AI agents are here, and they are reshaping how work gets done. However, scaling them isn’t just a technical challenge, it’s an organizational one. In this Future of Work episode, Workday’s David Somers sits down with Dan Priest, chief AI officer at PwC, to explore how forward-thinking enterprises are integrating agentic AI across business functions.
Audio also available on Apple Podcasts and Spotify.
AI agents are the latest trend in workplace transformation, moving from theoretical concepts to practical applications with real business value. Yet most leaders are grappling with where it fits, how it scales, and what new expectations it sets for teams, talent, and trust. The real challenge isn’t the technology, it’s building the organizational muscle to use it well.
That’s exactly what Workday’s David Somers, and Chief AI Officer at PwC, Dan Priest, unpack in this episode of the Future of Work podcast. Below are key takeaways from the episode.
Unlike advancements to language learning models (LLMs,) AI agents aren’t just an upgrade to automated processes. They represent a fundamental shift in how work gets done. Unlike traditional bots or scripts that execute rigid tasks, AI agents are autonomous applications designed to operate within defined parameters to accomplish complex goals. They don’t just follow instructions. They interpret context, collaborate with other agents, and adapt based on outcomes.
What makes them different is their ability to function independently within a system. You give them an objective, a dataset, guardrails, and expectations, and they figure out how to deliver the result. That level of autonomy changes everything about how we think about roles, productivity, and decision-making.
This isn’t just a future concept. It’s already playing out in organizations that are embedding agents into day-to-day workflows—from software engineering to customer support to talent acquisition.
Adoption is accelerating. In early use cases, a PwC report found that AI agents are already delivering productivity and speed-to-market gains of 50% or more. Some organizations have reduced software development cycles by up to 60%, while cutting production errors in half. Most companies are actively planning or piloting agentic AI across critical business functions. But there’s a gap. While the technology is moving fast, many teams are still catching up—organizational readiness, role clarity, and change management remain common bottlenecks.
The challenge isn’t just technical, it’s organizational. Employees aren’t sure how AI will affect their roles, their careers, or their long-term value. That uncertainty leads to hesitation, resistance, or worse: disengagement. And that makes leadership clarity essential. Leaders who can clearly communicate the “enduring human value proposition” will win trust faster, and get better results from both humans and machines.
Priest defines this enduring human value proposition as the skills AI can’t replicate: first-principles thinking, creative problem-solving, empathy, collaboration, and the ability to lead through ambiguity. When leaders design roles, teams, and strategies that center these strengths rather than sideline them, they give employees a clear stake in the future.
Another consideration: not every department is equally ready for AI integration. PwC refers to this as the “jagged frontier”—some functions are ahead, some are behind. Successful implementation requires understanding where you are on that curve, prioritizing wisely, and making change manageable rather than overwhelming.
In some use cases, AI agents are already delivering productivity and speed-to-market gains of 50% or more.
PwC’s own transformation provides a clear example of what’s possible. The company spends over 18 million hours annually on software development and deployment. That’s high-stakes, high-cost work. When they applied AI agents to code generation, they were able to redefine timelines and cost structures.
Instead of asking a single tool to write lines of code, they deployed a coordinated team of agents. One created functional specs, another built the technical configuration, a third handled code generation, and others managed testing and deployment. Each agent operated with purpose, and together they delivered 30–40% cost savings and timeline acceleration.
The same multi-agent approach is now being applied in customer service. Agents are helping triage tickets, resolve recurring issues, and surface relevant insights for human reps. These aren’t chatbots reading scripts. They’re collaborators identifying patterns, flagging anomalies, and learning from every interaction to improve the next one.
The pattern is clear: when you assign agents thoughtfully and design them to work together—like a human team would—you get real, compounding value.
Each agent operated with purpose, and together they delivered 30–40% cost savings and timeline acceleration.
As AI agents take on more of the execution, the differentiators in your workforce shift.Intelligence alone stops being a competitive advantage when every team has access to advanced reasoning tools. What rises in importance are traits that machines can’t replicate: creative thinking, emotional intelligence, first-principles problem solving, and the ability to lead others through change. These human strengths are where organizations will find their edge.
AI will certainly boost productivity, but it can also amplify creativity when paired with the right people. Individuals who are curious, critical thinkers, and fast learners will thrive. And those who can ask better questions, not just find better answers, will lead.
What this means for talent strategy is clear. Leaders need to invest now in developing these skills, especially among high-potential employees. The ROI isn’t just individual growth—it’s the ability to unlock the full value of AI investments by pairing them with empowered, adaptive humans.
What rises in importance are traits that machines can’t replicate: creative thinking, emotional intelligence, first-principles problem solving, and the ability to lead others through change.
AI agents aren’t just about operational efficiency, they’re unlocking personalization at scale. And HR leaders have a unique opportunity to reshape how people experience work.
Take talent acquisition. AI agents can already streamline resume screening, identify the best-fit candidates, and flag potential bias in hiring processes. But the opportunity goes beyond selection. Agents can personalize onboarding, recommend tailored learning paths, and connect employees with internal communities or mentors that align with their goals.
This shift turns the employee experience into something dynamic and responsive. A new hire in their 20s may want different growth opportunities than a seasoned leader nearing retirement. With AI, personalization becomes a built-in feature of how work systems operate, not an afterthought or nice-to-have.
This personalization doesn’t replace HR, it amplifies their reach. It also helps level the playing field. Every employee gets the equivalent of a support team, the kind of tailored guidance that’s traditionally only been available to senior leaders.
AI agents aren’t just about operational efficiency, they’re unlocking personalization at scale.
Successful AI adoption depends less on technology and more on psychology. Most resistance comes from fear—fear of obsolescence, fear of loss of control, or just fear of the unknown.
To manage this, leaders should think in personas. There are skeptics, who need data and small wins to be convinced. There are realists, who believe in the value but aren’t sure how far it will go. And there are zealots, who are all-in and want to experiment. Each group needs a different strategy, but the goal is the same: move people forward without leaving anyone behind.
Business leaders also need to clearly communicate not just what’s changing, and what’s not. It’s crucial that employees know they’ll still be empowered to make decisions, set strategy, and be valued for distinctly human contributions. Focusing on what remains consistent builds trust.
And finally, reward curiosity. Celebrate the people who try new tools, take smart risks, and help others navigate change. If the behaviors you want aren’t tied to recognition or outcomes, they won’t stick.
Responsible AI doesn’t start with policy. It starts with intention.
Trust is built through systems: assigning agents the right access, tracking their performance, and making clear who is accountable for what. It’s not unlike onboarding a contractor or third-party vendor. You give access based on purpose, not convenience.
This includes:
Most importantly, you need consistent governance to maintain trust over time. That includes clear standards for deployment, transparent communication with your workforce, and a culture that sees AI as a tool—not a threat.
AI agents are not a project to be passed down the chain. They are strategic assets that reshape how value is created and who creates it. That shift demands executive ownership.
For business leaders, the time to engage is now. You don’t need to be a technical expert, but you do need to ask the right questions:
Organizations that treat AI agents as part of their core workforce planning, not just an IT initiative, will have a clear advantage.
Agentic AI isn’t an overlay on your business—it’s a forcing function. It challenges how work is distributed, how value is measured, and what leadership looks like in a mixed human-digital workforce. The companies pulling ahead are doing more than piloting tools—they’re designing for scale, investing in new human differentiators, and treating agents as team members. The question isn’t whether AI agents can improve operations. It’s whether your organization is structured to effectively deploy and manage AI, and turn it into a competitive advantage.
Over half of business leaders are concerned about talent shortages—and only 32% are confident their organization has the skills needed for success. See how AI is transforming skills management in this report.
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