Ethical AI Adoption in Financial Services Hiring: How Truist and Regions Bank Built Trust While Moving Fast 

In regulated industries like financial services, bringing AI into talent acquisition processes means navigating risk, legal, compliance, privacy, and data protection teams, which surfaces friction points and ethical questions at every turn. But Truist and Regions Bank, have worked through those concerns in ways that benefit employees and candidates alike.

During IAMPHENOM 2026, Ray Tavares, EVP, Head of Human Capital Solutions & Experience at Truist and Antonio Howard, VP, HR Technology Manager at Regions spoke about what ethical AI adoption looks like inside two of the country’s major financial institutions.

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

Why Is AI Governance Such a Friction Point in Financial Services?

The challenge is that risk and compliance teams can seem obstructionist because they’re working from a different information base. Tavares described questions from risk and legal partners in the early days as “borderline insulting” — not because they were wrong to ask, but because they reflected how little exposure those teams had to TA technology. They were experts in regulated financial products, but less familiar with t AI-assisted candidate matching or interview scheduling.

Related: The Future of Financial Recruitment: How Regions Bank Harnesses AI for Smarter Hiring

Howard’s experience at Regions Bank surfaced a different version of the same problem: stakeholders getting involved too late. Once a tool has budget approval and implementation underway, objections from compliance become speed bumps that cost everyone time. The better approach, he found, was to ask those questions upfront to understand the risk framework vendors had to meet before selecting one, not after.

"I don't want to run down the tracks and then you tell me no,” Howard said.

How Did They Build AI Governance That Actually Works?

Both Tavares and Howard framed the answer the same way: education and co-creation, not compliance after the fact. Tavares brought risk, legal, and privacy partners into solution discussions early so they could understand not just what a tool does but what track record it has, along with its limitations. 

“You’ve got to show some willingness to poke holes in your own solutioning,” he said. 

That kind of credibility is what builds the trust necessary for faster approvals over time.

Howard took a proactive approach. He went to Regions Bank’s internal risk team to ask what their deal-breakers were before talking to vendors. Knowing the framework in advance meant he could filter out options early and enter vendor conversations with a clearer picture of what “approval-ready” actually looked like. He noted that those partners are sometimes reluctant to share the full framework, but the effort to understand it is worth making. 

Pilot programs also featured prominently in both approaches. Smaller scopes surface operational realities and build a visible track record of success that compliance partners can evaluate.

On the vendor side, both panelists emphasized that governance responsibility doesn’t end with the customer. Howard credited Phenom for independently testing and evaluating Phenom Fit Score, Phenom’s candidate-matching tool, and providing the documentation customers need to bring to their own risk teams.

“Your vendors need to be testing their stuff too,” he said. “Don’t let them throw that responsibility solely on you.”

What Has Responsible AI Adoption Produced for Truist and Regions Bank?

Truist’s clearest data point came from a pilot of Phenom Automated Interview Scheduling. Over roughly 15 months, the team:

• Sent 40,000 interview requests through the self-scheduling pilot

• Achieved a 70%+ candidate acceptance rate on those requests

• Scheduled 22,000 interviews within the first calendar hour of the request being sent

Previously, scheduling was a manual process that took two business days.

Regions Bank, on a parallel track, has saved thousands of hours collectively across scheduling and automations across their candidate experience.

Tavares described the scheduling pilot as “small” from a compliance standpoint but strategically significant, because the sustained track record of results was exactly what risk and legal partners needed to see. That credibility accelerated the next approval cycle. Success in one area of AI creates the conditions for faster adoption in the next.

Both leaders were also clear that ROI metrics alone don’t tell the whole story. Less measurable values, such as time freed for hiring managers, faster candidate response, and improved experience support the business case in a way that hour counts don’t fully capture. 

Howard put it plainly: “What are you trying to buy your recruiter’s time for? Think about purpose and intent.”

What Does Sustainable AI Adoption Look Like Going Forward?

Both Truist and Regions Bank are extending AI and automation capabilities within the employee experience to enhance talent mobility efforts. Automated job alerts for internal candidates, and proactive outreach to employees with personalized internal career paths are just the beginning. Interview intelligence is also on the roadmap for both, pending the governance work around recording and transcription policies that need to be addressed in a regulated environment.

Related: Dear Phenom: How AI Scheduling Transforms Interview Coordination for Busy Recruiters

Howard and Tavares had similar advice. Start small if you haven’t started, build the relationship with your internal partners before you need something approved, and make the effort to understand what the framework actually requires.

Financial services may move carefully by nature, but the institutions that invested in AI governance early are now adopting AI faster than those who didn’t.

As Tavares put it: Patience is required, and patience pays.

Learn more about fairness and transparency in HR through ethical AI