Curiosity Over Configuration: What RBC and Bon Secours Mercy Health Say HRIT Leaders Need Now

A few years ago, competency in Human Resources Information Technology (HRIT) meant knowledge of system configuration, ticket resolution, and integration maintenance. Today, however, the same role requires a mastery of AI governance, enterprise architecture, and business transformation. The skills that made someone effective in HRIT three years ago are still useful, but they’re no longer sufficient.

Travis Windling, Senior Director of Talent Acquisition Strategy, Operations & Contingent Workforce at Royal Bank of Canada, and Lisa Funari, Director of Talent Acquisition, Marketing Brand and Candidate Pipelines at Bon Secours Mercy Health, spoke at IAMPHENOM 2026 about what the AI era is demanding from HRIT professionals, and how two of Phenom’s most mature customers are building teams that can meet it.

Watch the full session here, or explore the highlights below.

Why Is HRIT’s Role Changing So Quickly?

Both panelists described the same structural shift. AI is taking on the manual tasks that previously defined HRIT’s day-to-day, which means the people doing that work need to move up the value chain. Being a “box checker” is no longer enough because automation can handle the boxes. What’s left requires judgment, systems thinking, and the ability to translate between business context and technical architecture.

In healthcare recruiting, the days of moving all candidates from one stage to the next as fast as possible is being replaced by getting better qualified candidates to hiring managers faster. “We are now builders of processes instead of just being the ones that implement the process," Funari said. Much of that design work is now an HRIT skill.

Windling added that as technology has improved, so have expectations to rapidly adapt and innovate. “You still have to test and learn,” he said. “But you have to test and learn with the enterprise scale in mind."

What HRIT Skills Are Top Companies Building for the AI Era?

Funari and Windling both pointed out capabilities they didn’t need to prioritize two years ago.

For Funari’s team, data fluency topped the list. This involves bringing in data from one system to another, ensuring it translates, and deciphering whether it’s actionable.

For Windling, AI architecture and knowledge management are the two skills he’s most focused on. Architecture in the AI context means being able to map capabilities to tools, such as knowing which system owns interview scheduling, why, and how to make that case at the executive level. Knowledge management means documenting processes, understanding how users interact with systems, and creating the organizational context that agentic AI will eventually be built on.

“If you don't have well-documented processes, if you don’t understand your architecture, if you don’t understand the way in which your users interact with your system, then you're always going to be behind the eight ball,” Windling said.

Taken together, what they’re describing is a function that has quietly become a technical discipline. Data fluency, capability mapping, and knowledge management aren't soft skills or change management buzzwords. Rather, they're the foundational work that determines whether AI has anything solid to run on.

Funari added that change leadership is an increasingly critical HRIT responsibility. As AI shifts how candidates are sourced and how recruiters engage with them, hiring managers need to understand why the process looks different and trust the outputs. That buy-in is now an HRIT communication skill, not just an HR one.

"We are now totally changing the way in which candidates may be coming to them” she said. “How do we communicate to the leaders that we are getting you better qualified candidates than what you're used to seeing before so that they buy into the process?"

But when asked what the single most important skill is for the road ahead, both panelists pointed to curiosity as a disposition, rather than a specific technical competency or certification. It’s about the ability and willingness to dive into a tool, learn how it actually works, build on that understanding, and adapt when the environment changes again.

"I think that is probably, bar none, the most important skill that we can all have in terms of what the future of work looks like," Windling said. "You really need to architect your own future.”

What Are Some Best Practices for AI Governance in HR?

Both organizations are working through AI governance from different angles, but the same principle runs through both approaches: humans own the decision. AI accelerates, prioritizes, and recommends. The human in the loop is responsible for the outcome.

Funari described a live example from Bon Secours’ pilot with Phenom Voice Screening Agent. The team had set up a scoring rubric for recruiters, but found recruiters were still advancing candidates with low scores.

When they asked why, recruiters explained that candidates lacked years of experience in one category but compensated in others. The rubric needed to reflect the judgement a seasoned recruiter would make. That kind of continuous calibration — catching misalignment between what AI measures and what the business actually needs — is now a core part of HRIT’s job. It doesn’t happen without people who understand both the system and the business context behind it.

Windling's experience points to the same principle from the technical side: build a strong internal team and own your architecture.

Vendor expertise doesn't replace internal decision-making. Without people on both the business and technology side who understand the full downstream impact of technology decisions, organizations will keep relying on outside resources for work that should be internal capabilities.

The AI era isn't asking HRIT to become a different function. It's asking it to become a more capable one.

Learn more about how to build your AI-ready HR tech team

Pooja Thammina