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After a 20-year career at design software company Autodesk, Prakash “PK” Kota felt an urge for a career change, in part because he didn’t want to end his career having worked for only one employer.
Upon ending his time at Autodesk with a seven-year stint as chief information officer, Kota made the leap to human resources software company UKG, where he has held the same role since April 2025. He quickly consolidated software vendors, oversaw the launch of 387 internal AI applications from more than 1,400 employee-submitted ideas, and spearheaded the internal creation of more than 12,000 AI agents across Microsoft, Google’s Gemini, and OpenAI’s ChatGPT.
“In the AI era, everyone is talking about how work will be reshaped; how do employees and workers coexist?,” says Kota. “HR tech is going to be a huge area of investment in every company.”
AI’s ability to automate workplace tasks ranging from coding to customer service to marketing, while increasingly taking on more complex tasks via agentic AI, has put more pressure on businesses and their HR teams to envision a more collaborative workforce that blends humans and machines. There’s also far more demand for prospects who have AI skills at their fingertips: job listings that mention AI are growing almost eight times faster than the total employment market, according to consulting giant PwC.
And yet, studies consistently show that the workforce’s youngest cohort, Gen Z, is more worried about their prospects in an AI era than older workers. This is partly driven by the difficulty many are facing in landing their first entry-level job, and swirling headlines of massive job cuts at major employers including Meta Platforms, Verizon, and Oracle, oftentimes with internal AI adoption attributed as a root cause.
Even as major employers are overhauling their teams and quickly pivoting on the skills they desire from new recruits, a vast majority of HR leaders say they haven’t yet established a firm grasp on what their internal future workforce needs will be in the AI era. Only 11% of HR professionals reported having established strategic, long-term workforce plans that extend beyond a three-year window, according to a study of about 1,300 HR industry pros across ten countries published by consultancy McKinsey in June.
While AI was certainly high on Kota’s agenda when he joined UKG, he said his first major project was addressing the long-delayed IT integration projects that hadn’t yet been completed following the pandemic-era merger between Kronos and Ultimate Software. Those two companies united to form the then-newly created, privately held UKG in October 2020.
Within his first 90 days as CIO, Kota centralized the technology department to handle all systems, data, and AI across both legacy businesses. After just six months, UKG consolidated the company’s enterprise resource planning and customer relationship management software to Microsoft and Salesforce, respectively. Multiple data warehouses were also merged.
As UKG leans into AI, the company has made ChatGPT Enterprise and Google’s Gemini Enterprise widely available to all 14,000 employees, while the product and engineering team is also using Anthropic’s Claude Code tool. Kota says he’s mostly avoiding multi-year contracts, because the technology is evolving so quickly.
One of the more impactful internal applications of AI is UKG’s utilization of AI-enabled voice and chat agents to handle customer inquiries, with an estimated 27% of those calls now being addressed autonomously. Autonomous agents are also drafting customer materials to make it easier for human representatives to handle the calls they do have with customers, helping workers handle issues at a speedier pace while also giving them time to upsell UKG’s products.
This system is also continuously learning, says Kota, as the AI tool has created around 300 “case studies” that summarize a customer service issue that it newly learned to handle and explain how to address the problem. These case studies are then used by both the AI tool and human workers. Propensity AI models, meanwhile, rely on 300 unique signals to predict the probability that a customer may be willing to buy more from UKG and share those insights with the sales and marketing teams.
Kota says he looks at multiple metrics to gauge the success of an AI internal deployment. For coding, value is determined by not just the quantity that’s produced, but also by what product features are actually bought by customers. Within customer service, UKG is monitoring both overall productivity and also upselling and customer sentiment scores.
And while AI-enabled efficiency isn’t measured equally for every employee, Kota and UKG says they have measured that AI has added 8,500 hours in productivity each month.
UKG’s C-suite leadership is also encouraged to think about AI adoption through a so-called “T3” concept: talent, tools, and tokens. Business leaders need to allocate spending to all three, and Kota says they are best suited to determine what’s the right mix.
“I don’t want to have a standard rule across the company about what the divisional token spend should be,” says Kota. “We should have an open mind with this concept.”
John Kell
This story was originally featured on Fortune.com
