Why Regulated Industries May Have an Unexpected AI Advantage

Content contribution by Elissa Torres, Director of Transformation and Enablement

 For years, regulation was viewed as a drag on innovation. 

Healthcare operates under HIPAA, insurance carriers navigate complex compliance requirements, and financial institutions have invested heavily in governance, oversight, documentation, and risk management. While technology companies are celebrated for moving quickly, regulated industries were often characterized by lengthy reviews, approval processes, and controls that appeared to slow progress. 

When AI arrived, that perception seemed justified. The companies moving fastest attracted the most attention, and the prevailing assumption was that speed would determine who benefited most from AI. 

A few years later, the conversation is beginning to change. Most organizations have figured out how to experiment with AI at this point, with the challenge being how to turn those experiments into something repeatable, scalable, and sustainable. Those succeeding with AI now are discovering that governance is no longer the opposite of innovation.

Moving Beyond the Pilot Phase 

The gap between a successful pilot and a production-ready capability is where many AI initiatives stall. A proof of concept can demonstrate technical feasibility, generate excitement and create momentum, and produce measurable results in a controlled environment. 

The pilot phase in AI implementation often overlooks the operational questions that emerge when AI becomes part of everyday business processes. 

• Who owns the solution once it moves into production? 
• How is performance monitored over time? 
• Who reviews outputs when they influence decisions? 
• How are exceptions handled? 
• What happens when regulators, auditors, executives, or customers ask how a decision was made? 

At that point, the conversation shifts away from the technology itself and toward the systems surrounding it. Deloitte's 2026 State of AI research, based on a survey of 3,235 leaders across 24 countries, found that worker access to AI rose 50% in 2025 alone, from under 40% to around 60% of workers now equipped with sanctioned AI tools. Today, just 25% of organizations have moved 40% or more of their AI pilots into production. Within three to six months, 54% expect to reach that threshold, which is more than double the share that is there today. The experimentation phase is closing. What comes next is conversion, turning AI investment into operational performance. 

Using Regulation as an Advantage 

One of the more interesting developments in enterprise AI is that the conversation has started moving away from the models themselves. A year ago, most discussions centered on capability: What can the technology do? Which platform is best? Which use cases should we pursue first? 

Today, many organizations are asking a different set of questions: How do we move a successful pilot into production? Who owns it once it's there? How do we monitor performance over time? What happens when an AI recommendation influences a decision that later needs to be reviewed or explained? 

Eventually, every AI initiative reaches a point where success depends less on what the technology can do and more on how the organization manages it. Organizations that already have mature processes for risk management, oversight, and decision-making tend to reach that point with fewer surprises. The conversations are not necessarily easier, but they are familiar. 

A Foundation Is Not the Same Thing as Readiness 

None of this means regulated industries automatically have AI figured out. Many organizations have spent years building governance structures, risk management processes, and oversight frameworks. This foundation creates potential. 

AI introduces new questions around ownership, accountability, monitoring, and decision authority that many organizations are still working through. To make this work, organizations need to adapt their existing strengths to the realities of AI adoption. In other words, move from governance as documentation to governance as an operating capability. 

Looking Beyond the Technology 

For years, regulation was often viewed as something organizations had to work around. Today, it may be providing something many organizations are struggling to build. 

As AI moves from experimentation into day-to-day operations, the conversation is becoming less about access to technology and more about the ability to manage it responsibly. Organizations are discovering that successful AI adoption depends on more than selecting the right tools, as it requires clear decision-making, operational discipline, and confidence that new capabilities can be integrated into existing business processes. 

Many regulated organizations are further along that path than they realize, but does your organization know what needs to happen before AI can be deployed at scale? In less than 15 minutes, we evaluate the capabilities that support successful AI adoption and gain a clearer understanding of where they stand today. Because before the next AI initiative begins, it's worth understanding whether the foundation is ready to support it. 

Take the Kona Kai AI Readiness Assessment  and identify the gaps that could prevent AI from delivering meaningful business value.