Driving AI strategy in regulated industries: Balancing innovation with compliance

In heavily regulated industries like healthcare, pharmaceuticals, and financial services, artificial intelligence (AI) is no longer an idea of the future or nice-to-have technology. It’s a modern engine for operational efficiency. 

Yet, for a lot of leaders in these sectors, the path to AI adoption often feels blocked by a wall of regulatory red tape. The challenge is clear: how do you innovate at the speed of AI while adhering to strict standards like HIPAA, SOC1&2, and GDPR?

During our recent webinar, “Driving AI Strategy & Automation in Regulated Industries,” experts from the Codal team and AWS sat down to discuss these barriers. The conversation shifted the perspective on compliance, viewing it not as a hurdle to be cleared, but as the foundation for sustainable innovation.

If you missed the live discussion, you can catch the full session on-demand.

The maturity spectrum

The current market reflects a “tale of two speeds.” While organizations are eager to deploy customer-facing AI, regulatory complexity often creates hesitation in high-stakes environments. Consequently, the most significant momentum is happening within internal operations.

As Vishal Srivastava, Generative AI Specialist at AWS, noted during the webinar:

“I see momentum moving fast on internal operations. Especially intelligent document processing, claims automation, or risk assessment… where they can typically control data flows end-to-end and maintain compliance boundaries.”

Success in these areas is often defined by where an organization sits on the AI maturity spectrum:

• Rule-based automation: Efficient for predictable tasks but lacks the flexibility to handle unexpected data shifts.
• GenAI assistants: Capable of handling broader tasks, like summarization, but still require constant human prompts.
• Goal-driven agents: The current frontier. Digital teammates that coordinate multiple systems to achieve high-level business outcomes.

Security by design

A common misconception is that compliance stifles innovation. In reality, a robust governance framework provides the “braking system” that allows a company to drive faster. If you attempt to retrofit security after a project is built, you’ve already failed.

Stephen Yi, Managing Director of Engineering and Product at Codal, emphasized the need for proactive standards:

“Establishing those standards of governance and compliance upfront is a key point. If you’re doing that after the fact, you’re already too late, and I think you will fail.”

By leveraging AWS tools like Amazon Bedrock and SageMaker, enterprises can utilize built-in governance features. 

As Vishal added, “The right AI architecture accelerates innovation because you build trust into the system rather than retrofitting it later.”

Proof in production

Theory only takes an organization so far. Our webinar highlighted how regulated firms are turning these strategies into measurable ROI:

Health & life sciences

For a medical device manufacturer, Codal implemented a system to transcribe messy, handwritten doctor prescriptions. Using AWS Textract and Bedrock, the solution associates free-form data with specific patient records. 

Stephen noted that this resulted in “net savings of both manpower… as well as time to fulfillment; fulfilling these devices out to customers that actually needed it.” 

See the full case study here.

Financial services

Firms are meeting strict data retention mandates by automating the archival of sensitive data using S3 Glacier Deep Archive. These automated pipelines move “cold” data to low-cost storage, ensuring compliance (like SOC) without incurring astronomical infrastructure costs.

Architecting a long-term strategy

To ensure an AI initiative doesn’t stall in the “proof of concept” (POC) phase, leaders should focus on a strategic framework. 

Vishal suggested a move away from the hype:

“Successful AI adoption is not just about chasing any buzzwords. It’s about solving business problems intelligently… with the right level of automation for your maturity stage.”

To reach that maturity, keep these three pillars in mind:

1. Map the value: Identify high-cost, repetitive processes where the “human-in-the-loop” can be most effective.
2. Cross-functional collaboration: Success requires a “Center of Excellence” approach. As Stephen highlighted, “You need the intersection of business objectives, technical architecture, and data flow, and usually, it’s not one person that understands all three of those facets.”
3. Data readiness: AI is only as good as the data it consumes. Assess where your data lives, its quality, and its “cleanliness” before moving to production.

Q&A

During the live session, attendees posed critical questions about scaling and risk.

What strategies work best for scaling AI automation across multiple business units while maintaining QA standards?

Stephen: Start with a POC to prove the concept in a small form factor. But crucially, as you build up automation, you must equally build up your test automation. If your regression tests don’t evolve with your AI, you will spend more time fixing broken functionality than innovating.

How can we balance innovation with regulatory risk management in financial services?

Vishal: Use tools that provide explainability. Services like Amazon SageMaker Clarify help detect bias and explain why a model made a specific prediction.

Steve: Leverage your historical data to create realistic stress tests. Use “Black Swan” events from the past to simulate high-stress scenarios for your AI, ensuring it holds up under pressure before going live.

Ready to build your AI roadmap?

AI in regulated industries doesn’t have to be a gamble. With the right strategy, governance, and partners, you can move from experimentation to production-grade deployment with confidence.

You can download our comprehensive whitepaper, Navigating AI in Regulated Industries, to take a deeper look at the enterprise companies in regulated industries can responsibly adopt AI while simultaneously avoiding risk, ensuring transparency and, maintaining compliance. 

And if you’re ready to define your AI use cases, book a custom AI strategy workshop with our team to prioritize your roadmap and prototype your next concept.