McKinsey: AI Is Everywhere. Agentic Organization Isn’t Yet.
What becoming an agentic organisation actually requires
Source: McKinsey & Company, AI is everywhere. The agentic organization isn’t yet, April 2026.
Executive summary
Most companies are running AI pilots, but few are converting that activity into enterprise value. In this conversation, Alexis Krivkovich argues that the limiting factor is not model capability. The limiting factor is the redesign required to make an organization operationally compatible with agentic work.
The shift to agentic AI changes what the organization has to manage day to day. Teams are no longer only coordinating people and software. They must also coordinate a growing population of agents, and they must do it with repeatable governance, risk controls, and quality rituals that prevent low quality output from creating downstream rework. This is why the discussion keeps returning to workflows. Scalable impact appears when a workflow is reimagined end to end, rather than improved through point solutions.
Krivkovich connects this workflow redesign to leadership and talent. As agents take on more of the execution layer, humans increasingly operate above the loop, where the job is judgment, oversight, and risk management. That evolution forces role clarity, new skills, and new expectations across the hierarchy, including managers and senior leaders who must model changed ways of working rather than simply experimenting at the margin.
Finally, the episode frames the transition as a cultural and structural program, not a technology rollout. It touches business model disruption in a world of near zero marginal cost, the possibility of flatter decision making structures, and the need for continuous learning and two way door experimentation. The underlying message is that the path to value runs through operating model change, not through more pilots.
Key stats and quotable claims
80% or more of companies say they are not yet seeing bottom line impact from AI investments.
75% of roles need fundamental reshaping right now.
About 50% of leaders say they see skill gaps in their organization.
The great AI paradox
Lucia Rahilly: Alexis, we hear a lot about companies investing in agentic AI without realizing corresponding returns. In fact, our own research shows the complexity of delivering on the promise of agentic AI at any kind of scale. Where are we now?
Alexis Krivkovich: The great paradox is just that. Companies expect massive transformations from AI and have invested with that mindset.
Lucia Rahilly frames the gap between experimentation and outcomes; Krivkovich calls it a paradox:
“Companies expect massive transformations from AI and have invested with that mindset.”
“Yet more than 80 percent of companies say they’re not yet seeing impact on the bottom line from those investments.”
The real question is “How do we meet this moment and create the agentic organization of the future?” In every conversation I have, leaders feel like they’re on the precipice of that question. What they’re trying to think through is really a set of questions:
“How are roles going to change? What are the skills we’ll need in the future? How do I bring along our workforce with excitement, not fear? How do I drive that change so it hits every corner of the organization?”
Lucia Rahilly: The research outlines five pillars that make up the new agentic organization. Let’s start with the first, an organization’s business model. What are some examples that help illustrate the value at stake for leaders?
Business model: near-zero marginal cost and agents on the customer side
Krivkovich pushes a business-model thought experiment:
“Suppose the world could move toward near-zero marginal cost of delivery. How would that change what you’re able to bring to customers, how you’re able to tailor and hypersegment to the unit of one…?”
Then she extends it to the demand side:
“On the receiving end, what if the small business or individual has agents interacting on their behalf?”
“Imagine a customer has an agent that can move money frictionlessly across bank accounts to seek the best rate.”
Her conclusion is that the competitive “moat” itself shifts:
“That fundamentally changes the moat that has existed in financial services since the beginning of time.”
Rethinking teams for the agentic era (operating model shift)
She distinguishes agentic from earlier AI waves:
“The real promise with agentic, relative to generative AI or previous evolutions of AI, is that you can have the equivalent of superhuman capabilities added to your teams.”
But she’s direct that this is not a tooling layer—ways of working must change:
“The day-to-day workflows and the rituals around ways of working will need to fundamentally change.”
“You need to think about how the hours of the day happen differently, the process of overseeing an agent population, how you engage in problem-solving as a team—and put the right governance and risk controls on top of that.”
She also points to structural implications:
“Most companies have added at least one layer to their structure…”
Not only is that expensive, it also slows decision-making because more people weigh in before any decision can be made. There’s hope that AI will enable leaders to have a more superhuman capacity to manage across bigger scopes. That would allow companies to flatten their structure and get faster in the process.
Lucia Rahilly: What should leaders be doing to develop the skills they need both to manage broader spans of talent and to oversee agentic tech?
Skills: reshaped roles and capability building
The claim is broad and time-bounded:
“Just about everybody in the workforce is going to need a new job description in the next two to three years.”
“Most roles won’t go away, but they’ll be reshaped.”
“Seventy-five percent of roles need fundamental reshaping right now. That includes people leading teams and those who report to them.”
On capability building:
“Nearly half of leaders say they think they see skill gaps in their organization.”
So when we ask about capability-building needs, nearly half of leaders say they think they see skill gaps in their organization. And most would say they’d really benefit from more training, more capability building, and more support—all the way up to senior leadership.
What ‘reimagining workflows’ really means
Lucia Rahilly: We talk a lot about reimagining workflows from end to end. What does this look like in practice, and what are the potential implications?
Alexis Krivkovich: The best use cases, where AI is enabling scalable impact, are where a workflow can be reimagined in its entirety. That’s because those workflows typically cut across multiple teams and areas of a company. In a traditional context, there’s a lot of connecting the dots across touchpoints and people. This is where agentic can be incredibly valuable because it can be part of that connection stream in a fast, multifaceted way.
She makes the point that scalable impact comes from redesigning whole workflows:
“The best use cases, where AI is enabling scalable impact, are where a workflow can be reimagined in its entirety.”
“Instead of point solutions… we’re now talking about a whole workflow.”
And she names the granular design questions you have to answer:
“Where does it benefit from having an agent? Where do I need a human in the loop, or above the loop, to supervise? Where do I need a team of agents? How do I make them reusable, so once they’re trained, I can deploy them in multiple places?”
Leadership in the paradigm shift (trust + learning mode)
She describes a trust gap driven by early failures:
“A huge trust gap exists today.”
“The hallucination stories we read in the headlines are real.”
“The slop that moves around a company through poor use of AI can create more work, not less.”
So leadership becomes balancing excitement with risk management while acknowledging maturity:
“How do you acknowledge that we’re in a learning mode rather than a mature state of deployment?”
Lucia Rahilly: There must be a certain amount of resistance that’s fear-driven as well. Folks think they may lose their jobs and be replaced by agentic AI.
Alexis Krivkovich: The technology is not going to stop. There’s that line, you won’t be replaced by AI, but you may be replaced by someone who embraces AI before you do. I think that’s a real question for the employee base. And as a leader, how do I get you excited about new skills, new tooling that could make you more successful? And if that’s not in this role, because this role will transform so dramatically, perhaps it’s in an adjacent role.
When judgment becomes the job
Lucia Rahilly: On that topic, do you see any new talent profiles that you think will be essential? How might employees begin to develop those skills?
The key conceptual shift is explicit:
Having a human in the loop suggests agents are doing pieces of the process, then passing it to a human who does other pieces. Having a human above the loop suggests that if we get to a place where teams of AI agents are able to do most, if not the entire core process, the human’s role becomes judgment on top.
Let me give you an example. We’ve reimagined the American Arbitration Association’s process when a case is sent in for review. The traditional process involves gathering hundreds, if not thousands, of different data points, including photographic exhibits of contracts and email exchanges; reviewing the case file; and deciding on the right answer based on the terms of the agreement. This can take a really long time.
So we asked: “Could we train agents, using closed case files, to put together the timeline, review the fact base, look at both sides of the argument, and come to a summary decision?”
The final human check is:
“Do I agree with the decision the agents have come to?”
On talent development without “grunt work”:
“This is the billion-dollar question.”
“If you eliminate every new software engineer, you have a very expensive model of only senior folks.”
Culture: curiosity, continuous learning, and perpetual change
“Organizations that foster curiosity and continuous learning will be in a great space because so much is still unknown.”
“We need two-way doors, not one-way doors.”
And we’re going to have to get really good at being comfortable in constant change without introducing chaos and risk into the organization.
“Change management is no longer an episodic thing. It’s a perpetual state.”
Talent in a time of change
Lucia Rahilly: Any thoughts on how these changes will affect the shape of an organization’s talent hierarchy?
Alexis Krivkovich: I think it’s too early to have an answer on the final state of the shape of organizations. AI will absolutely enable that.
For example, there’s a lot of excitement about organizing around pods of work as opposed to traditional job hierarchies—pods that form and reform, are reusable, and move across the organization more nimbly. That’s really hard for companies to do in practice. You need a job hierarchy so people can be evaluated and know whom to go to for managerial support.
So I think we’re still pretty far away from seeing most companies reorganize themselves around that principle.
Key takeaways, Chasm compliant
Start with one lighthouse workflow, not scattered pilots. Pick an end to end process that crosses teams and redesign it completely.
Treat oversight and judgment as the new core work. Define what decisions humans must own, what agents can execute, and what gets reviewed.
Design the operating rhythm, rituals, governance, and risk controls for managing an agent population. Do not bolt it on later.
Plan for role reshaping at scale. Update job expectations and capability building plans now, and make learning continuous.
Close the trust gap with disciplined iteration. Run experiments as two way doors, measure quality, and reduce slop that creates downstream rework.
Enable talent mobility. If pods and fluid flows are the goal, invest in the HR and ops mechanisms that make movement safe and fast.
