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Rewiring Talent to Value in the age of AI
As humans enter a new era with AI agents as coworkers, companies need a new framework for defining their most valuable roles.
Generative AI changed how work gets done. Agentic AI is changing who—or what—does the work. Organizations are increasingly creating hybrid workforces in which people and intelligent agents collaborate across workflows. Sometimes agents support people; in other cases, they coordinate with one another while humans oversee decisions, exceptions, and outcomes.
Capturing AI's value will depend on deploying the right people—and the right agents—to the highest-value work. Companies must revisit familiar questions through a new lens: Where is value created? Which roles and systems matter most? And how should organizations combine talent and AI to maximize performance?
For over 30 years, we have helped organizations answer these questions through the Talent to Value framework, which identifies key roles responsible for roughly 80 percent of enterprise value. Matching exceptional talent to these roles consistently produces disproportionate performance.
That logic remains valid. What has changed is where value resides. Increasingly, it is created not by individuals alone but by coordinated systems of humans and AI agents. Competitive advantage will belong to organizations that can continually redesign these systems as technology and sources of value evolve.
1. Continuously redefine the value agenda
AI is reshaping competitive advantage faster than traditional planning cycles can accommodate. Capabilities are becoming commoditized, new business models are emerging, and value pools are shifting rapidly.
Rather than treating strategy as an annual exercise, leading organizations are continuously identifying where AI creates the greatest advantage—through productivity, innovation, customer experience, or entirely new sources of growth—and reallocating talent and AI accordingly.
Johnson & Johnson illustrates this shift. After identifying nearly 900 generative AI use cases, the company found that roughly 80 percent of the value came from only 10 to 15 percent of them. Rather than pursuing broad experimentation, it concentrated resources on the highest-value opportunities.
2. Redefine critical roles—and identify critical agents
AI is breaking apart traditional job descriptions into tasks that can be automated, augmented, or remain human-led. Organizations must therefore move beyond Talent to Value toward "Talent and Agents to Value".
The most important unit of performance is increasingly a coordinated human-agent system rather than an individual role. Leaders should determine which capabilities belong to people, which belong to AI agents, and which require close collaboration between both.
Alongside traditional leadership roles, organizations are creating new positions such as agentic workflow owners, agent operations leaders, and AI governance specialists. At the same time, AI agents themselves are becoming strategic assets whose design, deployment, and governance directly influence enterprise performance.
3. Hire and develop people who amplify AI
The question is no longer simply whether someone is qualified for a role. It is whether they can create substantially more value by working with AI.
Organizations should prioritize people who build AI fluency, redesign workflows, frame problems creatively, exercise sound judgment, and learn continuously as technology evolves.
Building this workforce will require a combination of hiring, reskilling, and selectively using external expertise. But critical AI capabilities should ultimately become institutional strengths rather than outsourced dependencies.
4. Rethink leadership
Talent to Value has always depended on leadership ownership. That responsibility becomes even greater in the AI era.
Executive teams and boards must become sufficiently AI fluent to shape strategy, prioritize investments, redesign work, and make better talent decisions. They must manage dynamic systems of people, AI, workflows, and capital rather than static organizational hierarchies.
Many companies have already begun reorganizing leadership around AI, creating new executive roles and redefining existing ones. These changes reflect a broader reality: organizations are redesigning leadership itself to match new sources of value creation.
5. Measure system performance—not individual output
Agentic AI fundamentally changes performance management. The central question becomes not "Who completed the work?" but "How well did the system perform?"
AI agents should be evaluated on reliability, decision quality, speed, cost, and compliance. People should be assessed on business outcomes, workflow design, responsible AI use, and their ability to improve human-agent collaboration.
Organizations that struggle with AI often do so not because they lack talent or technology, but because they fail to integrate both into coherent operating systems with clear accountability and continuous learning.
Leading companies are already managing AI agents alongside employees, evaluating managers on the performance of entire workflows rather than individual output alone. This represents a fundamental shift from managing people to managing systems.
The Talent to Value framework becomes more—not less—important in the age of AI. But its focus must expand beyond identifying critical roles.
The competitive challenge is now to design AI-native operating systems that combine human judgment with machine execution, continuously adapting as technology and sources of value evolve. Organizations that can rapidly redesign work and redeploy both talent and AI toward the highest-value opportunities will define the next era of competitive advantage.
*Source: McKinsey Insights, 6/2026; Bengi Korkmaz, Sandra Durth, Vincent Bérubé, Lucia Darino.
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