HRIT leaders in every industry are living under the immense pressure to modernize. But those in the most regulated industries feel a unique strain: They must satisfy regulatory and compliance obligations while generating AI investment return and transforming their workforce for ever-evolving state of AI at work.
The ask is urgent and business-critical, while simultaneously requiring more governance than ever before. How can HRIT leaders manage these competing priorities?
The Pressure to Balance Compliance and AI Innovation
Regulated industries are already trailing behind in terms of AI adoption. They sit at a 58% adoption rate, compared to 92% of all other organizations, according to a Deskpro study.
Slow AI adoption is something no business can afford right now. It’s time to understand where the change is breaking down. For regulated orgs, it’s twofold: both in governance demands and due to a lack of data visibility.
These companies need to implement AI tools that the compliance environment itself hasn’t yet fully figured out how to “govern.” Just under half (49%) of the organizations that are using AI or are about to have policies in place governing its use, according to the 2026 SHRM State of AI Report. Governance is a necessity, not an option, in industries like financial services, pharma, and healthcare—with this gap pointing to one of the reasons that adoption is trailing in regulated industries.
Even without a lot of formal regulatory guidance on AI, there’s no compliance leniency in the face of digital transformation, and tech advancements aren’t stopping to wait for organizations to catch up. The playbook most HR tech vendors are selling (move fast, consolidate, and deploy AI) was written for organizations that don’t have to answer to an auditor at the end of the quarter. Sometimes “moving fast” is capped by what governance allows for.
Despite limitations, AI readiness is not optional for businesses that want to succeed long term. They have likely already made investments in AI, and now it’s time to produce results. This is a playbook for HRIT leaders in regulated industries who are feeling that tension—trying to drive AI transformation and stay compliant, while successfully modernizing their HR tech stack and their workforce solutions.
Don’t Choose: Measure Readiness and Completions
Regulated industries have always measured workforce activity. Completions. Certifications. Attendance records. And for compliance purposes, tracking those metrics is non-negotiable. Now, AI transformation has to be part of that equation.
Instead of picking speed and AI readiness over compliance, it’s critical to build an HRIT system that adds to the regulated metrics your business already collects.
Why? Completion data satisfies an audit. Readiness data drives a business forward. You need both.
Your workforce needs to be prepared for constant change, and completion data doesn’t equate to practical readiness. In a business environment where AI usage is now the baseline expectation, measuring completions could create a false sense of progress. It’s a measurement that’s difficult to defend when real growth and transformation results don’t follow.
In a way, this is what’s already happening in the market: Businesses invested heavily in AI and the results have been minimal. Now, HRIT leaders are being tasked with answering for that.
This is where the ecosystem and infrastructure becomes critical.
Build an Infrastructure that Prioritizes Visibility and Innovation
HCMs track skills. Learning platforms measure completions. AI tools answer questions in the moment. But, if siloed, none of these systems alone present the full picture you need in a high-stakes transformation environment. You need to know whether your people are ready for what’s coming next.
That distinction matters enormously in regulated industries. Knowing that someone completed a training module is not the same as knowing they’re ready to apply it in a role where a mistake carries legal, financial, or patient safety consequences.
System connectivity is the missing piece. Without it, AI readiness scalability remains a huge barrier. According to McKinsey, only about a third of businesses have started scaling AI enterprise-wide, despite the fact that 88% reported regular AI use in at least one function.
When skill data, compliance records, and workforce readiness signals all flow through a connected system, leaders get a truly complete picture of their workforce. They can see not just who completed the AI fluency training, but who is confident applying it. Not just who holds the required certification, but who is ready for the role that certification is meant to qualify them for.
That’s how you understand where change breaks down. You have to have the full picture to find the gaps. In regulated industries, the best option is a simple, connected HR ecosystem and a way to make skills the center of your people architecture.
Don’t add more tools. Instead, build the connective layer that makes the tools you’ve already invested in work together. Simplifying your HR tech stack will add visibility while reducing cost and making compliance easier than ever.
That’s the data layer that makes talent strategy decisions defensible, transformation progress measurable, and compliance reporting easy.
The New HRIT Playbook
The old playbook for HRIT in regulated industries was essentially defensive: Maintain compliance, manage risk, keep the systems running. That playbook made sense when the pace of change was manageable and the regulatory environment was relatively stable, in the years before HRIT was being asked to answer for AI ROI.
Now, change is constant, adapting governance is essential to keep pace, and proof is required to secure and maintain budget.
The new playbook isn’t about choosing between compliance and innovation. It’s about building a system and a workforce that can operate seamlessly between both. It’s not easy, but it is doable and essential for the AI transformation your business needs.
Regulated, disrupted, and under pressure? Yes. But also, if the right connections get made, better positioned than most to build the workforce intelligence layer that the AI era demands.
You can do that by building an infrastructure where the output of every learning interaction, every certification, and every workforce signal feeds into a single, decision-grade picture of what your workforce can do.
When that foundation is in place, AI stops being a line item that’s hard to defend and starts being the engine that drives measurable workforce transformation. Not because the technology changed, but because the data infrastructure is finally in place to track and measure impact.
Sigamos en contacto.
Quiero suscribirme al boletín mensual con perspectivas exclusivas, los próximos eventos y novedades sobre las soluciones de Degreed.