Gen AI changed how work gets done. Agentic AI is changing who—or what—does the work. As organizations adopt AI agents across workflows, they are creating a new kind of hybrid workforce in which people and intelligent systems operate side by side. In some cases, agents support humans. In others, agents coordinate with other agents while humans oversee decisions, exceptions, and outcomes.
Capturing the value of AI will depend on having the right people—and the right agents—in 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 deploy talent and AI to maximize performance?
This challenge represents a new frontier in talent strategy. Over the past decade, we have worked with hundreds of companies across industries to apply a framework known as “Talent to Value,” which helps companies identify the 30 to 50 critical roles that drive about 80 percent of value. By defining the success profile for each role and matching top performers to those roles, companies can generate significant impact. Roughly 5 to 10 percent of those roles, which can deliver 2.5 times more value than an average corporate role, report directly to the CEO, with 30 to 40 percent two levels below the CEO and 50 to 65 percent three levels below the CEO.
This framework and logic remain highly relevant in the AI era. But value is no longer created by roles alone. It is increasingly created by dynamically orchestrated systems of humans and agents—and the performance gaps between the best and the rest are widening rapidly. Organizations must rethink not just who creates value, but how value is created and how quickly it shifts.
In this article, we revisit the four steps of the original Talent to Value framework and outline how they must evolve in an AI-powered world. We also introduce a fifth step focused on managing system performance to fully capture value.
Step 1: Define the value agenda—and continuously adapt
Companies have traditionally defined their value agenda by mapping ambitions and targets across business units, product lines, and functional domains. In the AI era, however, the foundations of value creation are shifting rapidly, as technological advances commoditize capabilities and reshape competitive advantage.
Leaders must determine where AI can add disproportionate advantage—whether through cost, innovation, productivity, or entirely new business models. They should develop scenarios to assess how AI could reshape their industry, customer expectations, and competitive landscape.
But scenario planning alone is not enough. Value pools are shifting faster than traditional planning cycles can keep up. Leading organizations are beginning to treat value allocation as a continuous process, dynamically tracking where AI is creating or eroding advantage and rapidly redeploying talent and agents toward emerging opportunities. This requires a more adaptive operating model—one capable of continuously reassessing where value resides and redeploying critical resources accordingly. For example, Johnson & Johnson identified nearly 900 gen AI use cases and found that 80 percent of the value came from 10 to 15 percent of those initiatives.1 The healthcare company shifted away from broad, long-range experimentation to continuous strategic planning, focusing on use cases that yield the most value.
Step 2: Identify and clarify critical roles—and agents
Defining critical roles and assigning one individual to each role has been a clear, if challenging, process. It’s harder now as AI disrupts static, rigid roles on organizational charts. With work increasingly broken into tasks—some automated, some augmented, some still human-led—leaders must shift their perspective from “Talent to Value” to “Talent and Agents to Value.”
For the highest-value areas, organizations should identify which capabilities, skills, and tasks are needed—then decide whether humans, agents, or hybrid systems are best positioned to perform them. In many cases, the true unit of value is no longer a single role, but a coordinated system of humans and agents delivering outcomes together.
AI-enabled operating models also elevate the importance of several roles, including domain leaders who own AI-driven outcomes, AI product owners, architects who design human–agent workflows, prompt engineers who propose and route models, and data and knowledge specialists who ensure agents operate with trusted context.
These roles are not new but have grown in importance given their link to value creation and enablement. Entirely new roles are also emerging, including agentic workflow mission owners, agent operations platform leads, and agent governance liaisons. These roles are often hired deep within an organization and can be easily overlooked as critical roles.
As organizations scale AI, these roles increasingly operate in coordinated groups—often within “agent factories” that handle end-to-end workflows. In this context, talent pools become as important as individual roles.
Much like organizational capabilities and roles need to be prioritized, so do agents. Some agents will create significantly more value than others, and this calculus will evolve as technology improves. Agents could range from claims processors in insurance to patient trial companions in pharmaceuticals to deal-sourcing relationship managers in banking. Their design, deployment, and governance are strategic priorities.
Step 3: Match talent to roles—and assess rigorously to maximize AI-enabled value
In the traditional Talent to Value approach, individuals would be evaluated against the specific knowledge, skills, attributes, and experience requirements of a critical role. Different value equations defined what constituted a “good fit.” While these factors remain relevant, they are no longer sufficient in a world where roles, workflows, and technologies are evolving rapidly. Knowledge is becoming more accessible via intelligent systems, while experience can lose relevance as work itself changes.
The goal is no longer simply to match talent to roles, but to determine how much individuals can amplify value with AI. This is where AI “super users” can come in. These are individuals who use AI systems to handle work that previously required teams to complete. Organizations can develop such talent by improving their baseline assessments of existing employees and prioritizing individuals who can do the following:
- build AI fluency within themselves and their teams
- reimagine business outcomes, processes, and workflows through AI
- apply problem framing, creativity, and judgment to mobilize change
- make decisions and take accountability
- learn continuously and apply new knowledge
For example, Meta is redefining role and talent expectations and tying performance to AI-enabled impact.2 The tech company is now defining the skills needed and the expected AI-driven impact for each critical role and assessing how employees build and use AI tools to create value and boost productivity. This reflects a recognition that broad AI access does not automatically translate into differentiated performance and that a relatively small group of individuals is significantly more effective at integrating AI to outperform their peers.
But not all “top talent” will exist within the four walls of an organization today. Leaders likely will need a mix of “buy, build, borrow” strategies—that is, recruiting new talent, training existing employees, or using external workers or consultants—to find such talent in a competitive market. Organizations should avoid overreliance on borrowed talent for critical AI capabilities, which can create fragile operating models that struggle to scale or sustain value over time.
Step 4: Operationalize and mobilize—and take a fresh look at your top team
The Talent to Value approach has always depended on leaders owning the process and managing talent to help them deliver value. In the AI era, this becomes even more critical. However, many executive teams still lack the AI fluency needed to define a coherent value agenda, prioritize critical roles, and make effective talent decisions.
Top teams and boards must therefore become AI literate, hands-on, and willing to challenge legacy assumptions. They must act as evangelists and manage dynamic systems of talent, AI, workflows, and resource allocation—not simply oversee organizational hierarchies.
In the past 18 months, many Fortune 500 companies have reorganized their leadership teams to sharpen their focus on AI strategy. In some cases, this has coincided with restructuring initiatives and executive departures. In other instances, companies have brought in AI talent to take over existing roles. These changes signal issues ranging from differing views on the company’s AI-driven direction to misaligned skills to dissatisfaction with performance. The clear takeaway is that more companies will do whatever it takes to match talent to value creation as they navigate their AI journeys.
Step 5: Focus on system performance
Agentic AI creates a fundamental shift in performance management. In an agentic organization, the focus moves from “Who did the work?” to “How well did the system perform?” Performance is no longer attributable to individuals alone but to the effectiveness of coordinated human–agent systems.
Organizations will need distinct but complementary performance models. Agents should be evaluated based on decision quality, reliability, speed, and cost. Humans should be evaluated based on business impact, their ability to define and improve AI-enabled workflows, ethical use of AI, and collaboration across teams.
Many organizations struggle not because they lack strong talent or advanced AI tools, but because they fail to integrate them into coherent systems with clear accountability and feedback loops.
For example, a global financial institution is actively managing AI systems alongside its human workforce. The firm’s managers oversee agents, monitor their performance, and continuously refine how they interact with employees and workflows. Managers are increasingly evaluated not simply on individual output but also on how effectively the overall workflow performs: how quickly work moves, how reliably decisions are made, how well humans and agents collaborate, and how continuously the system improves. Meanwhile, agents are assessed on operational metrics such as speed, accuracy, reliability, and compliance.
Leaders must manage these systems with the same rigor applied to capital allocation. They should rigorously track where value is created, prioritize better and continuous visibility into individual and team productivity, identify gaps, and manage internal talent marketplaces powered by data to reallocate resources as needed.
Talent to Value will become more relevant in the AI age. The challenge for leaders is no longer simply identifying the highest-value roles. It is designing AI-native systems that combine human judgment and machine execution to create disproportionate value—and continuously adapt as that value shifts. This changes workforce planning, performance management, and organizational design. Competitive advantage increasingly will belong to organizations that can rapidly redesign how work gets done and continuously redeploy talent and AI toward the highest-value opportunities. The companies that succeed will build operating systems in which humans and intelligent agents learn, adapt, and perform together.
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