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.”

Software management struggles that have pained enterprises for decades cause the same anguish to government agencies, and a bill making its way through the US House of Representatives to strengthen controls around government software management holds lessons for enterprises too.

The Strengthening Agency Management and Oversight of Software Assets (SAMOSA) bill, H.R. 5457, received unanimous approval from a key US House of Representative committee, the Committee on Oversight and Government Reform, on Tuesday.

SAMOSA is mostly focused on trying to fix “software asset management deficiencies” as well as requiring more “automation of software license management processes and incorporation of discovery tools,” issues that enterprises also have to deal with.

In addition, it requires anyone involved in software acquisition and development to be trained in the agency’s policies and, more usefully, in negotiation of contract terms, especially those that put restrictions on software deployment and use.

This training could also be quite useful for enterprise IT operations. It would teach “negotiating options” and specifically the “differences between acquiring commercial software products and services and acquiring or building custom software and determining the costs of different types of licenses and options for adjusting licenses to meet increasing or decreasing demand.”

The mandated training would also include tactics for measuring “actual software usage via analytics that can identify inefficiencies to assist in rationalizing software spending” along with methods to “support interoperable capabilities between software.”

Outlawing shadow IT

The bill also attempts to rein in shadow IT by “restricting the ability of a bureau, program, component, or operational entity within the agency to acquire, use, develop, or otherwise leverage any software entitlement without the approval of the Chief Information Officer of the agency.” But there are no details about how such a rule would be enforced.

It would require agencies “to provide an estimate of the costs to move toward more enterprise, open-source, or other licenses that do not restrict the use of software by the agency, and the projected cost savings, efficiency measures, and improvements to agency performance throughout the total software lifecycle.” But the hiccup is that benefits will only materialize if technology vendors change their ways, especially in terms of transparency.

However, analysts and consultants are skeptical that such changes are likely to happen.

CIOs could be punished

Yvette Schmitter, a former Price Waterhouse Coopers principal who is now CEO of IT consulting firm Fusion Collective, was especially pessimistic about what would happen if enterprises tried to follow the bill’s rules.

“If the bill were to become law, it would set enterprise CIOs up for failure,” she said. “The bill doubles down on the permission theater model, requiring CIO approval for every software acquisition while providing zero framework for the thousands of generative AI tools employees are already using without permission.”

She noted that although the bill mandates comprehensive assessments of “software paid for, in use, or deployed,” it neglects critical facets of today’s AI software landscape. “It never defines how you access an AI agent that writes its own code, a foundation model trained on proprietary data, or an API that charges per token instead of per seat,” she said. “Instead of oversight, the bill would unlock chaos, potentially creating a compliance framework where CIOs could be punished for buying too many seats for a software tool, but face zero accountability for safely, properly, and ethically deploying AI systems.”

Schmitter added: “The bill is currently written for the 2015 IT landscape and assumes that our current AI systems come with instruction manuals and compliance frameworks, which they obviously do not.”

She also pointed out that the government seems to be working at cross-purposes. “The H.R. 5457 bill is absurd,” she said. “Congress is essentially mandating 18-month software license inventories while the White House is simultaneously launching the Genesis Mission executive order for AI that will spin up foundation models across federal agencies in the next nine months. Both of these moves are treating software as a cost center and AI as a strategic weapon, without recognizing that AI systems are software.”

Scott Bickley, advisory fellow at Info-Tech Research Group, was also unimpressed with the bill. “It is a sad, sad day when the US Federal government requires a literal Act of Congress to mandate the Software Asset Management (SAM) behaviors that should be in place across every agency already,” Bickley said. “One can go review the [Office of Inspector General] reports for various government agencies, and it is clear to see that the bureaucracy has stifled all attempts, assuming there were attempts, at reining in the beast of software sprawl that exists today.”

Right goal, but toothless

Bickley said that the US government is in dire need of better software management, but that this bill, even if it was eventually signed into law, would be unlikely to deliver any meaningful reforms. 

“This also presumes the federal government actually negotiates good deals for its software. It unequivocally does not. Never has there been a larger customer that gets worse pricing and commercial terms than the [US] federal government,” Bickley said. “At best, in the short term, this bill will further enrich consultants, as the people running IT for these agencies do not have the expertise, tooling, or knowledge of software/subscription licensing and IP to make headway on their own.”

On the bright side, Bickley said the goal of the bill is the right one, but the fact that the legislation didn’t deliver or even call for more funding makes it toothless. “The bill is noble in its intent. But the fact that it requires a host of mandatory reporting, [Government Accountability Office] oversight, and actions related to inventory and overall [software bill of materials] rationalization with no new budget authorization is a pipe dream at best,” he said. 

Sanchit Vir Gogia, the chief analyst at Greyhound Research, was more optimistic, saying that the bill would change the law in a way that should have happened long ago.

“[It] corrects a long-standing oversight in federal technology management. Agencies are currently spending close to $33 billion every year on software. Yet most lack a basic understanding of what software they own, what is being used, or where overlap exists. This confusion has been confirmed by the Government Accountability Office, which reported that nine of the largest agencies cannot identify their most-used or highest-cost software,” Gogia said. “Audit reports from NASA and the Environmental Protection Agency found millions of dollars wasted on licenses that were never activated or tracked. This legislation is designed to stop such inefficiencies by requiring agencies to catalogue their software, review all contracts, and build plans to eliminate unused or duplicate tools.”

Lacks operational realism

Gogia also argued, “the added pressure of transparency may also lead software providers to rethink their pricing and make it easier for agencies to adjust contracts in response to actual usage.” If that happens, it would likely trickle into greater transparency for enterprise IT operations. 

Zahra Timsah, co-founder and CEO of i-GENTIC AI, applauded the intent of the bill, while raising logistical concerns about whether much would ultimately change even if it ultimately became law.

“The language finally forces agencies to quantify waste and technical fragmentation instead of talking about it in generalities. The section restricting bureaus from buying software without CIO approval is also a smart, direct hit on shadow IT. What’s missing is operational realism,” Timsah said. “The bill gives agencies a huge mandate with no funding, no capacity planning, and no clear methodology. You can’t ask for full-stack interoperability scoring and lifecycle TCO analysis without giving CIOs the tools or budget to produce it. My concern is that agencies default to oversized consulting reports that check the box without actually changing anything.”

Timsah said that the bill “is going to be very difficult to implement and to measure. How do you measure it is being followed?” She added that agencies will parrot the bill’s wording and then try to hire people to manage the process. “It’s just going to be for optic’s sake.”

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.

이기열 지사장은 한국 내 킨드릴의 전략적 비전을 수립하고 실행하는 핵심 리더십 역할을 맡아, IT 인프라 및 디지털 전환 분야에서 혁신과 운영 우수성을 강화하도록 킨드릴을 이끌 예정이다. 또한, 통신·금융 등 주요 산업 분야에서의 시장 입지 확대, 전략적 파트너십 구축, 지속 가능한 성장과 미래 경쟁력 확보를 위한 조직 운영을 총괄한다.

이기열 지사장은 30여 년에 걸쳐 기술 및 비즈니스 전환 분야에서 경험을 쌓아온 전문가로, 다양한 산업과 지역을 아우르는 최고경영진 역할을 수행하며 대규모 디지털 전략을 총괄하고 인재 육성과 전략적 파트너십 구축을 주도해 왔다. 복잡한 전환 프로젝트를 성공적으로 이끌고 디지털 생태계를 조성하며 고성과 조직을 이끈 그의 역량은 기술 분야에서 비전 있는 리더이자 전략적 설계자로서의 명성을 입증한다.

킨드릴 동남아시아 및 한국 총괄 앤드류 림은 “이기열 지사장은 공공 및 민간 부문 전반에서 디지털 전략, 클라우드, AI, 대규모 혁신에 대한 깊은 전문성을 갖추고 있으며, 이는 한국 기업들의 디지털 전환 가속화에 중요한 기여를 할 것”이라며, “그의 리더십 아래 킨드릴 한국 지사가 지속적으로 성장하고 새로운 성과를 창출할 것이라고 본다”라고 말했다.

킨드릴 코리아 이기열 신임 지사장은 “한국 기업은 현대화를 가속화하면서, 미션 크리티컬 시스템이 혁신, 회복탄력성, 장기 성장을 어떻게 지원해야 하는지 재정의하고 있다. 한국의 우수한 팀과 파트너들과 협력해 미션 크리티컬 서비스를 제공하고, 전략적 파트너십을 강화하며, 고객의 현대화 목표 달성을 지원할 수 있기를 기대한다”라고 말했다.

이기열 지사장은 킨드릴 합류 전, AI, 블록체인, 양자컴퓨팅 기반 디지털 전환을 전문으로 하는 컨설팅 기업 디지털 닥터 주식회사(Digital Doctor Ltd)의 CEO로 재직했다. 그 이전에는 SK그룹, IBM, PwC컨설팅에서 주요 리더십 직책을 맡았다. 한양대학교에서 산업공학 석사 및 학사 학위를 취득했으며, 하버드 비즈니스 스쿨(Harvard Business School)과 서강대학교 경영대학원에서 고급 리더십 과정을 수료했다. 또한, MIT 슬론 경영대학원(MIT Sloan)에서 기술 관련 고급 과정을 이수했다.
dl-ciokorea@foundryco.com