AI agent workplace skills are reshaping professional careers faster than most learning and development strategies can keep pace, with business leaders converging on a new archetype: the versatilist, a professional whose depth in one field stretches to include directing, questioning, and correcting the agents working alongside them.
The shift is visible in hiring priorities, internal workforce strategies, and the data coming out of enterprise AI deployments. What is emerging is not a choice between generalist and specialist, but something in between, and companies are beginning to build explicit frameworks around it.
Embracing Adaptability Over Mastery
Gerrit Kazmaier, who joined Workday as president of product and technology on 25 February 2025, has argued that agentic AI amplifies workers’ strengths, allowing them to function as polymaths. In that role, Kazmaier is responsible for the strategy, delivery, infrastructure, and security of Workday’s entire platform and suite of AI agents, drawing on over 11 years at SAP and a subsequent stint at Google before joining Workday.
Julie Colwell, principal strategist at Workday, developed the theme in a blog post published on Workday’s Perspectives site on 23 June 2026. Her argument: labour demand is moving away from professionals who know everything about a single domain toward those with knowledge across multiple disciplines.
Kathy Pham, vice president of AI at Workday, told ZDNET that professionals who can work with agents across multiple areas simultaneously will be crucial. ‘These are the gaps that we need to fill, and maybe we can then redirect our energy to higher-value areas, because we’ve now automated the less valuable parts of our working lives that took our time,’ she said.
Chris Kairinos, senior director of global modern workplace technology at A+E Global Media, put it in practical terms. Specialising in one area is being replaced by the aptitude to work across many. ‘I don’t think you need to master technology, because I don’t think there is a way to master it now,’ he told ZDNET.
His analogy: professionals are not learning to play a piano once. The instrument keeps changing. ‘It’s as if 30 different keys are being added to a piano. The shape of the piano is changing; there are now three layers of keys on that piano, and there are an extra three pedals,’ he said. ‘If the piano is changing all the time, how are you ever going to become a master? In those conditions, you can’t. You’ve just got to be adaptable enough to be able to use it.’
David Minahan, director of digital, data, and technology at UK charity Young Lives vs. Cancer, has translated this into a deliberate internal strategy. Rather than training generalists or retaining narrow specialists, he is building what he calls ‘versatilists.’ ‘Our people are expected to work across the tech stack,’ he told ZDNET. ‘And it’s a deliberate point: generalists aren’t necessary, but versatility is.’
For his team, the appeal is practical. ‘There is a history in IT where people get pigeonholed in certain things, and it’s difficult for them to move,’ Minahan said. The versatilist pathway gives motivated professionals the freedom to move between disciplines without abandoning their core competence.
AI Agent Workplace Skills Demand a New Kind of Expert
The demand for this profile is backed by enterprise data. A Snowflake report on the ROI of generative AI and agents, based on responses from 2,050 business and IT managers across European and global organisations, found that 32% of respondents already have agentic solutions in production. Senior executives expect up to a 47% return on agentic investments over the next 12 months.
The teams most actively deploying agents are IT operations (62%), data analytics (59%), cybersecurity (53%), software development (50%), and customer service (49%). In each case, human oversight is part of the design: agents execute, but professionals with domain knowledge set parameters and catch errors.
Ankur Anand, group CIO at recruiter Harvey Nash, frames this as specialism stretched rather than abandoned. ‘Every professional who’s actually pulling ahead right now is an expert whose specialism has stretched to include directing, questioning, and correcting AI that works alongside them,’ he told ZDNET. ‘That split is different [from] becoming a jack of all trades, and it’s a much more useful goal for anyone building a career around this shift.’
Anand pointed to engineers at Snowflake as an example. One head of engineering spends 20 to 30 hours a week directing five agents, reviewing design choices, and deciding when code is ready to ship. ‘His deep technical background is exactly what lets him catch the agents when they get something wrong,’ Anand said. ‘Someone with only a shallow, general grasp of engineering would have nothing solid enough to check the agents’ work against.’
The pattern extends beyond technology roles. In recruitment, an AI sourcing tool can scan thousands of candidate profiles faster than any consultant. But Anand argues that the hires that matter, senior technical roles with genuinely scarce skills, still depend on a consultant who has spent years inside one specific market. ‘They’re the specialists who know a candidate who looks like a moderate match on paper might be the far better fit, while one who ticks every box on the spec sheet would walk out within six months,’ he said. ‘That judgment is built from pattern recognition earned over years in a narrow field.’
Inside Workday itself, early adopters of an internal agent that automates release preparation have already cut time spent on feature rollouts by 50%, according to the Workday blog. The productivity gain did not come from replacing the engineers involved; it came from pairing agent automation with professionals who understood what a correct output looked like.
Workday has held its position as a Leader in the Gartner Magic Quadrant for Cloud HCM Suites for enterprises of 1,000 or more employees for 11 consecutive years, a run that spans the pre-agent and agentic eras of enterprise software. The companies now building agent-ready workforces may be the ones who define what year 12 looks like.