How UKG puts AI to work for frontline employees

As organizations rebrand themselves as AI companies, most of the conversation is focused on knowledge workers rather than the people in retail, manufacturing, and healthcare who can benefit from AI just as much. Prakash Kota, CIO of UKG, one of the largest HR tech platforms in the market, which delivers a workforce operating platform utilized by 80,000 organizations in 150 countries, explains how his company uses agentic AI, voice agents, and a democratized innovation framework to transform the frontline worker experience, and why the CIO-CHRO partnership is critical to making it stick.

How do you leverage AI for growth and transformation at UKG?

UKG is one of the largest HR, pay, and workforce management tech platforms in the market, and our expertise is in creating solutions for frontline workers, which account for 80% of the world’s workforce. This is important because when companies rebrand themselves as AI for knowledge workers, they’re not talking about frontline workers. But people in retail, manufacturing, healthcare, and so on also benefit from AI capabilities.

So the richness of our data sets, and our long history with the frontline workforce, positions us well for AI driven workforce transformation. 

What are some examples?

We use agentic AI for dynamic workforce operations, which shows us real-time labor demand. Our customers employ thousands of frontline workers, and the timely market insights and suggested actions we give them are new and valuable.

We also provide voice agents. Traditionally, when a frontline worker requests a shift, managers would review availability, fill out paperwork or update scheduling software, and eventually offer an appropriate job. With voice agents, AI works directly with the frontline worker, going through background and skills validation, communication, and even workflow execution. The worker can also ask if they can swap shifts or even get advice on how to make more money in a particular month. This is where AI changes the entire frontline worker experience.

We also launched People Assist, an autonomous employee support agent. Typically, when an employee is onboarded, IT and HR need to trigger and approve workflows. People Assist  not only tracks workflows, but also performs those necessary IT and HR onboarding activities so new employees are productive from day one.

What framework do you use to create these new capabilities?

For internal AI usage for our own employee experience, we use an idea-to-implementation framework, which involves a community of UKG power users who are subject matter experts in their area. Ideas can come from anybody, and since we started nine months ago, more than 800 ideas have been submitted. The power users set our priorities by choosing the ideas that will make the most impact.

Rather than funneling ideas through a small central team — a linear process that kills momentum — we’ve democratized innovation across the business. We give teams the governance frameworks, change models, and risk guardrails they need to move quickly.  With AI, the most important thing isn’t to launch, but to land.

But before we adopted the framework, we defined internal personas so we could collaborate with different employee groups across the company, from sales to finance.

With the personas and the framework, we can prioritize ideas by persona, which also facilitates crowd sourcing. You’re asking an entire persona which of these 10 ideas will make their lives better, rather than senior leaders making those decisions for them.

Why do so many CIOs focus on personas for their AI engine?

Across the enterprise, every function has a role to play. We hire marketing, sales, and finance for a particular purpose. Before AI, we gave generic packaged tools to everyone. AI allows us to build capabilities to make a specific job more effective. Even our generic AI tools are delivered by persona. Its impact on specific roles is the reason personas are so important right now. Our focus is on the actual jobs, the people who do them, the skills and tasks needed, and the outcomes they want to achieve.

We know our framework and persona focus work from employee data. In our most recent global employee engagement survey, 90% said they’re getting the right AI tools to be effective. For the AI tools we’ve launched broadly across the company, eight out of 10 employees use them. For me, AI isn’t about launching 10,000 tools, because if no one uses them, it’s just additional cost for the CIO and the company.

Is the build or buy question more challenging in this nascent stage of AI?

The lifecycle of technology has moved from three years to three hours, so whenever we build at UKG, we use an open architecture, which allows us to build with a commercial product if one comes on the market.

Given the speed of innovation, we lean toward augmentation rather than build. There are areas, like our own native products, where a dedicated engineering team makes sense. But for most of our AI capabilities — customer support and voice agents, for example — we work with our vendor partners. We test and learn with multiple vendors, and decide on one usually within two weeks.

This is what AI is giving all CIOs: flexibility, rapid adoption, interoperability, and the ability to quickly switch vendors. It’s IT that’s very different from what it used to be.

Given the shift to augmentation, how will the role of the software engineer change?

For software builders, business acumen — the ability to understand context — is no longer optional. In the past, the business user would own the business context, and the developer, who owns the technology, brings that business idea to life. Going forward, the builder has the business context to create the right prompts to let AI do the building, and the human in the loop is no longer the technology builder, but the provider of context, prompts, and validation of the work. So the engineer doesn’t go away, however they now finish a three-week scope of work in hours. With AI, engineers operate at a different altitude. The SDLC stays, but agility increases where a two-week concept compresses into two days.

At UKG, you’re directly connected to the CHRO community. What should they be thinking about how the workforce is changing with AI?

The best CHROs are thinking about the skills they’ll need for the future, and how to train existing talent to be ready. They’re not questioning whether we’ll need people, but how to sharpen our teams for new roles. The runbooks for both IT and HR are evolving, which is why the CIO-CHRO partnership has never been more critical to create the right culture for AI transformation.

CIOs can deliver a wealth of employee data like roles, skillsets, and how people spend their time. And as HR leaders help business leaders think through their roadmap for talent —  both human and AI — IT leaders can equip them with exactly that intelligence.

What advice would you give to CIOs driving AI adoption?

Invest in AI fluency, not just AI tools. Your people don’t need to become data scientists, but they do need a new kind of literacy — the ability to work alongside AI, question its outputs, and know when to override it. That’s a training and culture investment, not a software investment.

And redesign work before you redeploy people. Don’t just drop AI into existing workflows. Use this moment to ask what work really matters. AI is forcing us to have the job design conversations we should’ve had years ago, so it’s important to be transparent about the journey. What’s killing workforce trust now is ambiguity. Your people can handle hard truths but not silence. Leaders who communicate openly about where AI is taking the organization will retain the talent they need to get there.