When a large tech company first launched its AI agent just over a year ago, the CHRO was blown away by the results. The output was better than most of the company’s HR business partners and culture leaders, and the company moved fast to implement it, launching in six weeks. With two-thirds of the company having no access to an HR business partner at all, the impact could be great.

That was then. Today, twelve months later, the CHRO told a room full of peers at a recent M1 CHRO Community gathering that the company has gone from that single agent to some 15 different agents across the organization. Engineering teams use them for design, communications teams have their own tools, and other departments continue experimenting with new applications. What started as an exciting innovation quickly became a new management challenge.

This trajectory—from cautious experimentation to rapid proliferation to the urgent need for governance—is increasingly a challenge for HR executives who’ve attended recent M1 meetings in Boston, Paris, Seattle, Toronto, and New York. They’re discovering that implementing artificial intelligence successfully requires more than deploying smart tools. It demands rethinking fundamental assumptions about how work gets done, how talent develops, and what it means to manage a workforce.

The Governance Imperative: When Innovation Becomes Chaos

In entrepreneurial companies where teams are able to add their own tools, AI agents can start popping up everywhere, one CHRO shared. At that firm, what looked like healthy innovation quickly started consuming resources without clear returns. They formed a cross-functional board for integrated AI strategy, requiring all new agent deployments to pass through a governance review.

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The rationale is practical: Once you deploy these agents, there's ongoing costs—maintenance costs, data in the clouds—that adds up quickly. Without governance, companies face duplicative efforts and an inability to track return on investment. A fundamental question, as one CHRO put it: Is this delivering a productivity lift, or just longer lunches?

This measurement challenge cuts to the heart of AI adoption. Companies need rigorous frameworks to distinguish genuine productivity gains from employees simply having more free time. For CHROs, this means developing new capabilities in analyzing AI's impact across different functions and roles.

Managing the Digital Workforce: New Questions for HR

As AI agents become more sophisticated and prevalent, CHROs find themselves managing what one executive in an M1 meeting called a digital workforce alongside their human employees. This creates entirely new questions that traditional HR frameworks don't address: What's the process to onboard an agent? How do you do performance assessment for AI? How do you decide between hiring humans versus deploying agents?

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The shift is already changing resource allocation conversations. In the past, a department would ask for another headcount. Now leadership might respond: you don't get three positions—you can use two agents and maybe you get one person, a CHRO said. This requires new frameworks for workforce planning.

One CHRO described actively searching for organizations that have solved digital workforce management, hoping to learn from their experience. The reality? Few companies have figured it out yet. Everyone is building these capabilities in real time, sharing insights but still navigating largely uncharted territory.

The Entry-Level Paradox: Protecting Tomorrow's Talent Pipeline

Perhaps no topic generated more emotional discussion than the impact of AI on entry-level positions. The concern is specific: if a company eliminates 20,000 jobs in the next three years and they're all coming out of the bottom, you wake up three years later and don't have that pipeline. The immediate cost savings are obvious, but the long-term implications for leadership development and organizational capability are potentially devastating.

Some CHROs noted that past hiring slowdowns—the 2001 dot-com crash, the early months of Covid—have prompted leadership vacancies that have reminded them how detrimental it can be to not hire enough entry-level talent. When that happens, “how do you establish wisdom?” one CHRO asked. “It's like you don't have these people to continue to build the pyramid.”

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Companies that historically hired many new college graduates, such as professional or financial services firms, face a particular dilemma: Should they give new hires AI tools from day one, or require them to go through manual apprenticeship learning for the first two years so they have a point of reference for what AI is producing? The answer affects not just current hiring but the entire talent pipeline.

One CHRO pointed to a recent podcast that interviewed a futurist, who described how the medical field has long hired entry-level doctors, even though their utility and productivity is low in their early years. “We still pay them minimally to apprentice and the government actually supports it,” she said about young physician trainees. “Is there a world in which we go back to almost like an apprenticeship? A Benjamin Franklin and the printer press model? I thought that was interesting that there's still value in people gaining experience, even if they're not adding value.”

The Three Pillars: Strategic AI Integration

Companies with tech-savvy CEOs who lean into AI are pursuing implementation across three strategic pillars, as one CHRO explained. First: what does it mean for our products? This is often the biggest opportunity. Second: how can we use AI to improve the customer journey? Third: how do we deploy it within operations and enterprise functions?

This structured approach requires tight partnerships between HR and technology leaders. One company built a platform where all agents—whether third-party or internally developed—can be accessed by employees. This centralized approach helps prevent the fragmentation that occurs when every department independently selects and deploys its own tools.

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The partnership extends beyond technology deployment to workforce readiness. Companies are grappling with fundamental questions about their labor pyramid, hiring strategies, and development programs. These aren't technology questions—they're strategic workforce questions that require HR and business leadership to work in lockstep.

Lessons from Early Adopters: The Nadia Experience

Companies that piloted AI coaching tools like Valence’s Nadia report remarkable reception from users. One organization tested it first with their engineering group—about thirty to forty people—then took it away after the pilot. The engineers' feedback was striking: “When are we getting it back?” they wanted to know. One CHRO described a pilot participant who even said “my wife is jealous of Nadia because I talk about her all the time.”

What makes these tools so effective? They remember every conversation you've ever had and everyone you've ever talked about. When companies embed them, they can teach the agent their leadership frameworks, values, and policies. Companies can set guardrails around sensitive topics—for instance, whether the agent can discuss how to terminate an employee. The default is typically no, but large organizations with hundreds of thousands of employees might choose differently, recognizing the value of having an AI coach help a frontline manager handle a difficult situation in real time.

One CHRO described using Nadia when needing to give hard feedback. The tool knows the user's tendencies—you move fast, you're very direct, you're super compassionate. It offers three different approaches and asks which one you prefer, then follows up a week later to see how the conversation went.