The Biggest Delivery Leadership Challenges of 2026
AI Integration: AI is a current pressure for project leaders, with adoption often lacking adequate strategy and readiness.
Data Challenges: Unstructured, biased data undermines AI effectiveness, demanding better data governance and literacy.
Human Concerns: AI implementation creates team anxiety regarding job security, requiring leaders to manage trust effectively.
Expert Paradox: Deep expertise remains essential for effective AI use, as AI depends on human mastery for value.
Execution Focus: Project success often falters in execution due to multitasking and work overload, not poor planning.
Something is different about delivery in 2026. It is not just that the tools have changed, or that the pace of change has quickened — it is that every major force disrupting how organizations operate seems to have arrived at once. Artificial intelligence has moved from curiosity to operational expectation in the span of a few years. Geopolitical instability is reshaping markets and supply chains. Delivery pressure has intensified even as teams are leaner and more dispersed than ever. And through all of it, the humans responsible for making projects actually happen are being asked to navigate terrain that no playbook has fully mapped.
To understand what this moment really demands, we spoke with several delivery leaders across industries. Here are their thoughts.
The AI Reckoning — Opportunity, Anxiety, and Unanswered Questions
Artificial intelligence is no longer a future consideration for project managers — it is a present-tense pressure. But across our conversations, a consistent tension emerged: enthusiasm for AI has significantly outpaced the organizational readiness to use AI well. The question is no longer whether to adopt AI, but how to do it responsibly, and what happens when organizations skip the foundational work required to make it actually work.
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The Pressure to Adopt Without a Foundation
The demand to "use AI" is arriving from the top of organizations with urgency, but often without the strategy to back it up. Marcus Glowasz, Executive Coach & Advisory, Change and Project Communications, at Projects & Data, described a pattern he sees repeatedly: "I hear often, ‘We have to use AI. Because everybody uses it, we need to.’ And then everybody jumps on it. But maybe there is a better solution than AI."" The pressure is real, but the direction is frequently premature.
I hear often, ‘We have to use AI. Because everybody uses it, we need to.’ And then everybody jumps on it. But maybe there is a better solution than AI.
This top-down pressure is compounded by a lack of consultation with the people who actually manage delivery. Emmanuels Magaya, founder of Project Managers Africa, described a scenario he has encountered countless times: "AI often comes from a company-wide level, they tell you all to start using this tool. It's coming from the top like a helicopter...But, nobody consulted the PMO team." The result is implementation without fit — and failed adoption that breeds skepticism.
AI is often coming from the top like a helicopter…but nobody consulted the PMO team.
The same pattern shows up at the project level. Derek Fredrickson, Founder of the COO Solution, argues that the sequencing is fundamentally backwards in many organizations: "Oftentimes they [leaders] try to initiate the AI as the solution before a human has actually done it... What we need to get really clear on what the human is doing first to see where can AI add value as opposed to, 'I don't have a function or I don't have a thing yet, but I just want AI to do it.'" Before AI can be layered in, he says, the human process has to be understood and documented.
Oftentimes they [leaders] try to initiate the AI as the solution before a human has actually done it. We need to get really clear on what the human is doing first to see where can AI add value.
The Data Problem Underneath the AI Problem
Beneath the AI adoption question lies a more fundamental one: is the underlying data good enough to make AI useful? Bruno Morgante, Founder & CEO of Mantegora, is direct about the issue he sees with AI in portfolio work: "the biggest problem right now is the fact that the starting data is not so clean. So we have unstructured, unclean, biased data. And when you put that together, asking an AI tool to help you with prediction...the result is not going to be that good." Garbage in, garbage out — the oldest rule in data still applies, even in the age of large language models.
We have unstructured, unclean, biased data. And when you put that together and ask an AI tool to help you with prediction…the result is not going to be that good.
For Laurel Sim, Managing Partner and President at Taleo Project Services, Inc., the absence of clear boundaries around where and how AI can be used is one of the defining leadership challenges of the moment. "The speed of AI and how we have no guardrails to box-in where AI is acceptable practice," she says, is among her greatest professional concerns — and organizations without strong data governance are, in her view, acutely exposed.
Marcus Glowasz notes there is also a data literacy issue that needs to be addressed before any serious AI rollout: "There's data literacy that needs to come in. You need to understand the concepts around the data. If you have data you do not understand then...what use do you have of some real advanced AI tools?" Data governance coupled with a human understanding of the data being used is a prerequisite, not a nice-to-have for effective AI usage.
The Human Dimension: Teams, Trust, and Job Fear
Even when AI is being integrated thoughtfully, there is a human management challenge that leaders say is underestimated: the anxiety it creates in teams. Pam Butkowski, SVP, Delivery at Horizontal Digital, identifies this as the real leadership test: "I don't think that understanding or figuring out how to integrate AI tools and methods into our ways of working every day is actually the challenge. From a leadership perspective, the challenge for me has been encouraging my team to or finding ways to get my team to flex other muscles as we're pulling these AI tools in... And explaining and instilling confidence in the team members that AI is not going to take your job." The technical integration, she suggests, is almost the easier part.
The challenge for me has been encouraging my team to or find ways to get my team to flex other muscles as we’re pulling these AI tools in… And explaining and instilling confidence in the team members that AI is not going to take your job.
Bill Dow, Director, Enterprise PMO at UW Medicine, connects this anxiety to a broader environmental reality that leaders cannot ignore: "I think in 2026 you have to be aware of the environment that we're in, all the different layoffs due to AI, everything happening in the world and then settle in and say, how do we stay on top of that? How do we stay in front of that?" The question for delivery leaders is not only how to use AI, but how to lead teams that are afraid of what it means for their futures.
In 2026 you have to be aware of the environment that we’re in, all the different layoffs due to AI, everything happening in the world, and settle in and say, how do we stay on top of that?
The Expert Paradox — AI Needs Human Mastery to Deliver Value
There is a paradox at the heart of AI adoption that Markus Kopko, CPMAI Lead Coach, ALVISSION, articulates: "What most people have not realized yet is that you need to be an expert in your domain to get good results out of AI." The implication is significant. AI does not reduce the need for deep expertise — it depends on it.
Kopko goes further, raising a generational concern: "The problem we are going to face in the not so far future, I would assume, is that if younger people are no longer getting that knowledge and becoming experts over time, how should they be able to assess and review the results of the AI outputs?"
AI does not reduce the need for deep expertise. It depends on it.
Keeping Up — The Pace of Change Is Outrunning People
Artificial intelligence is the most visible accelerant, but it is not the only one. The experts we spoke with are navigating a much broader velocity problem: technological change is moving faster than most people and organizations can absorb, macro forces are generating new uncertainty, and the expectation of faster delivery is colliding with the reality of human limits. The challenge of keeping up is not just about learning new tools — it is about leading people through sustained disorientation.
Technology Velocity and Learning Debt
Kopko describes the pace as something relatively new: "[the biggest challenge] is getting up to speed with and integrating the rapid development of technologies. The AI space was relatively new for many people just a couple of years back. And now we use it basically on a daily basis. Now, we need to integrate AI literacy, data literacy, and learn things much faster than we used to do before."
The profession is not struggling to understand AI in theory — it is struggling to keep pace with how fast AI itself is evolving. What was current practice six months ago may already be outdated, and organizations are perpetually in a state of catch-up.
The Macro Environment: Politics, Markets, and Uncertainty
The velocity challenge extends well beyond technology. Laurel Sim names the macro environment as one of her two most pressing concerns for the profession: "my greatest concern is the political environment of the country, the continent, and the world and how we all have to evolve in where our markets are." Geopolitical instability, shifting trade dynamics, and economic uncertainty are not abstract concerns for delivery leaders — they translate directly into shifting priorities, disrupted supply chains, and projects that begin under one set of assumptions and must be completed under entirely different ones.
My greatest concern is the political environment of the country, the continent, and the world and how we all have to evolve in where our markets are
The Human Skills Crisis — Critical Thinking, Execution, and Team Dynamics
Amid all the noise about technology, the experts we spoke with returned again and again to a quieter, slower-moving crisis: the erosion of the foundational human skills that no AI can replicate.
Critical Thinking and Communication Skills Are Emerging as Table Stakes Skills
When asked which skills matter most for the next generation of project leaders, Laurel Sim didn’t hesitate: "Critical thinking," she says. "So that we're thinking and being creative...if we're not trying to encourage that strength, we are missing out." The concern is not simply that critical thinking is underdeveloped — it is that we are actively failing to cultivate this skill as it becomes increasingly important in the AI era.
Michael Gold, Founder & Fractional Head of Delivery, connects this shift directly to how AI is reshaping the PM role itself: "saying you are process-orientated as a project manager is meaningless, because who isn't process-oriented with AI. With AI there's even more of a focus on the human side of things, the relationship management, stakeholder management, persuasiveness, communication skills...AI can't replace that one-to-one human interaction." As AI absorbs the process-heavy parts of the role, what remains — and what matters most — is irreducibly human.
With AI there’s even more of a focus on the human side of things, the relationship management, stakeholder management, persuasiveness, communication skills.
Multitasking, Overload, and the Inhumane Workplace
One of the most consistent themes across our interviews was the damage done by organizational overload — the practice of assigning more work to people than they can reasonably absorb. Bill Dow describes it plainly: "managers put 10 projects on people... And what happens is you just get into this constant shifting from project to project to project, right? And no human body can do that." The result is not productivity — it is the illusion of productivity, masking deeper organizational issues.
Johanna Rothman, Owner, Rothman Consulting Group, takes the critique further, framing multitasking not just as a practical problem but an ethical one: "The reason I am so offended by multitasking is because it takes the humanity out of the work. Instead, we have bits and pieces of people in a spreadsheet. So multitasking arises from thinking that we can divide and conquer, making an inhumane workplace." In other words, when we treat people as fractional resources spread across parallel workstreams, we are not just reducing efficiency. We are creating the conditions that make meaningful, high-quality work harder to achieve.
Johanna Rothman also questions the way organizations plan work. Many teams build long backlogs and roadmaps that promise delivery every two weeks, but she argues that level of certainty is unrealistic: “I don't know how long things will take and I have a lot of experience with my outcomes. And if I, after 30 years of outcome-based experience, cannot predict what I am going to do over the next week, how can any team do that?”
Her point is that overload and overcommitment are often built into the system itself, leaving delivery leaders to deal with the fallout.
If I, after 30 years of outcome-based experience, cannot predict what I am going to do over the next week, how can any team do that?
The Road Ahead
The challenges described by these leaders are not isolated. They are interconnected. AI is transforming how projects are planned and delivered, but the governance frameworks, data infrastructure, and human development pipelines needed to support that transformation are lagging behind. The leaders who will navigate 2026 most successfully are those who understand that the hardest problems are still, at their core, human ones.
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