calmer times. They remind me that agility isn’t a reaction to change — it’s a readiness for it. And in a world where change never stops, that readiness may be a leader’s most reliable source of stability.

This article is published as part of the Foundry Expert Contributor Network.
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This was the year of AI agents. Chatbots that simply answered questions are now evolving into autonomous agents that can carry out tasks on a user’s behalf, so enterprises continue to invest in agentic platforms as transformation evolves. Software vendors are investing in it as fast as they can, too.

According to a National Research Group survey of more than 3,000 senior leaders, more than half of executives say their organization is already using AI agents. Of the companies that spend no less than half their AI budget on AI agents, 88% say they’re already seeing ROI on at least one use case, with top areas being customer service and experience, marketing, cybersecurity, and software development.

On the software provider side, Gartner predicts 40% of enterprise software applications in 2026 will include agentic AI, up from less than 5% today. And agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion, up from 2% in 2025. In fact, business users might not have to interact directly with the business applications at all since AI agent ecosystems will carry out user instructions across multiple applications and business functions. At that point, a third of user experiences will shift from native applications to agentic front ends, Gartner predicts.

It’s already starting. Most enterprise applications will have embedded assistants, a precursor to agentic AI, by the end of this year, adds Gartner.

IDC has similar predictions. By 2028, 45% of IT product and service interactions will use agents as the primary interface, the firm says. That’ll change not just how companies work, but how CIOs work as well.

Agents as employees

At financial services provider OneDigital, chief product officer Vinay Gidwaney is already working with AI agents, almost as if they were people.

“We decided to call them AI coworkers, and we set up an AI staffing team co-owned between my technology team and our chief people officer and her HR team,” he says. “That team is responsible for hiring AI coworkers and bringing them into the organization.” You heard that right: “hiring.”

The first step is to sit down with the business leader and write a job description, which is fed to the AI agent, and then it becomes known as an intern.

“We have a lot of interns we’re testing at the company,” says Gidwaney. “If they pass, they get promoted to apprentices and we give them our best practices, guardrails, a personality, and human supervisors responsible for training them, auditing what they do, and writing improvement plans.”

The next promotion is to a full-time coworker, and it becomes available to be used by anyone at the company.

“Anyone at our company can go on the corporate intranet, read the skill sets, and get ice breakers if they don’t know how to start,” he says. “You can pick a coworker off the shelf and start chatting with them.”

For example, there’s Ben, a benefits expert who’s trained on everything having to do with employee benefits.

“We have our employee benefits consultants sitting with clients every day,” Gidwaney says. “Ben will take all the information and help the consultants strategize how to lower costs, and how to negotiate with carriers. He’s the consultants’ thought partner.”

There are similar AI coworkers working on retirement planning, and on property and casualty as well. These were built in-house because they’re core to the company’s business. But there are also external AI agents who can provide additional functionality in specialized yet less core areas, like legal or marketing content creation. In software development, OneDigital uses third-party AI agents as coding assistants.

When choosing whether to sign up for these agents, Gidwaney says he doesn’t think of it the way he thinks about licensing software, but more to hiring a human consultant or contractor. For example, will the agent be a good cultural fit?

But in some cases, it’s worse than hiring humans since a bad human hire who turns out to be toxic will only interact with a small number of other employees. But an AI agent might interact with thousands of them.

“You have to apply the same level of scrutiny as how you hire real humans,” he says.

A vendor who looks like a technology company might also, in effect, be a staffing firm. “They look and feel like humans, and you have to treat them like that,” he adds.

Another way that AI agents are similar to human consultants is when they leave the company, they take their expertise with them, including what they gained along the way. Data can be downloaded, Gidwaney says, but not necessarily the fine-tuning or other improvements the agent received. Realistically, there might not be any practical way to extract that from a third-party agent, and that could lead to AI vendor lock-in.

Edward Tull, VP of technology and operations at JBGoodwin Realtors, says he, too, sees AI agents as something akin to people. “I see it more as a teammate,” he says. “As we implement more across departments, I can see these teammates talking to each other. It becomes almost like a person.”

Today, JBGoodwin uses two main platforms for its AI agents. Zapier lets the company build its own and HubSpot has its own AaaS, and they’re already pre-built. “There are lead enrichment agents and workflow agents,” says Tull.

And the company is open to using more. “In accounting, if someone builds an agent to work with this particular type of accounting software, we might hire that agent,” he says. “Or a marketing coordinator that we could hire that’s built and ready to go and connected to systems we already use.”

With agents, his job is becoming less about technology and more about management, he adds. “It’s less day-to-day building and more governance, and trying to position the company to be competitive in the world of AI,” he says.

He’s not the only one thinking of AI agents as more akin to human workers than to software.

“With agents, because the technology is evolving so far, it’s almost like you’re hiring employees,” says Sheldon Monteiro, chief product officer at Publicis Sapient. “You have to determine whom to hire, how to train them, make sure all the business units are getting value out of them, and figure when to fire them. It’s a continuous process, and this is very different from the past, where I make a commitment to a platform and stick with it because the solution works for the business.”

This changes how the technology solutions are managed, he adds. What companies will need now is a CHRO, but for agentic employees.

Managing outcomes, not persons

Vituity is one of the largest national, privately-held medical groups, with 600 hospitals, 13,800 employees, and nearly 14 million patients. The company is building its own AI agents, but is also using off-the-shelf ones, as AaaS. And AI agents aren’t people, says CIO Amith Nair. “The agent has no feelings,” he says. “AGI isn’t here yet.”

Instead, it all comes down to outcomes, he says. “If you define an outcome for a task, that’s the outcome you’re holding that agent to.” And that part isn’t different to holding employees accountable to an outcome. “But you don’t need to manage the agent,” he adds. “They’re not people.”

Instead, the agent is orchestrated and you can plug and play them. “It needs to understand our business model and our business context, so you ground the agent to get the job done,” he says.

For mission-critical functions, especially ones related to sensitive healthcare data, Vituity is building its own agents inside a HIPAA-certified LLM environment using the Workato agent development platform and the Microsoft agentic platform.

For other functions, especially ones having to do with public data, Vituity uses off-the-shelf agents, such as ones from Salesforce and Snowflake. The company is also using Claude with GitHub Copilot for coding. Nair can already see that agentic systems will change the way enterprise software works.

“Most of the enterprise applications should get up to speed with MCP, the integration layer for standardization,” he says. “If they don’t get to it, it’s going to become a challenge for them to keep selling their product.”

A company needs to be able to access its own data via an MCP connector, he says. “AI needs data, and if they don’t give you an MCP, you just start moving it all to a data warehouse,” he adds.

Sharp learning curve

In addition to providing a way to store and organize your data, enterprise software vendors also offer logic and functionality, and AI will soon be able to handle that as well.

“All you need is a good workflow engine where you can develop new business processes on the fly, so it can orchestrate with other agents,” Nair says. “I don’t think we’re too far away, but we’re not there yet. Until then, SaaS vendors are still relevant. The question is, can they charge that much money anymore.”

The costs of SaaS will eventually have to come down to the cost of inference, storage, and other infrastructure, but they can’t survive the way they’re charging now he says. So SaaS vendors are building agents to augment or replace their current interfaces. But that approach itself has its limits. Say, for example, instead of using Salesforce’s agent, a company can use its own agents to interact with the Salesforce environment.

“It’s already happening,” Nair adds. “My SOC agent is pulling in all the log files from Salesforce. They’re not providing me anything other than the security layer they need to protect the data that exists there.”

AI agents are set to change the dynamic between enterprises and software vendors in other ways, too. One major difference between software and agents is software is well-defined, operates in a particular way, and changes slowly, says Jinsook Han, chief of strategy, corporate development, and global agentic AI at Genpact.

“But we expect when the agent comes in, it’s going to get smarter every day,” she says. “The world will change dramatically because agents are continuously changing. And the expectations from the enterprises are also being reshaped.”

Another difference is agents can more easily work with data and systems where they are. Take for example a sales agent meeting with customers, says Anand Rao, AI professor at Carnegie Mellon University. Each salesperson has a calendar where all their meetings are scheduled, and they have emails, messages, and meeting recordings. An agent can simply access those emails when needed.

“Why put them all into Salesforce?” Rao asks. “If the idea is to do and monitor the sale, it doesn’t have to go into Salesforce, and the agents can go grab it.”

When Rao was a consultant having a conversation with a client, he’d log it into Salesforce with a note, for instance, saying the client needs a white paper from the partner in charge of quantum.

With an agent taking notes during the meeting, it can immediately identify the action items and follow up to get the white paper.

“Right now we’re blindly automating the existing workflow,” Rao says. “But why do we need to do that? There’ll be a fundamental shift of how we see value chains and systems. We’ll get rid of all the intermediate steps. That’s the biggest worry for the SAPs, Salesforces, and Workdays of the world.”

Another aspect of the agentic economy is instead of a human employee talking to a vendor’s AI agent, a company agent can handle the conversation on the employee’s behalf. And if a company wants to switch vendors, the experience will be seamless for employees, since they never had to deal directly with the vendor anyway.

“I think that’s something that’ll happen,” says Ricardo Baeza-Yates, co-chair of the  US technology policy committee at the Association for Computing Machinery. “And it makes the market more competitive, and makes integrating things much easier.”

In the short term, however, it might make more sense for companies to use the vendors’ agents instead of creating their own.

“I recommend people don’t overbuild because everything is moving,” says Bret Greenstein, CAIO at West Monroe Partners, a management consulting firm. “If you build a highly complicated system, you’re going to be building yourself some tech debt. If an agent exists in your application and it’s localized to the data in that application, use it.”

But over time, an agent that’s independent of the application can be more effective, he says, and there’s a lot of lock-in that goes into applications. “It’s going to be easier every day to build the agent you want without having to buy a giant license. “The effort to get effective agents is dropping rapidly, and the justification for getting expensive agents from your enterprise software vendors is getting less,” he says.

The future of software

According to IDC, pure seat-based pricing will be obsolete by 2028, forcing 70% of vendors to figure out new business models.

With technology evolving as quickly as it is, JBGoodwin Realtors has already started to change its approach to buying tech, says Tull. It used to prefer long-term contracts, for example but that’s not the case anymore “You save more if you go longer, but I’ll ask for an option to re-sign with a cap,” he says.

That doesn’t mean SaaS will die overnight. Companies have made significant investments in their current technology infrastructure, says Patrycja Sobera, SVP of digital workplace solutions at Unisys.

“They’re not scrapping their strategies around cloud and SaaS,” she says. “They’re not saying, ‘Let’s abandon this and go straight to agentic.’ I’m not seeing that at all.”

Ultimately, people are slow to change, and institutions are even slower. Many organizations are still running legacy systems. For example, the FAA has just come out with a bold plan to update its systems by getting rid of floppy disks and upgrading from Windows 95. They expect this to take four years.

But the center of gravity will move toward agents and, as it does, so will funding, innovation, green-field deployments, and the economics of the software industry.

“There are so many organizations and leaders who need to cross the chasm,” says Sobera. “You’re going to have organizations at different levels of maturity, and some will be stuck in SaaS mentality, but feeling more in control while some of our progressive clients will embrace the move. We’re also seeing those clients outperform their peers in revenue, innovation, and satisfaction.”

Tech talent, especially with advanced and specialized skills, remains elusive. Findings from a recent IT global HR trends report by Gi Group show a 47% enterprise average struggles with sourcing and retaining talent. As a consequence, turnover remains high.

Another international study by Cegos highlights that 53% of 200 directors or managers of information systems in Italy alone say the difficulty of attracting and retaining IT talent is something they face daily. Cybersecurity is the most relevant IT problem but a majority, albeit slight, feels confident of tackling it. Conversely, however, only 8% think they’ll be able to solve the IT talent problem. IT team skills development and talent retention are the next biggest issues facing CIOs in Italy, and only 24% and 9%, respectively, think they can successfully address it.

“Talents aren’t rare,” says Cecilia Colasanti, CIO of Istat, the National Institute of Statistics. “They’re there but they’re not valued. That’s why, more often, they prefer to go abroad. For me, talent is the right person in the right place. Managers, including CIOs, must have the ability to recognize talents, make them understand they’ve been identified, and enhance them with the right opportunities.”

The CIO as protagonist of talent management

Colasanti has very clear ideas on how to manage her talents to create a cohesive and motivated group. “The goal I set myself as CIO was to release increasingly high-quality products for statistical users, both internal and external,” she says. “I want to be concrete and close the projects we’ve opened, to ensure the institution continues to improve with the contribution of IT, which is a driver of statistical production. I have the task of improving the IT function, the quality of the products released, the relevance of the management, and the well-being of people.”

Istat’s IT department currently has 195 people, and represents about 10% of the institute’s entire staff. Colasanti’s first step after her CIO appointment in October 2023 was to personally meet with all the resources assigned to management for an interview.

“I’ve been working at Istat since 2001 and almost everyone knows each other,” she says. “I’ve held various roles in the IT department, and in my latest role as CIO, I want to listen to everyone to gather every possible viewpoint. Because how well we know each other, I feel my colleagues have a high expectation of our work together. That’s why I try to establish a frank dialogue and avoid ambiguity. But I make it clear that listening doesn’t mean delegating responsibility. I accept some proposals, reject others, and try to justify choices.”

Another move was to reinstate the two problems, two solutions initiative launched in Istat many years ago. Colasanti asked staff, on a voluntary basis, to identify two problems and propose two solutions. She then processed the material and shared the results in face-to-face meetings, commenting on the proposals, and evaluating those to be followed up.

“I’ve been very vocal about this initiative,” she says, “But I also believe it’s been an effective way to cement the relationship of trust with my colleagues.”

Some of the inquiries related to career opportunities and technical issues, but the most frequent pain points that emerged were internal communication and staff shortages. Colasanti spoke with everyone, clarifying which points she could or couldn’t act on. Career paths and hiring in the public sector, for example, follow precise procedures where little could be influenced.

“I tried to address all the issues from a proactive perspective,” she says. “Where I perceived a generic resistance to change rather than a specific problem, I tried to focus on intrinsic motivation and people’s commitment. It’s important to explain the strategies of the institution and the role of each person to achieve objectives. After all, people need and have the right to know the context in which they operate, and be aware of how their work affects the bigger picture.”

Engagement must be built day by day, so Colasanti regularly meets with staff including heads of department and service managers.

Small enterprise, big concerns

The case of Istat stands out for the size of its IT department, but in SMEs, IT functions can be just a handful of people, including the CIO, and much of the work is done by external consultants and suppliers. It’s a structure that has to be worked with, dividing themselves between coordinating various resources across different projects, and the actual IT work. Outsourcing to the cloud is an additional support but CIOs would generally like to have more in-house expertise rather than depend on partners to control supplier products.

“Attracting and retaining talent is a problem, so things are outsourced,” says the CIO of a small healthcare company with an IT team of three. “You offload the responsibility and free up internal resources at the risk of losing know-how in the company. But at the moment, we have no other choice. We can’t offer the salaries of a large private group, and IT talent changes jobs every two years, so keeping people motivated is difficult. We hire a candidate, go through the training, and see them grow only to see them leave. But our sector is highly specialized and the necessary skills are rare.”

The sirens of the market are tempting for those with the skills to command premium positioning, and the private sector is able to attract talent more easily than public due to its hiring flexibility and career paths.

“The public sector offers the opportunity to research, explore and deepen issues that private companies often don’t invest in because they don’t see the profit,” says Colasanti. “The public has the good of the community as its mission and can afford long-term investments.”

Training builds resource retention

To meet demand, CIOs are prioritizing hiring new IT profiles and training their teams, according to the Cegos international barometer. Offering reskilling and upskilling are effective ways to overcome the pitfalls of talent acquisition and retention.

“The market is competitive, so retaining talent requires barriers to exit,” says Emanuela Pignataro, head of business transformation and execution at Cegos Italia. “If an employer creates a stimulating and rewarding environment with sufficient benefits, people are less likely to seek other opportunities or get caught up in the competition. Many feel they’re burdened with too many tasks they can’t cope with on their own, and these are people with the most valuable skills, but who often work without much support. So if the company spends on training or onboarding new people who support these people, they create reassurance, which generates loyalty.”

In fact, Colasanti is a staunch supporter of life-long learning, and the experience that brings balance and management skills. But she doesn’t have a large budget for IT training, yet solutions in response to certain requests are within reach.

“In these cases, I want serious commitment,” she says. “The institution invests and the course must give a result. A higher budget would be useful, of course, especially for an ever-evolving subject like cybersecurity.”

The need for leadership

CIOs also recognize the importance of following people closely, empowering them, and giving them a precise and relevant role that enhances motivation. It’s also essential to collaborate with the HR function to develop tools for welfare and well-being.

According to the Gi Group study, the factors that IT candidates in Italy consider a priority when choosing an employer are, in descending order, salary, a hybrid job offer, work-life balance, the possibility of covering roles that don’t involve high stress levels, and opportunities for career advancement and professional growth.

But there’s another aspect that helps solve the age-old issue of talent management. CIOs need to recognize more of the role of their leadership. At the moment, Italian IT directors place it at the bottom of their key qualities. In the Cegos study, technical expertise, strategic vision, and ability to innovate come first, while leadership came a distant second. But the leadership of the CIO is a founding basis, even when there’s disagreement with choices.

“I believe in physical presence in the workplace,” says Colasanti. “Istat has a long tradition of applying teleworking and implementing smart working, which everyone can access if they wish. Personally, I prefer to be in the office, but I respect the need to reconcile private life and work, and I have no objection to agile working. I’m on site every day, though. My colleagues know I’m here.”

기술 부채가 IT 조직을 마비시킬 위협 요인으로 떠오르자 상당수 CIO가 레거시 소프트웨어와 시스템 유지보수·업그레이드를 위해 서드파티 서비스 업체에 눈을 돌리고 있다. 매니지드 서비스 업체 엔소노(Ensono)가 실시한 설문조사 결과, IT 리더 100명 가운데 95명이 레거시 IT를 현대화하고 기술 부채를 줄이기 위해 외부 서비스 업체를 활용하고 있는 것으로 나타났다.

이 같은 움직임은 부분적으로 레거시 IT 비용 증가에서 비롯됐다. 응답자 가운데 거의 절반은 지난해 노후 IT 시스템 유지보수에 예산보다 더 많은 비용을 지출했다고 답했다. 더 큰 문제는 레거시 애플리케이션과 인프라가 IT 조직의 발목을 잡고 있다는 점이다. IT 리더 10명 가운데 9명은 레거시 유지보수가 AI 현대화 계획에 걸림돌이 되고 있다고 지적했다.

엔소노의 CTO 팀 베어먼은 “레거시 시스템 유지보수가 현대화 노력에 큰 방해가 되고 있다”라며, “전형적인 혁신가의 딜레마다. 혁신보다는 노후 시스템과 그 해결 방안에만 집중하고 있다”라고 지적했다.

일부 CIO는 레거시 시스템 운영을 서비스 업체에 맡기거나 외부 IT팀을 활용해 기술 부채를 정리하고 소프트웨어와 시스템을 현대화하고 있다. 베어먼은 레거시 시스템을 외부에 맡기는 기업이 증가하는 배경으로 고령화된 인력을 꼽았다. 기업 내부의 레거시 시스템 전문가가 은퇴하면서 축적된 지식도 함께 빠져나가고 있다는 의미다.

베어먼은 “이 일을 내부에서 직접 처리할 수 있는 인력이 많지 않다. 조직 내 인력이 고령화되고 퇴직자가 늘어나는 상황에서, 필요한 인재를 채용하기 어려운 영역에서는 외부에서 전문 인력을 찾아야 한다”라고 설명했다. 또, “MSP 모델 자체는 수십 년 전부터 존재했지만, 최근에는 예산을 확보하고 AI를 도입할 시간을 만들기 위해 MSP를 기술 부채 관리 수단으로 활용하는 흐름이 커지고 있다”라고 분석했다.

AI처럼 새로운 기술이 빠르게 확산되는 것도 이런 흐름에 일조하고 있다. 베어먼은 “한쪽에는 관리·유지해야 하는 레거시 문제가 있고, 다른 한쪽에는 수년 동안 경험하지 못한 속도로 발전하는 최신 기술이 있어 양쪽을 동시에 따라가기 어렵다”라고 덧붙였다.

위험의 아웃소싱

사이버 보안 서비스 업체 뉴빅(Neuvik)의 CEO 라이언 레이르빅은 레거시 IT 관리를 서비스 업체에 맡기는 흐름이 확대되고 있다는 점에 동의했다. 레이르빅은 레거시 시스템에 적합한 전문가를 매칭하는 등 여러 장점을 언급하면서도, CIO가 위험 관리를 위해 MSP를 활용하는 경향도 있다고 지적했다.

레이르빅은 “많은 장점 가운데 자주 언급되지 않는 핵심은 취약점 악용이나 서비스 중단 위험을 서비스 업체에 맡길 수 있다는 점”이라며, “취약점 발견과 패치, 전반적인 유지보수에 지속적으로 많은 비용이 드는 환경에서는 잘못 대응했을 때 발생하는 위험을 서비스 업체가 떠안게 되는 경우가 많다”라고 설명했다.

미 국방부(US Department of Defense)에서 비서실장 겸 사이버 부문 부국장을 지낸 레이르빅은 레거시 IT 유지보수 예산을 초과 집행한 IT 책임자가 많다는 것이 놀랄 일은 아니라고 말한다. 많은 조직이 현재 보유한 IT 인프라와 앞으로 전환해야 할 인프라 사이에서 필요한 인재 역량이 맞지 않는 상황에 놓여 있다고 지적하며, 레거시 소프트웨어와 시스템의 지속적인 유지보수 비용이 예상보다 더 많이 드는 경우도 잦다고 말했다.

레이르빅은 “초기 도입 비용이 1이라면, 유지보수 비용은 1X이기 때문에 예상하지 못한 거대한 유지보수 꼬리가 생긴다”라고 덧붙였다.

레거시 유지보수의 덫에서 벗어나려면 적절한 서드파티 업체를 고르는 선견지명과 선택 기준이 필요하다. 레이르빅은 “장기적인 관점에서 해당 업체와 향후 5년 계획이 맞물리는지 반드시 확인해야 한다. 또 조직의 목표와 업체가 제공하려는 지원 방향이 일치하는지도 점검해야 한다”라고 조언했다.

두 번 지불하는 비용

일부 IT 리더가 레거시 시스템 현대화를 서드파티 업체에 맡기고 있지만, IT 서비스 관리 및 고객 서비스 소프트웨어 업체 프레시웍스(Freshworks)가 최근 공개한 보고서는 이런 현대화 노력이 과연 효율적인지에 의문을 제기했다.

프레시웍스의 조사에서 응답자의 3/4 이상은 소프트웨어 도입에 예상보다 더 많은 시간이 걸린다고 답했고, 프로젝트 가운데 2/3은 예산을 초과했다고 응답했다. 프레시웍스의 CIO 아슈윈 발랄은 서드파티 서비스 업체가 이 문제를 해결해 주지 못할 수도 있다고 경고했다.

발랄은 “레거시 시스템이 너무 복잡해지면서 기업이 도움을 구하려고 서드파티 업체와 컨설턴트에 점점 더 의존하고 있지만, 실제로는 수준 이하의 레거시 시스템을 다른 수준 이하 레거시 시스템으로 바꾸는 결과에 그치는 경우가 많다”라며, “서드파티 업체와 컨설턴트를 추가하면 기존 문제를 해결하기보다는 새로운 복잡성만 더해 문제를 악화시키는 사례도 적지 않다”라고 지적했다.

해법은 서드파티 업체를 늘리는 것이 아니라 별도의 복잡한 작업 없이 바로 쓸 수 있는 새로운 기술을 도입하는 데 있다. 발랄은 “이론적으로 서드파티 업체는 전문성과 속도를 제공한다. 하지만 현실에서는 복잡한 기술을 도입하는 데 한 번, 해당 기술이 제대로 작동하도록 컨설턴트를 투입하는 데 또 한 번 등 두 번 비용을 지불하는 경우가 많다”라고 꼬집었다.

피하기 어려운 서드파티 업체 활용

사이버 보안 솔루션 업체 워치가드 테크놀로지스(WatchGuard Technologies)의 필드 CTO 겸 CISO 애덤 윈스턴은 상당수 IT 리더가 일정 수준의 서드파티 지원을 사실상 피할 수 없는 선택으로 보고 있다. 윈스턴은 오래된 코드를 업데이트하거나 워크로드를 클라우드로 이전하고 SaaS 도구를 도입하고, 사이버보안을 강화하는 등 대부분의 과제에서 이제 외부 지원이 필요하다고 말했다.

윈스턴은 노후 원격접속 도구와 VPN을 포함한 레거시 시스템이 쌓이면 기술 부채가 눈덩이처럼 불어나 조직을 짓누를 수 있다고 경고했다. 또, 아직 많은 조직이 클라우드나 SaaS 도구로 완전히 현대화하지 못한 상태이며, 전환 시점이 오면 외부 업체에 도움을 요청할 수밖에 없을 것이라고 내다봤다.

윈스턴은 “대부분 기업은 자체 애플리케이션을 설계·개발·운영하지 않고, 그런 영역에 기술 부채가 쌓여 있는 상황에서 하이브리드 IT 구조를 유지하고 있다”라며, “여전히 코로케이션과 온프레미스 중심이던 시절의 환경을 유지하는 기업도 많고, 이런 환경에는 거의 예외 없이 레거시 서버와 레거시 네트워크, 현대적인 설계나 아키텍처를 따르지 않는 레거시 시스템이 포함돼 있다”라고 설명했다.

이런 기업의 IT 리더는 노후 기술을 단계적으로 퇴역시키는 계획을 세우고, IT 투자가 가능한 한 최신 상태를 유지하도록 솔루션 업체의 책임을 명확히 하는 서비스 계약을 체결해야 한다. 윈스턴은 많은 솔루션 업체가 신제품을 내놓으면서 기존 제품 지원을 너무 쉽게 중단한다고 지적했다.

윈스턴은 “업그레이드를 하지 않을 계획이라면 레거시 지원 비용을 면밀히 따져 보고, 업그레이드할 수 없다면 어떻게 격리할 것인지에 대한 답을 준비해야 한다”라며, “업그레이드가 불가능할 경우 위험을 옮기기 위한 이른바 ‘무덤 격리 전략(graveyard segmentation strategy)’을 어떻게 설계할지도 고민해야 한다”라고 강조했다. 또 “솔루션 업체 실사 과정에서 이런 논의가 빠지는 경우가 많고, 그러다 문제가 터지면 조직이 뒤늦게 놀라게 된다”라고 덧붙였다.

그렇다고 CIO가 레거시 IT 전문성을 쌓는 방향으로 커리어를 설계하는 것은 피해야 한다. 윈스턴은 “소프트웨어나 구축 비용을 충분히 상각하지 못했다면, 앞으로 도입하는 모든 신규 애플리케이션에는 최신 컴포넌트를 사용하겠다고 스스로 다짐해야 한다”라고 강조했다.
dl-ciokorea@foundryco.com

Talk to CEOs today and some common themes emerge: they’re moving faster, making bigger bets and relying more heavily on technology to execute their strategic agenda. Expectations on the IT estate have never been higher, yet many CEOs feel it’s a “black box” – essential, but difficult to see into and even harder to gauge.

At the same time, CIOs know that aging infrastructure is struggling to keep pace with AI-driven transformation, rising cyber risks or the agility their CEO has come to expect.  

This expectation gap is exactly why Netskope’s Crucial Conversations research identifies the IT estate as one of the six essential discussions CIOs must master today if they are to successfully align with their CEO on their modernization agenda. 

CEOs’ growing frustration with the “black box”

CEOs that took part in the research admitted they don’t understand what’s happening deep inside the IT stack and that makes them uncomfortable. Some feel their CIO shields them from the complexity; others feel the CIO overcomplicates it. Either way, this impacts confidence. 

Why the IT estate has become a strategic conversation

Three forces are pushing the IT estate onto the CEO agenda faster than many CIOs expected:

1. AI demands modern foundations

Organizations are moving from AI experiments to AI integration at pace. But AI doesn’t run effectively on infrastructure designed for a pre-AI world. CEOs need to understand that modernization is not a technology preference – it’s a prerequisite for delivering the business outcomes they now expect from AI.

2. The cost/risk trade-off is shifting

CEOs expect CIOs to be “gatekeepers” of cost, challenging suppliers and avoiding unnecessary spending. But they also expect CIOs to be candid about the real cost of doing nothing – outages, slowdowns, security exposure and innovation bottlenecks that compound, year after year.

3. The estate has moved from technical debt to strategic debt

Aging infrastructure no longer just slows down IT; it slows down the business. It limits agility, restricts transformation, and reduces competitiveness. CEOs may not use the words “technical debt,” but they understand when the organization is weighed down by the past.

How CIOs should reframe the conversation

To build trust and alignment, CIOs need to take ownership of this conversation rather than waiting for disruption to force it, and CEOs want three things from them. 

They want issues surfaced early and directly, with no surprises. CIOs need to lead with transparency.

Second is proactivity and the confidence to embrace change, make bold strategic calls, and recognize that even small fixes can have outsized impact, especially in an AI-driven environment.

And third is practicality. CEOs aren’t interested in “new toys,” but in well-evidenced, sensible solutions that reduce risk and address problems decisively when they arise. 

Above all, they want CIOs to think long term, planning infrastructure over the next decade rather than the next budget cycle and moving beyond an “if it isn’t broken” mindset. 

The moment for this conversation is now

Most enterprises are at an inflection point. Modernize the estate to unlock AI-driven advantage or carry forward a legacy footprint that cannot support the ambitions the CEO now expects the CIO to deliver. The CIO who leads this conversation will be seen as a true strategic partner.  

Explore all six crucial conversations

The IT estate is only one of six crucial conversations CIOs need to master with their CEO. To dive deeper into the rest – cost, risk, innovation, people and measurement – read the full Crucial Conversations report now. 

So your newly promoted CTO walks into their first executive meeting, armed with deep technical expertise and genuine enthusiasm for transformation. Six months later, they’re frustrated, your digital initiatives have stalled and your board is questioning the technology leadership strategy.

This isn’t a story about hiring the wrong person. It’s a story about building the wrong environment.

Here’s the truth your consultants won’t share: When technical leaders fail, it’s rarely a failure of intelligence. It’s a failure of integration.

Charles Sims notes this in his analysis of C-suite dynamics, “If you’re seated in the ‘big chair,’ you can’t expect people to intuit where they need to go. You need to build the bridge.”

The organizations winning the transformation race aren’t just hiring better CTOs; they’re creating fundamentally different conditions for technology leadership to thrive.

The hidden architecture of failure

Before we dive into solutions, let’s diagnose what’s actually broken.

The problem isn’t individual competence, it’s institutional design.

Most C-suite structures were established when technology was viewed as a cost center, rather than a competitive weapon. The processes, meeting rhythms and decision-making frameworks assume technology comes after strategy, not during it.

This creates what I call the integration gap, the space between where technology leaders sit and where they need to be to drive real transformation.

Deloitte research on resilient technology functions reveals a telling insight: High-performing “tech vanguard” businesses fundamentally differ in how they structure technology leadership.

As Khalid Kark and Anh Nguyen Phillips point out, these organizations embrace “joint accountability” and “establish sensing mechanisms that help anticipate business change.”

Translation: They don’t just include technology in business strategy, they integrate it.

The strategic exclusion problem

Here’s the most expensive mistake organizations make: bringing technology leaders into strategy validation, not strategy formation.

I’ve watched this pattern across dozens of transformations. The business leadership team spends months crafting the digital strategy. They debate market positioning, customer experience and competitive responses. Then, in the final act, they bring in the CTO to confirm technical feasibility.

This isn’t collaboration, it’s a recipe for execution failure.

CIO advisor Isaac Sacolick sums it up nicely, “What the risk here for CIOs is to get something out there on paper and start communicating. Letting your business partners know that you’re going to be the center point of putting a strategy together.

“Being able to do blue sky planning with business leaders, with technologists and data scientists on a very frequent basis to say, ‘is our strategy aligned or do we need a pivot’ or do we need to add I think that’s really the goal for a CIO now is to continually do that over the course of how this technology is changing.”

When technologists inherit fully formed strategies, they inherit the constraints, assumptions and blind spots of non-technical decision-making. The result? Strategies that sound compelling in PowerPoint but break down in reality.

The integration solution: As Sims emphasizes, successful businesses bring technology leaders in “when the goals are still being shaped.” Technology leaders become co-architects of strategy, not just implementers of it.

The translation challenge

Every business talks about wanting CTOs who can “translate technical complexity into business value.”

But most create conditions that make effective translation impossible.

The problem isn’t that technology leaders can’t communicate. It’s that business leaders structure every interaction to discourage strategic thinking. Fifteen-minute slots for infrastructure decisions. “High-level only” constraints on technical briefings. Interruptions when discussions get into architectural details.

Sims captures the real need perfectly: “Ask them to explain how tech can enable outcomes, not just avoid outages.” But enabling outcomes requires time, context and genuine dialogue — not rapid-fire status updates.

The integration solution: Create forums for substantive technical dialogue. Allocate time for technology leaders to educate business counterparts on possibilities, constraints and trade-offs.

The four pillars of technology leadership integration

The rebel leaders I’ve studied don’t just talk about integration, they systematically engineer it. Here are the four pillars that separate transformation winners from digital theater performers.

Pillar one: Strategic co-creation

Instead of: Bringing technology leaders in for feasibility validation.

Rebels: Include them in strategic formation from day one.

The breakthrough insight is simple: Technology constraints and possibilities should shape strategy, not just constrain it. When technologists participate in strategic formation, they help identify opportunities that pure business thinking might miss.

Actionable implementation:

• Include your CTO in quarterly business reviews, not just technology reviews
• Require technology input before major strategic initiatives get funded
• Create joint business-technology planning sessions for all transformation efforts
• Give technology leaders access to the same market intelligence and customer feedback as other executives

Pillar two: Outcome-driven accountability

Instead of: Asking for deliverables and timelines.

Rebels: Define success in business outcomes and measure accordingly.

This shift eliminates the translation problem entirely. When success is defined in business terms from the beginning, technology leaders naturally think about impact, not just implementation.

The Deloitte study talks about “value-based investments” aligned with “iterative Agile sprints.” But the real innovation isn’t methodological, it’s definitional. Success gets measured by business value delivered, not features completed.

Actionable implementation:

• Replace project status meetings with outcome review sessions
• Tie technology leader compensation to business metrics, not just technical ones
• Create shared dashboards that track business impact of technology initiatives
• Require business case updates, not just project updates

Pillar three: Information symmetry

Instead of: Functional hierarchy with information silos.

Rebels: Ensure technology leaders have the same strategic context as business leaders.

Sims makes a crucial point: “Technology touches every department. The org chart should reflect that.” But organizational design goes beyond reporting structures; it’s about information flow and decision rights.

The Deloitte research highlights the need for “sensing mechanisms that help anticipate business change.” But sensing requires access to information, not just responsibility for reaction.

Actionable implementation:

• Include technology leaders in customer advisory boards and market research reviews
• Share competitive intelligence and industry analysis with the entire C-suite, not just business functions
• Create cross-functional intelligence-sharing sessions where every leader contributes market insights
• Ensure technology leaders participate in customer meetings and strategic partnerships

Pillar four: Translation excellence

Instead of: Expecting natural translation ability.

Rebels: Systematically develop two-way translation competence.

Here’s where most organizations get it backwards. They expect CTOs to be great translators but provide no development, feedback or support for this critical skill.

As Sims notes, “The best CTOs turn complexity into clarity. They make everyone around them smarter. That’s the leadership skill we should be measuring.”

But translation is a two-way street. Business leaders also need to develop competence in asking strategic questions that unlock technological insight.

Actionable implementation:

• Create monthly translation labs where technology leaders practice explaining complex concepts to different audiences
• Train business leaders to ask better questions: “What are the trade-offs?” instead of “Is this feasible?”
• Establish technology education sessions for non-technical executives
• Reward and recognize technology leaders who effectively educate their peers

Better leadership means faster business

When you get technology leadership integration right, the impact extends far beyond individual performance. You create what the Deloitte research calls enterprise agility: the ability to “nimbly strategize and operate” in response to constant change.

The data reveals so much: businesses with integrated technology leadership outperform peers across every meaningful metric. Revenue growth, profit margins, customer satisfaction, employee engagement and market share all improve when business and technology leadership truly collaborate.

But the most significant impact might be speed. Integrated organizations move faster because they eliminate the handoff delays, translation loops and rework cycles that plague siloed structures.

The competitive reality

While you’re optimizing technology leadership integration, your competitors are making a choice. Some will continue the old patterns: hiring smart technologists, giving them business requirements and wondering why transformation is hard.

Others will join the integration revolution. They’ll create conditions where technology leaders thrive. They’ll build strategic collaboration into their organizational DNA. They’ll accelerate past competitors while others struggle with digital theater.

The study reveals that tech vanguard organizations are already pulling away from baseline performers. The gap isn’t just technical: it’s structural, cultural and strategic.

Ready to ramp up?

The path forward isn’t about your next technology hire, it’s about the environment you create for technology leadership to succeed.

Week one: Audit your current integration points. Where does your CTO participate in strategic decision-making? Where are they excluded? Map the information flows and decision rights.

Month one: Redesign your leadership meeting rhythms. Include technology leaders in strategic formation, not just implementation planning. Create forums for substantive business-technology dialogue.

Month two: Implement outcome-based accountability. Replace deliverable tracking with business impact measurement. Align technology leader success metrics with business results.

Month three: Launch translation competence development. Create systematic programs for both business-to-technology and technology-to-business communication improvement.

Month six: Measure integration velocity. How quickly do business insights flow into technology decisions? How rapidly do technological possibilities inform business strategy?

The businesses that systematically build technology leadership integration won’t just transform their trajectory; they’ll transform their markets. They’ll set the pace while competitors struggle to keep up.

The choice is yours: Continue with traditional technology leadership models or build the integration capabilities that drive real transformation.

The rebels are already deciding. What about you?

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In my experience leading ERP modernization projects and collaborating with IT and business executives, I’ve learned that technology alone rarely determines success, but mindset and architecture do. Gartner reports, “By 2027, more than 70% of recently implemented ERP initiatives will fail to fully meet their original business case goals.” ERP success now requires a fundamentally different architecture.

For decades, ERP systems have been the core of enterprise operations: managing finance, supply chain, manufacturing, HR and more. The same systems that once promised control and integration are now stifling flexibility, slowing innovation and piling up technical debt.

From what I’ve observed across multiple ERP programs, the problem isn’t ERP itself, but rather, it’s how we’ve come to think about it. Many organizations still treat ERP purely as a system of record, missing the broader opportunity in front of them.

The next era of business agility will be defined by ERP as a composable platform: modular, data-centric, cloud-native and powered by AI. In many of the organizations I’ve worked with, technology leaders aren’t debating whether to modernize the core. Instead, they’re focused on how to do it without stalling the business.

Forbes captures the shift succinctly: “it is anticipated that 75% of global businesses will begin replacing traditional monolithic ERP systems with modular solutions — driven by the need for enhanced flexibility and scalability in business operations.” This highlights ERP’s evolution from monolithic legacy suites to an adaptive, innovation-driven platform.

Those who embrace this shift will make ERP an enabler of innovation. Those who don’t will watch their core systems become their biggest bottleneck and stay held back.

From monoliths to modular backbones

In the 1990s and 2000s, ERP meant one vendor, one codebase and one massive implementation project touching every corner of the business. Companies spent millions customizing software to fit every process nuance.

I saw the next chapter unfold with the cloud era. Companies such as SAP, Oracle, Microsoft and Infor transitioned their portfolios to SaaS, while a wave of startups emerged with modular, industry-focused ERP platforms. APIs and services finally promised a system that could evolve with the business.

In one transformation I supported, our biggest turning point came when we stopped treating ERP as a single implementation. We began decomposing capabilities into modules that business teams could own and evolve independently.

But for many enterprises, that promise never fully materialized. The issue isn’t the technology anymore, but the mindset. In many organizations, ERP is still viewed as a finished installation rather than a living platform meant to grow and adapt.

The cost of the old mindset

Legacy ERP thinking simply can’t keep up with today’s pace of change. The result is slower innovation, fragmented data and IT teams locked in perpetual catch-up mode. Organizations need architectures that change as fast as the business does.

LeanIX, citing Gartner research, highlights the advantage: “Organizations that have adopted a composable approach to IT are 80% faster in new-feature implementation, particularly when using what Gartner defines as composable ERP platforms,” demonstrating the performance gap between modular ERP and traditional monolithic systems.

I’ve seen legacy ERP thinking carry a high price tag in real projects:

• Inflexibility: Business models evolve faster than software cycles. Traditional ERP can’t keep up.
• Over-customization: Years of bespoke code make upgrades risky and expensive.
• Data fragmentation: Multiple ERP instances and disconnected modules create inconsistent data and unreliable analytics.
• User frustration: Outdated interfaces drive workarounds and disengagement.
• High total cost of ownership: Maintenance and upgrades consume budgets that should fund innovation.

Enter the composable ERP

The emerging composable ERP model breaks this monolith apart. Gartner defines it as an architecture where enterprise applications are assembled from modular building blocks, connected through APIs and unified by a data fabric.

As LeanIX explains, “Composable ERP, built on modular and interoperable components, allows organizations to respond faster to change by assembling capabilities as needed rather than relying on a rigid, monolithic suite,” illustrating the transition from static ERP systems to a dynamic, adaptable business platform.

Having worked on both sides — custom development and packaged ERP — I’ve learned that the real power of composability lies in how easily teams can assemble, not just integrate, capabilities. Rather than seeing ERP as a single suite, think of it as the system that enables how an enterprise operates. The core processes — finance, supply chain, manufacturing, HR — are what make up the base. Modular features such as AI forecasting, customer analytics and sustainability tracking can plug in dynamically as the business evolves.

This approach enables organizations to:

• Mix and match modules from different vendors or in-house teams.
• Integrate best-of-breed cloud apps through standard APIs instead of brittle custom code.
• Leverage AI for automation, insights and predictive decisions.
• Deliver persona-based experiences tailored to each user’s role.

Personas: The human face of composable ERP

Traditional ERP treated every user the same, in which there would be one interface, hundreds of menus, endless forms. Composable ERP flips that script with persona-based design, built around what each role needs to accomplish.

• CFOs see real-time financial health across entities with AI-driven scenario modeling.
• Supply chain leaders monitor live demand signals, supplier performance and sustainability metrics.
• Plant managers track IoT-enabled equipment, predictive maintenance and production KPIs.
• Sales and service teams access operational data seamlessly without switching systems.

From my experience, when ERP is designed around real personas rather than generic transactions, adoption rises and decisions happen faster.

Challenges and pitfalls

These are not theoretical issues; they’re the practical challenges I see IT and business teams grappling with every day.

• Data governance: Without a unified data strategy, modularity turns to chaos.
• Integration complexity: APIs require discipline for versioning, authentication, semantic alignment.
• Vendor lock-in: Even open platforms can create subtle dependencies.
• Change management: Employees need support and training to unlearn old habits.
• Security: A more connected system means a larger attack surface. Zero-trust security is essential.

True success demands leadership that balances technical depth with organizational empathy.

The CIO’s new playbook

Through years of ERP work and collaboration between business and IT teams, I’ve realized that the biggest hurdle to ERP success is the belief that ERP is a fixed system instead of a constantly evolving platform for innovation.

This shift isn’t about tools, but rather it’s about redefining the ERP’s role in the business. McKinsey reinforces this reality, stating, “Modernizing the ERP core is not just a technology upgrade — it is a business transformation that enables new capabilities across the enterprise.” It’s a shift that calls for a fundamentally different playbook, especially for CIOs leading modernization efforts.

1. Start with the business architecture, not the software. Define how you want your enterprise to operate, then design ERP capabilities to fit.
2. Build a unified data fabric. A composable ERP lives or dies by consistent, high-quality data.
3. Adopt modular thinking incrementally. Start small by piloting a few modules, prove the value, then scale.
4. Empower fusion teams. Blend IT, operations and business experts into agile squads that compose solutions quickly.
5. Measure success by outcomes, not go-lives. The goal is agility and resilience and not a single launch date.
6. Push vendors for openness. Demand published APIs and true interoperability, not proprietary cloud labels.

Oracle reinforces this imperative: “Companies need to move toward a portfolio that is more adaptable to business change, with composable applications that can be assembled, reassembled and extended,” highlighting flexibility as a core selection criterion.

Reframe ERP as an innovation platform. Encourage experimentation with low-code workflows, analytics and AI copilots.

Looking ahead: When ERP becomes invisible

In a few years, we might not even use the term ERP. Like CRM’s evolution into customer experience platforms, ERP will fade into the background, becoming the invisible digital backbone of the enterprise.

I’ve watched ERP evolve from on-premises to cloud to AI-driven platforms. AI will soon handle transactions and workflows behind the scenes, while employees interact through conversational interfaces and embedded analytics. Instead of logging into systems, they’ll simply request outcomes — and the composable ERP fabric will dynamically orchestrate everything required to deliver them.

That future belongs to organizations rethinking ERP today. This isn’t just another upgrade cycle — it’s a redefinition of how enterprises operate.

From record-keeping to value creation

ERP was once about efficiency — tracking inventory, closing books, enforcing process discipline. Today, it’s about resilience and innovation. From my own journey across multiple ERP programs, I’ve seen that the CIO’s challenge isn’t just keeping systems running, but also architecting agility into how the enterprise operates.

Composable ERP, which is built on cloud, AI and human-centered design, is the blueprint. It turns ERP from a system of record into a system of innovation that evolves as fast as the market around it.

The opportunity is clear: Lead the transformation or risk maintaining yesterday’s architecture while others design tomorrow’s enterprise.

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As tech debt threatens to cripple many IT organizations, a huge number of CIOs have turned to third-party service providers to maintain or upgrade legacy software and systems, according to a new survey.

A full 95% of IT leaders are now using outside service providers to modernize legacy IT and reduce tech debt, according to a survey by MSP Ensono.

The push is in part due to the cost of legacy IT, with nearly half of those surveyed saying they paid more in the past year to maintain older IT systems than they had budgeted. More importantly, dealing with legacy applications and infrastructure is holding IT organizations back, as nearly nine in 10 IT leaders say legacy maintenance has hampered their AI modernization plans.

“Maintaining legacy systems is really slowing down modernization efforts,” says Tim Beerman, Ensono’s CTO. “It’s the typical innovator’s dilemma — they’re focusing on outdated systems and how to address them.”

In some cases, CIOs have turned to service providers to manage legacy systems, but in other cases, they have looked to outside IT teams to retire tech debt and modernize software and systems, Beerman says. One reason they’re turning to outside service providers is an aging employee base, with internal experts in legacy systems retiring and taking their knowledge with them, he adds.

“Not very many people are able to do it themselves,” Beerman says. “You have maturing workforces and people moving out of the workforce, and you need to go find expertise in areas where you can’t hire that talent.”

While the MSP model has been around for decades, the move to using it to manage tech debt appears to be a growing trend as organizations look to clear up budget and find time to deploy AI, he adds.

“If you look at the advent of lot of new technology, especially AI, that’s moving much faster, and clients are looking for help,” Beerman says. “On one side, you have this legacy problem that they need to manage and maintain, and then you have technology moving at a pace that it hasn’t moved in years.”

Outsourcing risk

Ryan Leirvik, CEO at cybersecurity services firm Neuvik, also sees a trend toward using service providers to manage legacy IT. He sees several advantages, including matching the right experts to legacy systems, but CIOs may also use MSPs to manage their risk, he says.

“Of the many advantages, one primary advantage often not mentioned is shifting the exploitation or service interruption risk to the vendor,” he adds. “In an environment where vulnerability discovery, patching, and overall maintenance is an ongoing and expensive effort, the risk of getting it wrong typically sits with the vendor in charge.”

The number of IT leaders in the survey who overspent their legacy IT maintenance budgets also doesn’t surprise Leirvik, a former chief of staff and associate director of cyber at the US Department of Defense.

Many organizations have a talent mismatch between the IT infrastructure they have and the one they need to move to, he says. In addition, the ongoing maintenance of legacy software and systems often costs more than anticipated, he adds.

“There’s this huge maintenance tail that we weren’t expecting because the initial price point was one cost and the maintenance is 1X,” Leirvik says.

To get out of the legacy maintenance trap, IT leaders need foresight and discipline to choose the right third-party provider, he adds. “Take the long-term view — make sure the five-year plan lines up with this particular vendor,” he says. “Do your goals as an organization match up with where they’re going to help you out?”

Paying twice

While some IT leaders have turned to third-party vendors to update legacy systems, a recently released report from ITSM and customer-service software vendor Freshworks raises questions about the efficiency of modernization efforts.

More than three-quarters of those surveyed by Freshworks say software implementations take longer than expected, with two-thirds of those projects exceeding expected budgets.

Third-party providers may not solve the problems, says Ashwin Ballal, Freshworks’ CIO.

“Legacy systems have become so complex that companies are increasingly turning to third-party vendors and consultants for help, but the problem is that, more often than not, organizations are trading one subpar legacy system for another,” he says. “Adding vendors and consultants often compounds the problem, bringing in new layers of complexity rather than resolving the old ones.”

The solution isn’t adding more vendors, but new technology that works out of the box, Ballal adds.

“In theory, third-party providers bring expertise and speed,” he says. “In practice, organizations often find themselves paying for things twice — once for complex technology, and then again for consultants to make it work.”

Third-party vendors unavoidable

Other IT leaders see some third-party support as nearly inevitable. Whether it’s updating old code, moving workloads to the cloud, adopting SaaS tools, or improving cybersecurity, most organizations now need outside assistance, says Adam Winston, field CTO and CISO at cybersecurity vendor WatchGuard Technologies.

A buildup of legacy systems, including outdated remote-access tools and VPNs, can crush organizations with tech debt, he adds. Many organizations haven’t yet fully modernized to the cloud or to SaaS tools, and they will turn to outside providers when the time comes, he says.

“Most companies don’t build and design and manage their own apps, and that’s where all that tech debt basically is sitting, and they are in some hybrid IT design,” he says. “They may be still sitting in an era dating back to co-location and on-premise, and that almost always includes legacy servers, legacy networks, legacy systems that aren’t really following a modern design or architecture.”

Winston advises IT leaders to create plans to retire outdated technology and to negotiate service contracts that lean on vendors to keep IT purchases as up to date as possible. Too many vendors are quick to drop support for older products when new ones come out, he suggests.

“If you’re not going to upgrade, do the math on that legacy support and say, ‘If we can’t upgrade that, how are we going to isolate it?’” he says. “‘What is our graveyard segmentation strategy to move the risk in the event that this can’t be upgraded?’ The vendor due diligence leaves a lot of this stuff on the table, and then people seem to get surprised.”

CIOs should avoid specializing in legacy IT, he adds. “If you can’t amortize the cost of the software or the build, promise yourself that every new application that’s coming into the system is going to use the latest component,” Winston says.

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.

엔터프라이즈 아키텍트 관련 기획 기사에서 생성형 AI가 언급된 적은 있지만, 기업 기술 전반에 미치는 영향은 지금까지 크게 드러나지 않았다. 그러나 지금은 주요 서비스형 소프트웨어(SaaS) 기업이 에이전틱 AI 솔루션을 잇달아 내놓으면서 아키텍처와 아키텍트 역할 자체가 변화하고 있다. 그렇다면 지금 CIO와 아키텍트는 무엇을 알아야 할까?

기업, 특히 CEO는 생산성을 높이고 성장세를 회복하기 위해 AI 도입이 필요하다고 꾸준히 목소리를 내왔고, 분석가들도 같은 의견을 전하고 있다. 예를 들어 가트너는 향후 5년 동안 IT 업무의 75%가 AI를 활용한 직원에 의해 수행될 것이라고 전망했다. 이는 새로운 시장 진출, 추가 제품·서비스 개발, 마진을 높일 기능 확충처럼 IT 업무가 새로운 가치를 만들어내도록 적극적으로 나서야 한다는 의미일 수 있다.

생산성이 이처럼 근본적으로 변화한다면, 기업에는 비즈니스 프로세스와 이를 운영하는 기술 전반에 대한 새로운 계획이 필요하다. 최근 사례들은 기업이 새로운 운영 모델을 도입하지 않으면 기술 투자 효과를 제대로 얻기 어렵다는 점을 보여주고 있다.

에이전틱 AI 도입은 기업의 프로세스뿐 아니라 소프트웨어 개발 방식, 맞춤화, 기술 구현 방식까지 모두 바꿀 가능성이 높다. 따라서 아키텍트는 소프트웨어가 어떻게 개발되고 조정되며 배포되는지 재설계하는 최전선에 서게 된다.

기술 업계 일부에서는 생성형 AI가 기업용 소프트웨어와 이를 제공하는 대형 벤더에 근본적 변화를 가져올 것으로 보고 있다. 그러나 포레스터(Forrester) 총괄 애널리스트 디에고 로 주디체는 “AI가 본격화된다고 해서 소프트웨어 산업이 붕괴된다는 주장은 터무니없다. 그런 결론을 내려면 AI에 가장 낙관적인 전문가의 예상조차 뛰어넘는 수준의 완전무결한 AI가 전제돼야 한다”라고 말했다. 로 주디체는 최근 열린 원 컨퍼런스에서 비즈니스 기술 리더 4,000명에게 “변화는 분명 진행되고 있지만, 이는 최근 축적된 성과를 기반으로 일어나는 것”이라고 설명했다.

로 주디체는 “애자일은 조직 간 조율을 개선했고, 데브옵스는 개발과 운영 사이의 벽을 허물었다. 이는 모두 목표가 같았다. 바로 아이디어와 구현 사이의 간극을 줄이는 것이다”라고 말했다. 그는 AI가 기업용 소프트웨어 개발 방식을 바꿀 것이라는 점을 부정하지는 않았지만, 애자일과 데브옵스가 그랬듯 AI도 소프트웨어 개발 생애주기를 개선하고 결국 아키텍처 전반을 고도화하게 될 것이라고 강조했다. 다른 점은 변화의 속도다. 콘텐츠 관리 소프트웨어 기업 엄브라코의 AI 스태프 엔지니어 필 휘태커는 “개발 역사상 이런 속도의 변화는 없었다”라고 진단했다.

복잡성 증가와 프로세스 변화

소프트웨어 개발 및 맞춤화 주기가 바뀌고 에이전틱 애플리케이션이 보편화되면서, 아키텍트는 복잡성과 새로운 비즈니스 프로세스를 염두에 둔 계획을 수립해야 하는 상황이다. 에이전틱 AI가 지금까지 직원이 수동으로 처리하던 업무를 맡게 된다면 기존 비즈니스 프로세스를 그대로 유지하기는 어렵다.

로 주디체는 아마존웹서비스(AWS) 같은 AI 선도 기업이 대규모 인력 감축에 나선 이후 과열된 논쟁에 다시 한번 의견을 전했다. 그는 원 컨퍼런스에서 “모든 직원이 자신의 일을 도와주는 봇 하나씩을 갖게 될 것이라는 생각은 단순한 발상이다”라며, “기업은 각 역할과 비즈니스 프로세스를 면밀히 분석해, 적절한 작업에 적절한 에이전트를 배치하는 데 예산과 자원을 쓰고 있는지 확인해야 한다. 이 과정을 거치지 않으면 필요하지 않은 곳에 에이전틱 기술을 도입해 복잡한 업무를 처리하지도 못하면서 기업의 클라우드 비용만 늘리는 결과를 초래하게 된다”라고 경고했다.

AI 기반 로우코드 플랫폼 기업 아웃시스템즈(OutSystems)의 CIO 티아고 아제베두는 “중요한 정보에 접근할 수 있는 에이전트를 만드는 일은 생각보다 쉽다”라고 말했다. 그는 “그래서 데이터 구분이 필요하다. 에이전트를 배포할 때는 이를 통제할 수 있어야 한다. 에이전트가 많아질수록 비용도 함께 늘어난다”라고 설명했다.

하지만 휘태커는 결정론적 방식과 비결정론적 방식 사이의 차이가 무엇보다 크다고 지적했다. 비결정론적 방식은 결과가 매번 달라질 수 있기 때문에, 항상 동일한 결과를 내는 결정론적 에이전트를 일종의 가드레일로 둬야 한다는 것이다. 그는 어떤 비즈니스 결과를 결정론적·비결정론적 방식 중 어디에 둘 것인지 정의하는 일이 아키텍처의 핵심 역할이라고 설명했다. 또한 휘태커는 여기서 AI가 조직의 빈틈을 메우는 데 도움을 줄 수 있다고 덧붙였다. 아키텍트로 일한 경험이 있는 휘태커는 기업이 AI를 적극 실험해 자사 아키텍처에 어떤 이점을 줄 수 있는지, 그리고 궁극적으로 비즈니스 성과에 어떤 영향을 미칠 수 있는지 확인하는 일이 매우 중요해질 것이라고 강조했다.

가트너 애널리스트 대릴 플러머와 알리시아 멀러리는 “실질적 경쟁력을 확보하는 길은 과장된 기대를 쫓거나 AI의 잠재력을 깎아내리는 데 있지 않다. 가치를 창출하는 중간지점을 찾는 데 있다”라고 밝혔다. 두 사람은 “AI의 가능성은 분명하지만, 그 가치를 온전히 실현할 가능성은 보장되지 않는다. 가트너 조사에 따르면 AI 프로젝트 가운데 ROI를 달성하는 경우는 5개 중 1개에 불과하고, 진정한 변화를 이끄는 사례는 50개 중 1개 수준에 그친다”라고 전했다. 또 다른 조사에서는 조직의 리더가 디지털 전환을 제대로 이끌 수 있다고 신뢰하는 직원이 32%에 불과하다는 결과도 나왔다. 이에 대해 아제베두는 “에이전트는 아키텍처 복잡성을 더해주기 때문에 아키텍트 역할이 더 중요해지고 있다”라고 분석했다.

과거 아키텍트는 주로 프레임워크 중심의 업무를 수행해왔다. 휘태커는 이제 직원, 애플리케이션, 데이터베이스, 에이전틱 AI가 얽혀 있는 엔터프라이즈 환경을 관리하려면 새로운 기술 모델을 이해하고 도입해야 한다고 설명했다. 그는 그중 하나로 MCP를 언급하면서, MCP가 AI 모델을 데이터 소스와 연결하는 표준 방식을 제공해, 지금처럼 각기 다른 방식으로 구성된 통합 구조나 검색 증강 생성(RAG) 구현의 복잡성을 줄여줄 수 있다고 언급했다. AI는 이러한 새로운 복잡성을 다루는 데도 도움을 제공할 전망이다. 로 주디체는 “기획, 요구사항 관리, 에픽 생성, 사용자 스토리 작성, 코드 생성, 코드 문서화, 번역까지 지원하는 다양한 도구가 등장하고 있다”라고 설명했다.

새로운 책임

포레스터(Forrester) 시니어 애널리스트 스테판 반레켐은 이제 에이전틱 AI가 주요 엔터프라이즈 아키텍처 도구의 핵심 기능으로 자리 잡고 있다고 설명한다. 그는 “에이전트는 데이터 검증, 역량 매핑, 아티팩트 생성 같은 작업을 자동화해 아키텍트가 전략과 전환 업무에 집중할 수 있게 한다”라고 말했다. 반레켐은 셀로니스, SAP 시그나비오, 서비스나우가 도입한 에이전틱 통합 기술을 사례로 들었다. 휘태커는 아키텍트가 조직을 보호하고 에이전틱 AI의 의사결정과 결과에 책임을 지는 역할로 더욱 중요해지고 있다고 진단했다.

일부 아키텍트는 이런 변화가 기존 전문 영역을 약화시킨다고 생각할 수도 있다. 그러나 휘태커는 오히려 역할의 범위를 넓힐 기회라고 봤다. 그는 “아키텍트는 여러 영역을 깊이 있게 파고들 수 있다. 사람을 한 가지 범주에 가둬두는 방식은 결코 바람직하지 않다”라고 말했다.

전통적으로 아키텍처는 무언가를 설계하고 구축한 뒤 고정된 형태로 존재하는 구조를 의미했다. 하지만 에이전틱 AI가 기업에 확산되면서 아키텍처를 관리하는 아키텍트의 역할은 더욱 유동적으로 변하고 있다. 이제는 설계 및 구축 감독뿐 아니라, 계획을 꾸준히 모니터링하고 조정하는 역할까지 요구된다. 이를 ‘오케스트레이션’이라고 부르기도 하며, 일종의 지도 읽기에 가깝다는 비유도 나온다. 아키텍트가 경로를 설계하더라도, 실제 환경의 다양한 변수로 인해 길이 달라질 수 있기 때문이다. 날씨 변화나 쓰러진 나무 때문에 길을 우회해야 하듯, 아키텍트 역시 비즈니스 환경이 바뀌면 계획을 수정하고 새로운 방향을 이끌어야 한다.

또한 새로운 아키텍트 역할 역시 기술 발전에 따라 계속 변하게 될 전망이다. 로 주디체는 조직의 자동화 수준이 더 높아질 것이라고 내다봤고, 아제베두는 조직 전반에 구축되는 에이전트를 묶어 카탈로그 형태로 관리하는 ‘오케스트레이션’ 관점에 힘을 실었다. 아키텍트와 CIO가 조직 전체의 조율자로 역할을 확장할 수 있는 기회라는 설명이다.

직함이 무엇이든, 아키텍처의 중요성은 그 어느 때보다 커지고 있다. 휘태커는 “AI가 더 많은 코드를 작성하게 될수록 아키텍트가 되는 사람도 더 늘어날 것”이라며 “앞으로는 눈앞에 있는 수많은 에이전트를 조율하고 통제하는 일이 아키텍트의 본래 역할이 될 것”이라고 말했다. 그는 기술 담당자가 점점 더 많은 개발 업무를 에이전트와 AI에 맡기게 되면서, 개별 에이전트와 프로세스가 어떻게 작동해야 하는지를 설계하는 책임이 더욱 확대되고 많은 기술 직원이 이 역할을 분담하게 될 것이라고 설명했다.

그는 “AI가 코드를 생성해줄 수는 있지만, 코드의 보안을 확인하는 책임은 여전히 사람에게 있다”라고 강조했다. 이에 따라 IT 조직은 코드를 직접 개발하는 팀에서, AI가 만든 기술을 점검·수용하고 이를 실제 비즈니스 프로세스에 배치하는 역할을 수행하는 아키텍처 중심 조직으로 변화하게 될 것이라고 전망했다.

이미 조직 내에 섀도우 AI가 깊숙이 스며든 상황에서, 휘태커는 기업이 도입한 AI 에이전트와 비즈니스가 조율하도록 지원하면서 동시에 고객 데이터와 사이버 보안 체계를 보호할 수 있는 아키텍트 팀의 필요성이 커지고 있다고 강조했다. AI 에이전트는 기업의 운영 구조를 다시 그려내고 있으며, 동시에 아키텍트 역할의 미래 또한 새롭게 정의하고 있다.
dl-ciokorea@foundryco.com

AI에 대한 기대와 과열된 관심이 그 어떤 기술 변화보다 커졌다고 평가된다. 이런 흐름에 맞춰 투자 규모도 덩달아 커졌다. 가트너는 2025년까지 전 세계 AI 투자 규모가 약 1조 5,000억 달러에 이를 것으로 추산했다. 거대한 투자 규모에도 불구하고 대부분 조직은 약속했던 가치와 실제로 실현된 가치 사이의 만성적인 격차에 여전히 시달리고 있다.

가장 널리 인용되는 근거는 올해 초 MIT 보고서에서 나왔다. 이 보고서는 생성형 AI 파일럿 프로젝트의 95%가 실패한다고 밝혔다. 맥킨지 조사에서도 거의 80%에 이르는 기업이 생성형 AI를 사용하고 있지만, 거의 같은 비율의 기업이 수익성에 의미 있는 효과를 얻지 못했다고 응답했다.

AI가 어느 정도 효과를 내고 있다는 근거도 있다. 2025년 시스코의 ‘AI 준비도 지표(AI Readiness Index)’에 따르면, 전체 기업의 13%는 AI에서 지속적으로 측정 가능한 수익을 얻고 있다고 답했다. 비록 소수이지만 선도 기업은 AI에서 가치를 끌어내기 시작했다는 의미다. 이런 가치의 출처는 점점 기술 자체가 아니라 리더십의 명확성, 전략적 정렬, 실행 역량에서 나오고 있다. 시스코의 AI 준비도 지표는 AI에서 가치를 실현한 기업의 99%가 변화 수용을 전제로 한 명확한 전략을 갖고 있고, 임직원이 새로운 기술에 익숙해지도록 지원하는 공식 프로그램을 운영하고 있다는 점을 확인했다.

오늘날 CEO와 CIO는 한 세대를 가르는 전환점에 서 있다. AI를 단순한 비용 절감 수단이 아니라 역량과 여력을 창출하고 혁신을 촉진하며 인간 중심의 결과를 이끄는 동력으로 재정의해야 하는 시기이다. 앞으로의 길은 업무를 잘게 쪼개고, 어디에 자동화를 적용해야 하는지 다시 평가하며, 인재가 성장과 변혁에 집중할 수 있도록 권한을 부여하는 리더십에 달려 있다.

성공을 위한 준비 : AI의 약속과 현실

많은 CIO에게 가장 큰 과제는 최고 경영진과 이사회가 “AI가 필요하다”는 사실은 알고 있지만, 정작 무엇을 위해 AI가 필요한지 명확하게 알지 못한다는 점이다. 이 때문에 AI가 모든 문제를 해결하고 생산성을 급등시켜 줄 만능 해결책이라는 비현실적인 기대가 생긴다. 처음부터 목표가 비현실적이면 프로젝트를 성공적으로 완료하고도 실패로 간주하는 상황이 발생한다. 여러 사업 부문이 사업 성과를 측정할 KPI를 갖추지 못한 점도 이런 문제를 부추긴다.

대표적인 예가 컨택센터다. 컨택센터에서는 상담사 코칭, 가상 상담사, 근무 스케줄링, 통화 스코어링, 콜 노트 작성 등 다양한 영역에 AI 에이전트를 활용할 수 있다. 하지만 이런 활동의 가치를 측정하기가 어렵기 때문에 많은 기업이 단순히 상담사 수를 줄여 비용 절감에 초점을 맞추는 방식으로 대응해 왔다. 이런 접근은 신중하게 실행하지 않으면 쉽게 역효과를 낸다. 예를 들어, 금융 서비스 회사 클라르나(Klarna)는 상담사 약 700명을 감축한 뒤 고객 서비스 점수가 급락하자 인력을 다시 채용해야 했다. 이 사례는 기술 문제가 아니라 경영진이 영향도를 제대로 파악하고 계획을 세우지 못한 리더십 문제였다.

문제 진단 : 기술적 한계인가, 리더십의 공백인가

AI와 자동화 기술에서 ROI를 얻지 못하는 문제를 진단하는 과정에서 실제 원인을 혼동하는 경우가 많다. 복잡한 데이터를 통합하고 자동화를 확장하는 기술적 준비가 부족한 것은 여전히 해결해야 할 과제이다. 하지만 실제로 개선 조치를 취해야 할 지점은 기술 그 자체가 아니라 기술 투자를 어떻게 지휘하고 이끌어 가는지에 더 가까운 경우가 많다. 이런 상황은 AI를 성공적으로 도입하는 데 방해가 되는 장애물이 코드나 클라우드 환경 설정이 아니라 이사회와 경영진에 더 가깝다는 사실을 시사한다. 많은 실패가 시장의 과열과 유행에 밀려 신속함을 우선하는 경영진의 선택에서 비롯되고, 진정한 비즈니스 변화를 고민하지 않은 채 추진하기 때문에 발생한다.

AI 프로젝트를 시작하기 전에 무엇을 측정하고, 어떤 지표로 투자수익률을 계산할지에 대해 비즈니스와 명확히 정렬하는 과정도 중요하다. 전략적 우선순위와 연계된 명확한 비즈니스 케이스 없이 고립된 기술 실험 형태로 출발한 프로젝트는 가치가 모호해 효과를 측정하기 어렵고, 예산 조정 시 우선적으로 잘려 나가기 쉽다.

리더십의 전환점 : 비용 절감을 넘어

비즈니스 세계는 리더십 전환점에 서 있다. 인력 혁신이 처음으로 ‘인간만의 문제’가 아닌 지점이다. AI와 자율 에이전트가 빠르게 확산되면서 경영진은 조직 안에서 가치를 어떻게 만들어내고 유지할지에 대해 훨씬 더 복잡한 결정을 내려야 한다. 이 변화는 비용을 줄이는 차원을 넘어, 인간의 역량과 AI 기술 역량 사이의 관계를 다시 설계하고, 조직 문화와 프로세스가 이런 하이브리드 인력 환경에 적응할 수 있도록 만드는 작업을 요구한다.

기업 인텔리전스 전문 업체 알포텐셜(r.Potential)의 CEO 그렉 슈메이커는 “많은 기업이 더 많은 AI 모델과 컴퓨트, 에이전트를 구축하는 데 경쟁하고 있지만, 엔터프라이즈 AI 도입의 실제 병목은 공급 측이 아니라 기업이 어디에 어떻게 AI를 활용해 실제 일을 처리할지 모른다는 점에 있다”라고 말했다. 슈메이커는 “누가 어떤 일을 해야 하는지에 대한 실제 수요를 포착하고, 사람과 AI 역량을 현실적으로 조합한 구성안을 만들며, 이를 실제 비즈니스 성과와 연계하는 인간-디지털 업무 조정 계층이 빠져 있다고 본다. 이 문제를 풀지 못하면 다음 자동화 물결은 기업을 재편하는 수준을 넘어, 일 자체를 불안정하게 만들 것”이라고 경고했다.

슈메이커의 지적은 많은 경영진이 간과하는 지점을 잘 보여준다. AI의 잠재력은 단순한 기술 혹은 운영 과제가 아니라 리더십의 본질을 묻는 문제라는 점이다. 인간이 해야 할 업무와 기계로 자동화할 업무 사이 경계가 흐려지는 상황에서 IT와 비즈니스 리더는 사람, 기술, 문화의 깊은 통합을 통해 가치 창출을 극대화하는 데 집중해야 한다.

그렇기 때문에 최고 경영진은 투자 타임라인을 다시 고민하고, 기술 및 시장 변화에 맞춰 조직의 역량을 확장하는 방식을 재설계해야 한다. 이런 인적 자원 관리의 변화는 미래 인력 구조를 예측하고, 사람과 기계 기반 지능을 연계해 인력을 배치하는 능력을 필요로 한다. 혁신은 현재 성과를 조금 보태는 차원이 아니라, 조직이 관련 인프라와 체계를 갖추고 스스로를 재창조할 준비가 되어 있는지 여부를 중심에 둬야 한다.

업무와 가치 창출의 분해

AI에서 이해 가능한 ROI를 도출하려면 업무를 구성하는 핵심 요소를 정확히 이해해야 한다. 어떤 수익이든 기술 도입뿐 아니라 프로세스 변화를 함께 고려해야 한다. 목표는 반복 작업에만 쓰이는 영역이 아니라 인간의 판단, 공감, 창의성이 필요한 영역에서 인간의 노력을 증폭시키는 방식으로 AI를 활용하고, 사람을 보다 높은 가치의 감독 및 대외 커뮤니케이션 역할에 배치해 생산성과 기여도를 극대화하는 데 있다.

성공의 핵심은 AI를 통해 인재가 매출 성장과 혁신을 주도할 수 있도록 돕는 방향으로 초점을 재조정하는 것이다. 목표는 어떤 직무에서든 80%에 달하는 자동화 가능 업무를 AI에 맡기고, 고숙련 인력이 나머지 20%에 집중하도록 만드는 것이다. 이 마지막 20%가 새로운 매출 성장, 고객 충성도, 혁신적 돌파구를 이끄는 영역이기 때문이다.

AI 시대에는 모든 산업이 재편될 것이며, CIO와 CEO가 함께 진화하지 않으면 AI 투자는 허공에 사라지는 비용이 된다. AI에서 실질적 가치를 얻기 위한 열쇠는 리더십이 미래 지향적 사고를 갖추고, 새로운 기술과 변화를 기꺼이 수용하느냐에 달려 있다. 중요한 것은 가장 뛰어난 코더가 되는 것이 아니라, 올바른 리더십 구조를 설계하는 일이다. AI를 사람 중심의 목표를 확장하는 도구로 활용하고, 단순히 운영 지표와 스프레드시트를 AI 데이터로 채우는 수단으로 쓰지 않는 리더십이 필요하다. 이를 위해서는 모든 의사결정 과정에 엄격한 윤리와 거버넌스를 심고, 각 AI 프로젝트가 조직에 어떤 목적을 수행하는지에 대해 비즈니스 측면에서 확실히 합의해 두어야 한다.
dl-ciokorea@foundryco.com

With the end of the year around the corner, I’ve been hearing a common refrain from enterprise IT and transformation leaders: “What budget should I allocate for AI agents in 2026?” The question comes as no surprise. While the rest of their organization might be winding down for the holidays, CIOs are gearing up for one of the most high-stakes planning cycles of the last decade.

AI agents are line items in almost every boardroom agenda. CIOs and CTOs are fielding a barrage of requests from their leadership cohort around “Where are our agents? What outcomes are we expecting? What’s the plan for next year?” According to Forrester, CIOs are set to receive more budget for AI in 2026, but it’s a case of more money, more problems. Next year, IT and tech leaders can expect continued business volatility and intensified pressure to justify every AI dollar is well spent.

As CIOs set their AI budgets, it’s worth taking a reality check: many AI projects are still struggling to make it from pilot to production, or if they do make it to production, they quickly find their use case cannot deliver the ROI they had hoped. That’s why I believe 2026 is a defining budget cycle. The organizations that select the right projects, invest in talent and capabilities and carefully consider the architecture needed to support agents at scale will build sustainable competitive advantages. As for the others? They’ll burn time and money on doomed pilots and incremental tools that offer no real business impact. To make smart bets for a critical year ahead, CIOs must start with understanding how AI agents can deliver real business outcomes instead of just excitement.

What do customers and employees actually want from AI agents? 

The reason AI agents are getting board-level attention is the promise to bridge the gap between human intent and business effect through automation. Over the course of the year, I’ve consulted with hundreds of enterprise leaders seeking to transform their business with AI agents. The ones able to turn those aspirations into transformations all possessed a similar ingredient – they identified the right use cases to start with. Any CIO and any organization can get this part right.

So what do customers and employees actually want from AI agents? Almost every high-impact AI agent project I’ve seen this year boils down to the same simple concept: a user expresses an intention, and an agent takes action on their behalf to deliver an outcome. This is the paradigm that separates agentic AI from chatbots. Agents promise to go beyond just information retrieval or recommendations. That capability is valuable, no doubt, but it’s table stakes in today’s AI world. Customers and employees don’t just want responses or insights; they want assistants that can take tangible actions. They don’t just want AI to help them navigate their supply chain orders across SAP modules; they want to say “order 10 tons of product” and have their agent deliver that outcome end to end.

The majority of successful AI agent projects I’ve seen look just like this. Agents for internal teams focus on meaningful ways to improve productivity by empowering workers to turn complex processes into simple requests. No one, especially anyone under 30, wants to spend time learning how to point-and-click their way through a needlessly complex user interface on a SaaS platform. Forward-thinkers are building their agents to ride a layer above core products to turn intention into outcomes. The ROI in these cases is driven by cost and time savings for the business, at scale.

As agentic capabilities evolve, I’m increasingly seeing this same concept also being applied to AI agents that reinvent the customer experience. Look at the mortgage industry for an example. These lenders report a high drop-off rate in online mortgage applications. The reality is that mortgage applications can be quite complicated. The average applicant is often overwhelmed by financial jargon or documents they may not have readily available. If the user gets confused and steps away, odds are they won’t come back. Now, imagine replacing that with an AI agent that interacts with the core service. It can answer questions, translate complex financial terms into plain terms in real-time, save the session if needed and securely reach out for bank documents. Just a 1-2% increase in completed applications from this represents a material impact on the bottom line. That’s high-impact for the business.

Don’t swing for the fences, just get on base 

As you’re allocating your budget for AI in 2026, here’s my advice: stop chasing moonshots. These vaguely scoped, overgeneralized agent dreams are often expensive, they rarely ship and they burn resources faster than they can create value. Instead, look for opportunities to hit singles and doubles. Keep your eye out for specific, high-value and outcome-driven projects that can deliver wins in months, not years. I’ll walk you through the coaching that I use with enterprise leaders planning AI projects.

Start with this question: What are 10 processes that are repetitive, well-documented and still being manually performed by humans? Score each one on a 1-10 scale across these three dimensions: the impact if you automated it, the risk if the project fails (where 10 is catastrophic) and the complexity to build and deploy it. The winning formula is high impact, low risk and low complexity. That’s your AI sweet spot. Aim your swings there.

One pharmaceutical company used this exact framework and landed on a sleeper hit: agent-based adverse event report processing. That’s not glamorous, but it freed up 40% of the team’s time that went back into actual drug discovery. In sales, I’m seeing teams use agents to create content production pipelines for outbounds, proposals and follow-ups, which helps speed up cycle times and frees reps to focus on closing deals. In financial services, agents are automating tedious back-office processes that are expensive and labor-intensive, cutting costs and reducing turnaround times by days.

Nailing the ROI formula for AI Agents 

So you’ve honed in on the right projects for 2026 AI agents, now comes the hard part: the investment. This is not free; you’ve got to make tough decisions about how to appropriate the budget and how to beef up the teams that will actually go build them. But first, you need to understand something critical about the ROI formula. Return and investment mean widely different things from company to company. 

At the enterprise scale, the value proposition for AI rests much more on increasing the bottom line. Enterprises have many more customers, opportunities for efficiency and additional sources of revenue. So even a one to two percent upside on a critical workflow, such as customer acquisition or cost reduction, can yield a material improvement to the bottom line. The scale at which enterprises operate changes the calculus for what automation flows should look like, due to the impact on their core business.

CIOs across the board must assemble the right team and ensure it is properly budgeted and staffed (I wrote on this topic earlier this year). On top of that, organizations must ensure they’re building AI agents the right way. Your ROI is fundamentally dependent on your ability to scale AI agents across all different teams in your business. Without security, compliance and governance built in from the start, you can’t scale at all. You need to solve for thousands of users, each with their own permissions, and be able to trace every action the agent takes on their behalf. Build these guardrails in from day one, and your agents can become force multipliers. Otherwise, they will drown under token costs to LLMs while the projects never go beyond a prototype.

If 2025 was the introduction to AI agents, then 2026 will be when the winners start to emerge. The companies that break away from the pack won’t be those that talked the loudest about agents or ran the most proof-of-cycle concepts. It will be the ones who shipped them to production and saved time, made money or created new customer experiences that resonate.  The difference won’t be luck; it will come down to those who approach agents with smart design, the right use case selection and informed bets on the teams and technology to bring automation to life. The right plan could change your company’s entire trajectory.

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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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I have spent most of my career accountable for the parts of technology nobody thinks about until something breaks. Service delivery, back-office workflows, knowledge decay, compliance friction and the invisible handoffs that quietly drain budgets. For years, I invested in automation as the answer to operational drag. We built rules, mapped flows and tried to automate the edge cases. But whenever reality changed, those automations snapped. It took me longer to realize I was automating drift.

Agentic AI changes the equation by introducing autonomy, adaptability and multi-step reasoning based on a deep understanding of context. It can escalate when confidence falls and apply policy dynamically. Over the past two years, I have deployed agentic capabilities across IT operations and talent acquisition. The cost savings were real, but the reduction in operational risk mattered more.

What concerns me most is how quickly interest is outpacing understanding. A recent enterprise AI maturity study found that many organizations are considering adopting agentic AI in the next 12 months, yet far fewer report being deeply familiar with AI technology. There is widening daylight between investment and comprehension, and leaders can feel it.

The hidden economics of back-office drag

Service delivery is accounted for as a cost center, but in practice, it behaves like a risk center. When incidents spike, I burn labor hours and credibility. When change freezes, innovation slows. When knowledge walks out the door, complexity compounds. Research from McKinsey estimates that major incident outages can cost more than $300,000 per hour when accounting for downtime, lost productivity and recovery labor. Outages erode trust as quickly as they drain budgets, and the longer the system stays down, the more stakeholders begin to question leadership’s judgment rather than the failure itself.

Agentic AI gave me ways to address root causes rather than symptoms. It accelerated the pace at which risk surfaced and reduced the dependence on human memory to carry operational knowledge.

IT service automation that actually bends cost curves

The first breakthrough came from reducing low-value, high-volume work. Password resets, access requests, policy clarification and device troubleshooting represented a disproportionate share of tickets. Conversational agents served as the first point of contact, recognizing intent, authenticating users, enforcing policy and triggering workflows. The response someone received at 4 p.m. on a weekday became indistinguishable from the one they received at 2 a.m.

As these agents matured, they evolved beyond conversation. Diagnostic agents pulled logs and compared them to historical incident signatures. Identity agents validated entitlements through policy. Remediation agents performed corrective actions autonomously when confidence thresholds were high enough. The agents could reason, plan and act instead of merely responding.

I also deployed agents that assisted human analysts. They summarized lengthy ticket histories, suggested relevant knowledge articles and drafted follow-up communication. They even generated new content as knowledge articles from closed incidents to expand self-service coverage. This type of coexistence shifted work away from repetition and toward judgment.

In parallel, autonomous agents operated inside infrastructure operations. They validated alerts, correlated telemetry and occasionally took action before anyone knew an issue existed. It was not about removing humans. It was about removing hours of manual investigation that added no value.

These moves consistently reduced incident resolution times. Industry benchmarks already show double-digit percentage decreases in resolution duration when agentic orchestration is applied to major incidents. I saw similar patterns. The improvement compounds because every minute saved in response time reduces the blast radius downstream.

Strengthening compliance and finance through continuous automation

Compliance workflows suffer when human memory carries the load. Before AI, teams stored rules in shared folders and hallway conversations. Today, compliance agents reconcile invoices, validate contract terms and flag anomalies proactively. They create explainable audit trails continuously rather than quarterly. NIST’s AI Risk Management Framework highlights traceability and explainability as foundational principles. Implementing those controls early reduced anxiety across audit teams and replaced after-the-fact cleanup with preventive action. This also reduced risk and elevated compliance reporting.

Finance experienced something similar. Reconciliation agents monitored variances and surfaced unusual patterns. What surprised me most was their reaction. They were not afraid of replacement. They were afraid of errors. When automation reduced manual variance, they became vocal advocates.

Finding use cases through process mapping

One of the most practical methods for identifying where agentic AI can help is process mapping. When I began visualizing workflows end-to-end, bottlenecks became obvious. Process mining tools uncovered rework loops, approval delays and exception handling that never made it onto formal documentation. Seeing work as a series of minor frictions makes it easier to understand where agents can step in.

The most compelling results emerged when agents were orchestrated together. A conversational agent collected symptoms and authenticated the user. A diagnostic agent pulled logs. A knowledge agent suggested resolutions based on pattern similarity. A remediation agent executed the corrective action. An orchestration layer coordinated all of it. That is where the returns accelerate.

Organizations that have leaned into this approach have reported dramatic improvements in self-submitted HR requests, faster employee onboarding and higher satisfaction due to real-time knowledge enrichment. This reinforces a simple truth: removing friction creates participation.

Workflow orchestration reduces cross-function friction

Most operational drag does not come from incidents. It comes from handoffs. Procurement requests that are bounced between finance, IT and security. Access approvals that depend on availability rather than policy. Tickets that accumulate because approvers are out of office or lack clarity. These interactions create delay and noise that nobody can see on a dashboard.

Orchestration agents change that dynamic. They trigger conditional workflows, collect missing information, validate approvals against policy and route requests without human intervention. Approval agents enforce thresholds. Inventory agents check asset life cycle status. Risk agents flag questionable suppliers. Tasks that previously took days now close in hours. And reducing interruptions had the same effect on productivity as adding headcount.

Why I do not build foundation models from scratch

At one point, I considered building a model internally. The idea was tempting. Owning the entire stack felt like a strategic advantage. But foundation models require massive compute, specialized research talent and years of iteration. Instead, I licensed access to best-in-class models and built the agentic layer on top. We used retrieval-augmented generation to feed proprietary documents and policy rules into the model, then layered business logic that governed behavior in context. We designed this with a strong emphasis on data governance, access control and privacy protection to ensure data was handled responsibly and in compliance with regulations.

This hybrid buy-and-enhance approach delivered faster time-to-value, reduced technical risk and allowed us to retain control of proprietary data and logic.

When I would build instead of buy

There are scenarios where owning the full stack makes sense. If AI is central to strategic product differentiation, if data cannot leave owned infrastructure, if regulatory constraints demand full control or if internal AI engineering maturity is high, then building becomes rational rather than romantic. MIT Sloan has explored the productivity paradox of AI, noting that capability without maturity can increase cost rather than reduce it. That matched my experience.

It is also important to recognize that both data and process maturity must be at a high bar before considering custom agentic development. Automating a broken or incomplete process does not eliminate chaos; it multiplies it. Inadequate governance, missing metadata, inconsistent runbooks or contradictory policies will produce unpredictable outcomes at machine speed. AI does not fix drift. It amplifies whatever it touches. When the substrate is clean, autonomy accelerates value. When it is not, it collapses into noise.

Agentic AI in talent acquisition was the unexpected hero

The biggest lift I saw came from HR. Application backlogs caused candidates to drop off. Interview scheduling created friction across time zones. Compensation exceptions slowed approvals. Agentic AI addressed all three. Conversational agents guide candidates through application steps. Scheduling agents reconciled calendars, set up interviews and sent confirmations. Qualification agents screened resumes against policy. Sentiment agents summarized tone and engagement from written and verbal communication, providing summaries of conversations to all parties.

Time-to-fill decreased and candidate satisfaction improved simply by eliminating the waiting. The SHRM Candidate Application Abandonment Study notes that delayed response time is one of the top drivers of candidate abandonment. Agents save time. And when you compress cycle time in recruiting, you increase talent density, which later reduces operational drag across the enterprise.

Cost is shifting from labor to compute

When human workload decreases, inference cost rises. Finance teams are not yet fluent in ROC (return on compute), but this metric will become as common as ROI. Without guardrails, cloud cost drift can quietly consume the savings that automation promised. I track ROC as closely as I track cost per ticket because unmonitored inference is the new runaway labor. Compute cycles do not call in sick or take a vacation and they scale without asking permission.

This is where leaders can get fooled. If compute spend rises faster than human workload declines, autonomy without financial guardrails can turn cloud cost into the new labor balloon — just harder to see, harder to attribute and harder to challenge. The danger is that it hides in budgets where executives are not trained to look. Leaders know how to question headcount, overtime and contractor spend, but they rarely scrutinize the compute charges buried in cloud bills. AI costs grow in technical corners of the budget, where they can expand quietly and avoid the financial scrutiny applied to labor.

In the same way cloud transformed capital expense into operating expense overnight, agentic AI will force us to treat compute as a strategic cost center rather than a utility. If we do not build that discipline now, autonomy will become the most elegant form of overspending we have ever engineered.

What success looks like

In mature environments, I saw fewer escalations, shorter outages, improved hiring velocity and predictable change cycles. Operational friction decreased and innovation increased. Teams felt less interrupted and more trusted. That cultural shift was as valuable as the financial one.

Predictability is the real outcome. When service delivery becomes stable and repeatable, IT stops acting like an internal repair shop and starts behaving like an engine of growth. Reliable delivery creates the headroom to build new products, partner with the business on revenue initiatives and invest in automations that compound value instead of compensating for failure. As the operational noise floor drops, capacity shifts from firefighting to forward motion.

Agentic AI is not just about doing the same work cheaper. It is about creating the conditions where IT can participate in strategy, influence the customer experience and build digital capabilities that generate revenue rather than support it. When systems stop surprising us, we can finally focus on the work that moves the company forward.

Final thought

Agentic AI is not about replacing judgment. It is about protecting it. When machines remove drag, humans spend more time on the decisions that matter. The organizations that treat back-office operations as a resilience discipline, not a cost bucket, will bend cost curves and compress risk where it quietly accumulates.

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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.

In a previous feature about enterprise architects, gen AI had emerged, but its impact on enterprise technology hadn’t been felt. Today, gen AI has spawned a plethora of agentic AI solutions from the major SaaS providers, and enterprise architecture and the role of enterprise architect is being redrawn. So what do CIOs and their architects need to know?

Organizations, especially their CEOs, have been vocal of the need for AI to improve productivity and bring back growth, and analysts have backed the trend. Gartner, for example, forecasts that 75% of IT work will be completed by human employees using AI over the next five years, which will demand, it says, a proactive approach to identifying new value-creating IT work, like expanding into new markets, creating additional products and services, or adding features that boost margins.

If this radical change in productivity takes place, organizations will need a new plan for business processes and the tech that operates those processes. Recent history shows if organizations don’t adopt new operating models, the benefits of tech investments can’t be achieved.

As a result of agentic AI, processes will change, as well as the software used by the enterprise, and the development and implementation of the technology. Enterprise architects, therefore, are at the forefront of planning and changing the way software is developed, customized, and implemented.

In some quarters of the tech industry, gen AI is seen as a radical change to enterprise software, and to its large, well-known vendors. “To say AI unleashed will destroy the software industry is absurd, as it would require an AI perfection that even the most optimistic couldn’t agree to,” says Diego Lo Giudice, principal analyst at Forrester. Speaking at the One Conference in the fall, Lo Giudice reminded 4,000 business technology leaders that change is taking place, but it’s built on the foundations of recent successes.

“Agile has given better alignment, and DevOps has torn down the wall between developers and operations,” he said. “They’re all trying to do the same thing, reduce the gap between an idea and implementation.” He’s not denying AI will change the development of enterprise software, but like Agile and DevOps, AI will improve the lifecycle of software development and, therefore, the enterprise architecture. The difference is the speed of change. “In the history of development, there’s never been anything like this,” adds Phil Whittaker, AI staff engineer at content management software provider Umbraco.

Complexity and process change

As the software development and customization cycle changes, and agentic applications become commonplace, enterprise architects will need to plan for increased complexity and new business processes. Existing business processes can’t continue if agentic AI is taking on tasks currently done manually by staff.

Again, Lo Giudice adds some levity to a debate that can often become heated, especially in the wake of major redundancies by AI leaders such as AWS. “The view that everyone will get a bot that helps them do their job is naïve,” he said at the One Conference. “Organizations will need to carry out a thorough analysis of roles and business processes to ensure they spend money and resources on deploying the right agents to the right tasks. Failure to do so will lead to agentic technology being deployed that’s not needed, can’t cope with complex tasks, and increases the cloud costs of the business.

“It’s easy to build an agent that has access to really important information,” says Tiago Azevedo, CIO for AI-powered low-code platform provider OutSystems. “You need segregation of data. When you publish an agent, you need to be able to control it, and there’ll be many agents, so costs will grow.”

The big difference, though, is deterministic and non-deterministic, says Whittaker. So non-deterministic requires guardrails of deterministic agents that produce the same output every time over the more random outcomes of non-deterministic agents. Defining business outcomes by deterministic and non-deterministic is a clear role for enterprise architecture. He adds that this is where AI can help organizations fill in gaps. Whittaker, who’s been an enterprise architect, says it’ll be vital for organizations to experiment with AI to see how it can benefit their architecture and, ultimately, business outcomes.

“The path to greatness lies not in chasing hype or dismissing AI’s potential, but in finding the golden middle ground where value is truly captured,” write Gartner analysts Daryl Plummer and Alicia Mullery. “AI’s promise is undeniable, but realizing its full value is far from guaranteed. Our research reveals the sobering odds that only one in five AI initiatives achieve ROI, and just one in 50 deliver true transformation.” Further research also finds just 32% of employees trust the organization’s leadership to drive transformation. “Agents bring an additional component of complexity to architecture that makes the role so relevant,” Azevedo adds.

In the past, enterprise architects were focused on frameworks. Whittaker points out that new technology models will need to be understood and deployed by architects to manage an enterprise that comprises employees, applications, databases, and agentic AI. He cites MCP as one as it provides a standard way to connect AI models to data sources, and simplifies the current tangle of bespoke integrations and RAG implementations. AI will also help architects with this new complexity. “There are tools for planning, requirements, creating epics, user stories, code generation, documenting code, and translating it,” added Lo Giudice.

New responsibilities

Agentic AI is now a core feature of every major EA tool, says Stéphane Vanrechem, senior analyst at Forrester. “These agents automate data validation, capability mapping, and artifact creation, freeing architects to focus on strategy and transformation.” He cites the technology of Celonis, SAP Signavio, and ServiceNow for their agentic integrations. Whittaker adds that the enterprise architect has become an important human in the loop to protect the organization and be responsible for the decisions and outcomes that agentic AI delivers.

Although some enterprise architects will see this as a collapse of their specialization, Whittaker thinks it broadens the scope of the role and makes them more T-shaped. “I can go deep in different areas,” he says. “Pigeon-holing people is never a great thing to do.”

Traditionally, architecture has suggested that something is planned, built, and then exists. The rise of agentic AI in the enterprise means the role of the enterprise architect is becoming more fluid as they continue to design and oversee construction. But the role will also involve continual monitoring and adjustment to the plan. Some call this orchestration, or perhaps it’s akin to map reading. An enterprise architect may plan a route, but other factors will alter the course. And just like weather or a fallen tree, which can lead to a route deviation, so too will enterprise architects plan and then lead when business conditions change.

Again, this new way of being an enterprise architect will be impacted by technology. Lo Guidice believes there’ll be increased automation, and Azevedo sides with the orchestration view, saying agents are built and a catalogue of them is created across the organization, which is an opportunity for enterprise architects and CIOs to be orchestrators.

Whatever the job title, Whittaker says enterprise architecture is more important than ever. “More people will become enterprise architects as more software is written by AI,” he says. “Then it’s an architectural role to coordinate and conduct the agents in front of you.” He argues that as technologists allow agents and AI to do the development work for them, the responsibility of architecting how agents and processes function broadens and becomes the responsibility of many more technologists.

“AI can create code for you, but it’s your responsibility to make sure it’s secure,” he adds. Rather than developing the code, technology teams will become architecture teams, checking and accepting the technology that AI has developed, and then managing its deployment into the business processes.

With shadow AI already embedded in organizations, Whittaker’s view shows the need for a team of enterprise architects that can help business align with the AI agents they’ve deployed, and at the same time protect customer data and cybersecurity posture.

AI agents are redrawing the enterprise, and at the same time replanning the role of enterprise architects.

IT가 막 성장하던 시절, 당시 ‘전자 데이터 처리(Electronic Data Processing, EDP)’로 불리던 분야가 핵심 회계 업무 자동화를 마무리하던 때에는 경영진 모두가 IT와 친해지고 싶어 했다. 그 이유는 간단했다. 수작업에 비하면 완성도가 다소 떨어지는 자동화라도 훨씬 나았기 때문이다.

그 시절에는 모두가 EDP를 사랑했다.

하지만 수십 년이 흐른 지금, 비즈니스 관리자는 그때보다 훨씬 까다로워졌다. 주요 업무 프로세스는 이미 자동화돼 있어 IT는 단순 자동화를 넘어 최적화까지 담당해야 하고, 애플리케이션 도입뿐 아니라 여러 시스템의 통합까지 해결해야 하는 상황이다.

그렇다면 왜 이제는 비즈니스가 IT를 예전처럼 좋아하지 않을까? 혹시 다니엘 골먼의 ‘EQ 감성지능’ 책을 필독서로 지정해야 한다고 생각할 수 있다. 사랑은 어디까지나 감정이니 EQ가 높아지면 관계도 나아질 거라는 기대다.

혹은 더 나아가, 인공지능을 넘어 ‘인공 감성지능(Artificial Emotional Intelligence, AEI)’을 도입할 수 있으면 좋겠다는 생각을 할지도 모른다. 공상과학 영화에나 나올 법한 발상이지만, 결말은 오히려 더 암울할 수 있다.

호감 단계 v1.0: 당신을 좋아하는 것과 IT를 좋아하는 것은 다르다

‘호감’을 어떻게 측정할 것인가를 생각해 보면, 명확성이 중요하다. 많은 CIO는 자신이 경영진과 얼마나 잘 지내는지를 지표로 삼는다. 그 자체로 목표로 삼을 이유는 충분하지만, 비즈니스와 IT의 관계 수준을 판단하는 지표로는 적절하지 않다.

그 이유는 명확하다. 누군가 당신을 좋아하는 것과 IT 조직을 좋아하는 것은 전혀 다른 문제이기 때문이다. 개인을 좋아하는 일은 인간적 관계의 영역이지만, 조직을 좋아하게 만드는 일은 성격이 다르다. 시카고 컵스나 그린베이 패커스 같은 스포츠팀, 일부 관계 중심 소매 브랜드는 이런 조직적 호감을 이끌어낼 수 있다. 애플도 팬층을 기반으로 이런 위치를 확보한 사례다.

그러나 IT는 스포츠팀도 아니고 소매 브랜드도 아니다. 그래서 당신이 이끄는 IT 조직을 비즈니스가 좋아하게 만들기 위한 안내서나 정답은 존재하지 않는다. 좋아하게 만드는 것은 물론, ‘사랑’에 이르게 하는 것은 더욱 어렵다.

호감 단계 v2.0: 팀 구성원을 좋아한다고 해서 IT를 좋아하는 것은 아니다

회사 경영진에게 IT팀 핵심 구성원들과 얼마나 잘 지내는지 물어본다고 가정해 보자. 그러면 여러 경영진이 자신과 함께 일하는 IT 분석가를 높이 평가할 것이다. 이를 근거로 비즈니스와 IT의 관계가 매우 견고하다고 판단할 수도 있다.

하지만 이것 역시 잘못된 지표다. 오히려 이런 1대1 관계의 긍정적 평가가 강한 비즈니스·IT 관계와는 정반대 상황을 보여주는 신호일 가능성이 크다.

실제로는 당신이 조사한 관리자들이 IT의 공식 프로세스와 절차를 거치는 경험을 매우 불편하고 비효율적이라고 느끼고 있을 수 있다. 그들이 좋게 평가하는 IT 분석가는 이러한 번거로운 절차를 우회하도록 도와주는 사람일 가능성이 훨씬 높다.

호감 단계 v3.0: IT의 한계를 인정하는 순간 관계가 달라진다

비즈니스 관리자는 어느 직급이든 항상 IT에 무언가를 요청한다. 유능한 CIO는 이러한 요청을 처리하기 위한 체계를 갖춘다. 이 프로세스는 하나의 전제로 시작된다. IT 프로젝트라는 것은 존재하지 않으며, 모든 프로젝트는 비즈니스 변화와 개선을 위한 것이어야 한다는 점이다. 그렇지 않다면 추진할 이유가 없다.

여기서 한 단계 더 나아간 CIO는 IT 구성원 전체에게 요청 대응의 핵심 규칙을 철저히 교육한다. 누군가 “IT가 이 일을 할 수 있는가?”라고 물었을 때, “예”라고 답하는 것은 언제나 잘못이며, “아니오”라고 답하는 것도 마찬가지로 잘못이라는 점이다.

정답은 항상 이 한 문장으로 귀결된다.
“그 일을 하기 위해 필요한 조건을 설명하겠다.”

요청–응답 상황에 놓인 IT 분석가는 정작 무엇이 필요한지 정확히 모를 때가 많다. IT는 본질적으로 복잡하며, 비즈니스 요청은 흔히 개념적으로 모호하고 구체성이 떨어지기 때문이다.

그럴 때 IT 직원이 취해야 할 합리적인 태도는 다음과 같다.
“무엇이 필요한지 파악하기 위해 어떤 작업이 필요한지부터 설명하겠다.”

그렇다면 시스템을 안정적으로 운영하고, 프로젝트를 계획대로 성공시키는 일이 비즈니스가 IT를 사랑하게 만드는 요소가 될 수는 없을까?

실은 그렇지 않다. 물론 시스템을 안정적으로 유지하고 프로젝트를 차질 없이 완수하는 일은 비즈니스가 IT를 존중하게 만드는 데 중요한 역할을 한다. 존중을 얻고 싶다면 좋은 출발점이 될 수 있다.

하지만 비즈니스가 IT를 ‘사랑하게’ 만들고 싶다면?

무엇보다 먼저 ‘거절하지 않는 자세’가 필요하다. 이는 IT가 비즈니스의 편에 서 있으며, 자신들에게 가장 편하거나 위험이 적은 방식이 아니라 비즈니스 관점에서 답하려 한다는 신호를 준다.

여기에 ‘예스’라고 쉽게 말하지 않는 태도가 더해질 때 비로소 완성된다. 이는 비즈니스가 비현실적인 약속이 아닌, 실제 상황을 기반으로 한 현실적 조언을 받고 있다는 확신을 얻게 해준다.

즉, 비즈니스가 신뢰하고 사랑할 수 있는 IT는 빈말 대신 현실을 정확히 보여주는 IT다.
dl-ciokorea@foundryco.com

여러 디지털 전환 프로젝트를 이끌고 재무 성과를 만들어냈다. 경영진은 고객과 직원 경험을 모두 개선한 변화 리더십 역량을 높게 평가하고 있다. 주도적으로 구현한 아키텍처는 이제 플랫폼 표준이 됐고, 조직의 데이터와 AI 전략을 떠받치는 기반이 됐다.

이 단계에서 많은 IT 책임자가 이제 CIO나 데이터, 디지털, 보안 분야의 다른 C 레벨 자리에 도전할 자격이 있는지 자문한다.

CIO.com의 연례 CIO 현황(State of the CIO) 보고서에 따르면, CIO의 80% 이상이 역할이 점점 더 디지털과 혁신 중심으로 바뀌고 있고 디지털 전환을 이끄는 데 더 깊이 관여하고 있으며, CIO가 변화의 촉매 역할을 맡고 있다고 답했다. 이 조건을 충족하고 있다면, C 레벨 자리에 어떻게 올라설 수 있을지 고민해야 한다.

디지털 혁신 책임자는 훌륭한 C 레벨 후보

디지털 트랜스포메이션 프로젝트를 이끄는 경험은 C 레벨 자리에 오르기 위한 중요한 전제 조건이다. 하지만 C 레벨 임원이 되면, 모든 IT 프로젝트는 물론, 운영 전반의 성과와 리스크에 대해 책임을 지면서 역할과 책임이 크게 확대된다. C 레벨 기술 임원은 CEO와 CFO가 공감할 수 있는 전략을 수립해야 하고, 끊임없이 진화하는 디지털 운영 모델을 총괄해야 한다.

워크데이(Workday)의 CIO 라니 존슨은 “리더로 성장하고자 하는 기술 책임자는 프로젝트 기반 변화 실행을 관리하는 수준을 넘어, 기업 전체의 기술, 아키텍처, IT 전략에 대해 완전한 소유권과 책임을 져야 한다”라고 강조했다.

존슨은 “IT 인프라, 사이버 보안, AI 플랫폼, 핵심 시스템 운영, 데이터 거버넌스에 대해 깊이 있고 실무적인 전문성을 쌓아야 한다. 기술 전략을 안정적으로 운영하면서도 지속적인 비즈니스 가치로 연결할 수 있는 역량을 보여줘야 한다”라고 설명했다.

C 레벨 역할을 준비하려는 IT 리더는 평생 학습 프로그램을 설계해 전문성을 키우고 자신감을 쌓아야 한다. 70-20-10 학습 모델은 업무 경험 70%, 동료와의 소셜 러닝 20%, 정규 교육 10%에 초점을 맞추는 접근법이다. 디지털 혁신을 이끄는 리더는 이 모델을 C 레벨 기회를 향한 여정에 어떻게 적용하는지 알아보자.

전문가에서 ‘비전문 영역’의 인플루언서로 전환하는 경험

많은 디지털 혁신 리더가 여러 해에 걸친 전사 차원의 전략 프로젝트까지 포함해 자신이 맡은 프로그램 전반에 대한 전문성을 확보하려고 노력한다. 우선순위를 조정하고 리스크를 낮추기 위해 애자일 프로그램의 모든 상황을 들여다보려는 리더도 많다.

하지만 C 레벨 기술 책임자는 모든 전략 프로젝트의 세부 사항까지 관여할 시간도 없고, 기술 구현의 세부 사항에 대해 최고의 전문가도 아니다. C 레벨 임원이 되고자 하는 IT 리더가 채워야 할 70%의 업무 경험은 전문성과 책임 범위를 넘어서는 영역에 과감하게 발을 들여놓는 데서 나온다.

프린시펄(Principal)의 CIO 캐시 케이는 “C 레벨 기술 임원 역할로 올라서는 데 중요한 것은 모든 답을 알고 있는 것이 아니라, 모호함과 복잡성 속에서 리더십을 발휘하는 법을 배우는 것이다”라고 말했다. 케이는 “가장 값진 성장은 스트레치 과제를 맡고, 임팩트가 큰 비즈니스 문제를 해결하면서, IT 조직 내부에만 머물지 않고 전사 차원에서 영향력을 발휘하는 과정을 통해 이뤄진다. 이런 경험이 강력한 멘토와 동료의 조언과 결합되면 오래 가는 리더십 기반이 만들어진다”라고 설명했다.

다음은 업무 현장에서 찾아야 할 경험에 대한 몇 가지 조언이다.

• 영업 및 마케팅 책임자와 함께 고객을 방문하고 비즈니스 감각을 기르고 구매자의 니즈를 이해하고, 고객의 엔드 투 엔드 워크플로우를 직접 검토한다.
• 다른 프로젝트를 이끄는 리더를 멘토링해, 전문 영역 밖에 있는 사안에 대해서도 조언을 제공할 수 있는 자신감을 키운다.
• 워크숍을 직접 주관한다. 특히 중간에 갈등이나 논쟁이 터지는 상황을 잘 수습해낸다면, 경영진 위원회나 이사회 앞에서 발표하는 훌륭한 실전 경험을 쌓을 수 있다.
• 새로운 기술 도입에 부정적인 부서 리더를 찾아 현 상태에 안주하는 사고를 깨뜨릴 수 있는 방법을 모색한다.
• 의사결정에 데이터를 충분히 활용하지 못하고 효율성을 높이기 위한 AI 도입에서 뒤처진 운영 조직을 골라 협력하면서 변화의 촉매 역할을 수행한다.

두 번째로 키워야 할 역량은 경청하고 도전하고 적응하고, 방향을 전환하는 능력이다. 성공적인 C 레벨 리더는 비전을 제시하고 지속적으로 계획을 세워야 하지만, 시장과 고객, 투자자, 이해관계자의 요구가 바뀌어 목표를 재조정해야 할 때를 감지할 줄도 알아야 한다.

메가포트(Megaport)의 CTO 카메론 다니엘은 “새로운 기술, 비즈니스 우선순위 변화, 예상치 못한 변수는 잘 설계된 계획도 단숨에 무력화할 수 있다”라고 지적했다. 다니엘은 “성공적인 리더는 변화가 벌어졌을 때 그때그때 대응하는 수준에 머물지 않는다. 변화를 미리 예측하고, 조직이 변화에 대비해 준비되고 무장돼 있도록 만든다. CTO는 이런 적응력의 총 설계자로서, 솔루션이 혁신의 속도를 따라 발전하면서도 비즈니스 영향과 전략적 목표를 계속 달성하도록 책임져야 한다”라고 강조했다.

AI와 신기술에 대한 소셜 러닝에 집중

생성형 AI와 인공 일반 지능이 언제 등장할 것인지에 대한 과대광고가 넘쳐나는 상황이다. 이사회와 경영진은 C 레벨 기술 리더가 이런 소음을 걸러내고 AI 전략을 이끌고, 데이터와 AI 거버넌스를 확립해 주기를 기대한다.

보도자료와 소규모 PoC만으로는 현실적인 AI 비전과 단기적인 투자 수익을 만들 수 없다. C 레벨 기술 리더는 동료와 네트워크를 넓히고 커뮤니티에 참여해 다른 조직이 어디에 투자하고 AI 기반 비즈니스 성과를 어떻게 만들고 있는지 배우면서 지식을 확장한다.

최근 열린 ‘커피 위드 디지털 트레일블레이저(Coffee With Digital Trailblazers)’에서는 변화 리더가 C 레벨 리더십의 바통을 어떻게 이어받을 수 있는지, 그리고 소셜 러닝을 사내에서 어떻게 구현할 수 있는지 논의했다.

예를 들어, 컨티뉴엄 스트래티지(Continuums Strategies)의 설립자이자 vCISO인 데릭 버츠는 AI 기반 위협 탐지와 다양한 유형의 AI 기반 자동화 공격을 분류하는 팀에 합류할 것을 제안했다. 성장 전략가이자 프랙셔널 CIO인 조 푸글리시는 AI를 기회로 삼는 열쇠로 호기심과 끊임없는 질문을 꼽았다.

푸글리시는 “호기심이 없고 일이 왜 그렇게 진행되는지 뿌리까지 파고들지 않으면, 고객 만족도를 새로운 수준으로 끌어올리고, 새로운 제품을 만들고, 새로운 매출원을 열고, 비용을 줄이는 더 새롭고, 더 빠르고, 더 똑똑하고, 더 저렴한 방법을 절대 발명하지 못한다”라고 강조했다.

소셜 러닝에서 또 하나 집중해야 할 영역은 비즈니스 운영을 뒷받침하는 데이터를 끝까지 설명할 수 있는 현업 전문가와의 만남이다. 퀀텍사(Quantexa)의 CTO 제이미 허튼은 “에이전틱 AI가 현실로 다가오면서 데이터 리터러시는 핵심 리더십 역량이 되고 있다. 데이터가 어디에서 왔는지 설명할 수 없다면, 그 위에 AI를 책임감 있게 올릴 수 없다. 사람과 AI 에이전트가 나란히 일하는 시대는 많은 사람이 생각하는 것보다 훨씬 빨리 온다”라고 지적했다.

‘왜’라는 질문을 던지는 소셜 러닝, AI 보안 이슈에 대응하는 보안팀과의 미팅, 비즈니스 운영 데이터 검토는 AI가 큰 가치를 만들 수 있는 영역에 대한 아이디어를 정리하는 데 도움을 준다. 인사이트(Insight) 계열사 SADA의 CTO 마일스 워드는 “C 레벨에 가장 빨리 다가가는 길은 회사의 명운이 걸린 문제를 직접 찾아 나서는 것이다”라고 말했다.

정규 교육을 버리지 말라

많은 C 레벨 리더는 업무가 너무 벅차고 시간이 부족하다는 이유로 정식 학습 활동을 ‘있으면 좋은 것’ 정도로 취급한다. 평생 학습자는 독서, 청취, 시청, 온라인 강의, 기타 학습 경험에 10% 정도 시간을 투자하면 사고의 폭이 넓어지고 새로운 개념을 접할 수 있다는 사실을 이해하고 있다. 학습은 단순한 기술 역량 개발에 그치지 않는다.

쏘트스팟(ThoughtSpot)의 최고 데이터·AI 전략 책임자인 신디 하우슨은 “혁신 속도가 빠른 지금은 70-20-10 규칙이 충분하지 않으며, 정식 학습 활동에 해당하는 10%는 더 늘려야 한다”라고 제안했다. 또, “집중적인 핸즈온 미니 클래스와 최신 AI 혁신 최전선에 있는 리더와의 피어 네트워크가 결합된 시의적절한 서밋을 활용하는 ‘바이브 러닝(Vibe Learning)’ 방식이 효과적이다”라고 덧붙였다.

활용할 수 있는 다른 학습 기회는 다음과 같다.

• 디지털 트랜스포메이션 필독서 목록, CIO 추천 도서, CIO를 위한 필독서 40선 같은 책을 읽는다.
• CIO 리더십 라이브(CIO Leadership Live), CXO토크(CXOTalk), 피터 하이의 테크노베이션(Technovation with Peter High), CIO 인 더 노우(CIO in the Know) 같은 인기 CIO 팟캐스트를 자주 청취한다.
• 링크드인의 이그제큐티브 리더십 과정과 CIO 대상 유데미(Udemy) 강의 같은 온라인 학습 기회를 검토한다.
• 더 큰 투자를 한다면 버클리나 카네기 멜런 대학교, 와튼 등 교육기관에서 제공하는 CTO 대상 학위 프로그램을 고려할 수 있다.

마지막으로, C 레벨 역할이 모든 사람에게 맞는 것은 아니다. CIO 현황 조사에 따르면, CIO의 43%는 스트레스 수준을 1~10점으로 평가할 때 8점 이상이라고 답했다. 따라서 C 레벨 자리를 오르고자 한다면, 경력 목표를 세우기 전에 역할을 충분히 이해해야 한다.
dl-ciokorea@foundryco.com

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.