Jeff Diana's blueprint for CHROs leading AI transformation

HR leaders face a choice: shape how AI transforms work or watch other functions make those decisions for you.

Jeff Diana has guided four companies through major transformations as CHRO, from SuccessFactors' IPO to Calendly's hypergrowth. His perspective cuts through the noise around AI adoption. The opportunity ahead is clear, but so are the risks of waiting.

The task-based approach to AI workforce planning

Most companies reverse-engineer their AI strategy. They deploy tools first, then figure out the business case later. This approach creates exactly the adoption problems CHROs are seeing today.

"We've just said, we've got to slap some AI in our business and there you go, CEO, we're doing something. And then we're going to figure out what we're really going to do after the fact," Diana explains.

The solution starts with task analysis, not job replacement. Diana advocates focusing on specific work tasks rather than entire roles. This approach reduces fear while targeting areas where AI can deliver immediate value. Tasks are building blocks you can iterate on; jobs trigger existential anxiety.

Some organizations can target high-value tasks first. Others should start with lower-risk work to build confidence and experience. The key is picking a path and getting started.

HR's internal marketing problem holds back AI adoption

HR professionals consistently underestimate the marketing challenge of AI adoption. Technical deployment is only half the battle. The other half is painting internal vision for how AI will improve business results and make human work more strategic.

"We are typically horrible marketers and we've got to get better at marketing the work and the why and getting people energized around it especially now because the broader publicity around this has so much fear framing around it we've got to cut through that noise and get people energized around what we're driving," Diana notes.

Trust levels determine which areas to pilot first. If organizational trust is low, start with areas where people feel secure enough to experiment. High-trust environments can tackle more complex implementations from the beginning. Diana explains:

Connections have to come before content People need to understand how AI connects to business goals, personal benefits, and cultural values before they engage with the technology itself.

Speed of decision-making is the new competitive advantage

The speed of decision-making has compressed dramatically. Leaders now need to aggregate large amounts of information, gain insights, and act, all within much tighter timeframes than previous generations of managers experienced. This same principle applies to AI adoption itself: the competitive advantage goes to organizations that start experimenting now and learn from experience, rather than waiting for perfect clarity.

"We've gone from, use some data, get ahead of big trends, like do some of that kind of stuff. In fact, if you sit in an HR chair, we've heard forever: when are we going to get data driven? And we've sort of missed delivering and ringing that bell. Now there's this notion of instantaneous ability to aggregate large amounts of information, gain insights, have those insights be in contact, and then have the scope skills to deal with the implications of those insights. But doing all of that in a compressed window like that," Diana explains.

This shift requires new management capabilities for human-AI collaboration. Context-based coaching becomes essential when experience patterns don't exist yet. No one has managed teams of humans and AI agents before. Traditional training programs can't address this gap because the scenarios are emerging in real-time. Diana notes:

"People need the ability to aggregate that information, gain insights and do it in context and do it in a way where they can personally interact with it with trust and a sense of safety to spar, to learn, to twist, to iterate, but do all of that at a pace that we've never seen before,"

The solution is learning integrated with work, not separated from it. Leaders need the ability to iterate, test hypotheses, and build new patterns while performing their daily responsibilities.

HR has a strategic opportunity to lead transformation

Diana sees this moment as HR's chance to re-cement its role in the C-suite. AI transformation touches every aspect of how work gets done: task redesign, workflow optimization, organizational structure, and skill requirements. When AI agents perform tasks traditionally done by people, the boundaries between "technology decisions" and "people decisions" disappear.

"These technologies are now enabling us and there's so much change in the nature of work that we have the ability to be at the very forefront of that," he says.

Partnership with the CIO becomes critical. AI tools aren't just software; they're becoming autonomous systems that behave more like people than traditional technology. Understanding how to optimize human-AI organizations requires both technical and people expertise.

The alternative is watching other functions absorb HR's traditional responsibilities as they implement their own AI solutions without considering the human side of the equation.

Learning investments can enhance existing systems

Many CHROs worry about justifying new AI coaching investments when they've already spent on LMS platforms, LinkedIn Learning, and human coaches. Diana reframes this as an enhancement opportunity rather than replacement.

AI will increase utilization of existing learning libraries by helping people target content more precisely. Meanwhile, contextual AI coaching delivers immediate business value that motivates longer-term skill development.

"So much of the real learning and value that comes from this comes from in context coaching in the moment to drive performance and to solve problems in the moment," Diana explains. This creates the short-term ROI that makes longer-term learning investments sustainable.

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Five steps to lead AI transformation in your organization

1. Analyze tasks before implementing tools. Map the specific work tasks across your organization rather than focusing on entire job roles. Identify which tasks are repetitive, data-heavy, or time-consuming but low-risk if automated.

2. Assess your organization's change readiness. Evaluate trust levels and AI maturity to determine whether to start with high-value, complex tasks or lower-risk areas that build confidence and experience.

3. Create a compelling vision and marketing strategy. Develop clear messaging about how AI will improve business results and make human work more strategic. Address fear directly by showing concrete benefits rather than abstract possibilities.

4. Design pilot programs with clear success metrics. Choose 2-3 specific task areas for initial implementation. Set measurable goals for productivity gains, time savings, or quality improvements. Plan to iterate and expand based on results.

5. Build contextual learning capabilities. Implement AI coaching tools that integrate with daily work rather than separate training programs. Focus on real-time guidance that helps leaders navigate new human-AI collaboration scenarios.

The organizations that thrive will be those where HR leaders shape AI adoption rather than react to decisions made elsewhere. The window for influence is open, but it won't stay that way indefinitely.

“I don't think there's actually ever been a better time to be an HR professional in terms of our ability to lead and harness all of these changes to drive business impact," Diana concludes.