From Talent to Value to Talent and Agents to Value: What Executive Search Must Learn from the Agentic Era
For more than a decade, one framework has quietly shaped how the best-run companies think about leadership: identify the 30 to 50 roles that drive roughly 80 percent of enterprise value, define what success looks like in each of them, and place the right people there. McKinsey calls it "Talent to Value," and it has been a reliable compass for boards, CEOs, and executive search professionals alike.
EXECUTIVE SEARCH
For more than a decade, one framework has quietly shaped how the best-run companies think about leadership: identify the 30 to 50 roles that drive roughly 80 percent of enterprise value, define what success looks like in each of them, and place the right people there. McKinsey calls it "Talent to Value," and it has been a reliable compass for boards, CEOs, and executive search professionals alike.
That compass still works. But the terrain it describes is shifting under our feet.
A recent McKinsey article, "Rewiring Talent to Value in the Age of AI" (June 2026), makes a claim worth sitting with: value is no longer created by roles alone but by dynamically orchestrated systems of humans and agents, and the performance gap between the best and the rest is widening fast. For anyone working in C-level search, board advisory, or succession planning, this is not an abstract observation. It changes what we are actually looking for when we evaluate a candidate.
The Familiar Framework, Under New Pressure
The original logic was straightforward. A small number of roles, often clustered near the top of the organization, account for a disproportionate share of value creation. McKinsey's research has long suggested that 5 to 10 percent of these critical roles report directly to the CEO, with the rest distributed two to three levels below. Executive search has built much of its discipline around finding and placing the right individuals into exactly these positions.
What has changed is not the existence of critical roles, but their composition. Work itself is being unbundled. Some tasks are now fully automated, some are augmented by AI agents, and some remain firmly human. As McKinsey puts it, leaders must shift their perspective from "Talent to Value" to "Talent and Agents to Value," because in many cases the real unit of value is no longer a single role but a coordinated system of people and agents delivering outcomes together.
This is precisely the kind of shift that gets missed in a purely technical reading of AI adoption, and precisely the kind of shift that organizational analysis is built to catch. Org charts do not yet show "agent factories" or human-agent workflows, but they exist, and they are reallocating where value actually sits inside a company.
New Roles Are Already Hiding in Plain Sight
One of the more useful observations in the McKinsey piece is also the most overlooked by traditional search mandates. New roles are emerging, including agentic workflow mission owners, agent operations platform leads, and agent governance liaisons, and these roles are often hired deep inside the organization rather than at the visible top of it. They are easy to miss precisely because they do not look like C-suite positions on paper.
This matters enormously for how we scope a search. A CIO or Chief AI Officer mandate gets the attention. But the person who actually designs how a claims-processing agent and a human underwriter hand off work to each other, or who governs how an agent escalates exceptions in a regulated workflow, may be sitting two or three levels down, invisible to a search process built around the old org chart logic. If we keep evaluating candidates purely against legacy success profiles, we will keep failing to identify exactly the roles that the McKinsey framework tells us drive the most value.
The implication for board advisory and succession planning is direct: a current talent map of "critical roles" is incomplete if it has not been re-examined since agentic workflows entered the business. This is not a one-time audit. It is a recurring exercise, because as the article notes, agents themselves vary widely in the value they create, and that calculus changes as the technology matures.
What "Fit" Means When Knowledge Is No Longer Scarce
The second shift cuts even closer to the core of what executive search does. Traditionally, we evaluate candidates against a defined set of knowledge, skills, attributes, and experience. McKinsey's framing suggests this is necessary but no longer sufficient, because knowledge is becoming more accessible via intelligent systems, while experience can lose relevance as work itself changes.
The real differentiator becomes something harder to assess on a CV: how much can this individual amplify value when working with AI. McKinsey describes a category of "AI super users," individuals who use AI systems to do work that previously required entire teams, and the company points to Meta as an example of an organization that is now defining the skills needed and the expected AI-driven impact for each critical role, then assessing employees against that bar.
This is a genuinely different interview question. It is not "tell me about your experience leading a digital transformation." It is closer to: how do you reimagine a workflow when an AI agent can already do half of it, and what judgment do you apply to the half that's left? Candidates who can build AI fluency in their teams, reframe business outcomes through an AI lens, and take accountability for decisions made in collaboration with agents are increasingly the ones who outperform peers with longer, more conventional résumés.
For search professionals, this means success profiles need an explicit AI-amplification dimension, not as a checkbox skill but as a genuine differentiator in the assessment. It also means being more deliberate about the "buy, build, borrow" question McKinsey raises: not every critical capability should be filled externally, and over-reliance on borrowed talent for AI-critical roles tends to produce fragile operating models that struggle to scale.
The Top Team Problem Nobody Wants to Name
Perhaps the most uncomfortable point in the McKinsey article is also the most relevant to board advisory work: many executive teams still lack the AI fluency needed to define a coherent value agenda or make sound talent decisions in this environment. McKinsey notes that in the past eighteen months, a wave of Fortune 500 companies have reorganized their leadership teams specifically to sharpen AI strategy focus, sometimes alongside executive departures.
This is rarely a technology problem at its root. It is a leadership and organizational design problem, and it is exactly where succession planning and HR due diligence earn their value. When a private equity fund is assessing a portfolio company's leadership ahead of an acquisition or exit, the question can no longer stop at "does this team have the right experience." It needs to extend to: can this team credibly own an AI-driven value agenda, or will the organization need a refresh at the top before AI investment translates into AI-driven performance.
A Fifth Step: Judging the System, Not Just the Individual
McKinsey adds a fifth step to the original framework that search professionals should take seriously, even though it sits slightly outside our traditional remit: performance management itself is shifting from "who did the work" to "how well did the system perform." Agents get evaluated on decision quality, reliability, speed, and cost. Humans get evaluated on business impact, their ability to improve AI-enabled workflows, ethical use of AI, and collaboration.
This reframing has a quiet but important consequence for executive search: when we assess a candidate for a senior operating role, we should increasingly be asking how they have managed a system of people and agents, not simply how they have managed a team. The McKinsey article describes a global financial institution where managers are evaluated not on individual output but on how reliably and how well the overall human-agent workflow performs. That is a fundamentally different leadership competency, and one that most legacy success profiles do not yet capture.
What This Means in Practice
None of this invalidates the Talent to Value logic that has guided executive search for years. If anything, it raises the stakes on getting it right. The 30 to 50 roles that drive most enterprise value still exist. But identifying them now requires looking past the org chart, into the agentic workflows where value is increasingly created and where some of the most important new roles are quietly forming. Evaluating candidates now requires going beyond a traditional success profile, into how much value an individual can amplify when paired with AI. And advising boards now requires an honest conversation about whether the top team itself has the fluency to own this shift, rather than simply delegate it.
Organizations that get this right will not be the ones with the most AI tools. They will be the ones that can rapidly identify where value is migrating, place the right combination of people and agents against it, and keep redesigning that combination as the technology and the competitive landscape continue to move. That is, in the end, still a talent question. It is simply a larger and more dynamic one than the one we have been used to answering.
Silvio Fontaneto is Senior Partner, Beaumont Group, leading the Tech & Digital Executive Search practice in Italy, with a strategic advisory focus on AI strategy, board advisory, and organizational transformation. Author of "Stop Fearing AI" and the "Vector" trilogy.
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