Artificial intelligence (AI) is transforming regulated industries such as healthcare, finance and legal services, but navigating these changes requires a careful balance between innovation and compliance.

In healthcare, for example, AI-powered diagnostic tools are improving outcomes by improving breast cancer detection rates by 9.4% compared to human radiologists, as noted in a study published in JAMA. Meanwhile, financial institutions such as the Commonwealth Bank of Australia are using AI to reduce fraud-related losses by 50%, demonstrating the financial impact of AI. Even in the traditionally conservative legal field, AI is revolutionizing document review and case prediction, enabling legal teams to work faster and more efficiently, Thomson Reuters reports.

However, introducing AI into regulated sectors comes with significant challenges. For product managers leading AI development, the stakes are high: Success requires a strategic focus on compliance, risk management, and ethical innovation.

Related: Balancing AI Innovation with Ethical Oversight

Why compliance is non-negotiable

Regulated industries operate within strict legal frameworks designed to protect consumer data, ensure fairness and promote transparency. Regarding the Health Insurance Portability and Accountability Act (HIPAA) in health, the General Data Protection Regulation (GDPR) in Europe or the supervision of the Securities and Exchange Commission (SEC) in finance, companies must integrate compliance into their product development processes.

This is especially true for AI systems. Regulations like HIPAA and GDPR not only limit how data can be collected and used, but also require explainability—meaning AI systems must be transparent and their decision-making processes understandable. These requirements are particularly challenging in industries where AI models rely on complex algorithms. Updates to HIPAA, including provisions addressing AI in healthcare, now set specific compliance deadlines, such as the one slated for December 23, 2024.

International regulations add another layer of complexity. The European Union's Artificial Intelligence Act, effective in August 2024, classifies AI applications according to risk levels, placing stricter requirements on high-risk systems such as those used in critical infrastructure, finance and healthcare. Product managers must adopt a global perspective, ensuring compliance with local laws while anticipating changes in international regulatory landscapes.

Ethical dilemmas: Transparency and bias

For AI to thrive in regulated sectors, ethical concerns must also be addressed. Artificial intelligence models, especially those trained on large data sets, are vulnerable to bias. like American Bar Association Of note, unchecked biases can lead to discriminatory outcomes, such as denying credits to particular demographics or misdiagnosing patients based on flawed data models.

Another critical issue is explainability. AI systems often operate as “black boxes,” producing results that are difficult to interpret. While this may be sufficient in less regulated industries, it is unacceptable in sectors such as healthcare and finance, where understanding how decisions are made is critical. Transparent it's not just an ethical consideration—it's also a regulatory mandate.

Failure to address these issues can result in serious consequences. Under the GDPR, for example, non-compliance can lead to fines of up to €20 million or 4% of annual global revenue. Companies like Apple have already faced scrutiny for algorithmic bias. or Bloomberg investigation found that the Apple Card credit decision-making process put women at an unfair disadvantage, leading to public backlash and regulatory investigations.

Related: AI isn't bad – but entrepreneurs need to keep ethics in mind while implementing it

How product managers can lead the charge

In this complex environment, product managers are uniquely positioned to ensure that AI systems are not only innovative, but also compatible and ethical. Here's how they can achieve this:

1. Make compliance a priority from day one

Engage legal, compliance and risk management teams early in the product lifecycle. Collaboration with regulatory experts ensures that AI development complies with local and international laws from the outset. Product managers may also work with organizations such as the National Institute of Standards and Technology (NIST) to adopt frameworks that prioritize compatibility without stifling innovation.

2. Design for transparency

Building explainability into AI systems should be non-negotiable. Techniques such as simplified algorithmic design, model-agnostic explanations, and user-friendly reporting tools can make AI results more interpretable. In sectors like healthcare, these features can directly improve trust and adoption rates.

3. Anticipate and mitigate risks

Use risk management tools to proactively identify vulnerabilities, whether they stem from biased training data, insufficient testing, or compliance gaps. Regular audits and ongoing performance reviews can help detect problems early, minimizing risk of regulatory penalties.

4. Promotion of cross-functional cooperation

The development of artificial intelligence in regulated industries requires input from various stakeholders. Cross-functional teams, including engineers, legal counsel, and ethics oversight committees, can provide the expertise needed to address challenges comprehensively.

5. Stay ahead of regulatory trends

As global regulations evolve, product managers must stay informed. Subscribing to updates from regulatory bodies, attending industry conferences and fostering relationships with policymakers can help teams anticipate changes and prepare accordingly.

Lessons from the field

Success stories and cautionary tales underscore the importance of integrating compliance into AI development. At JPMorgan Chase, the deployment of its AI-powered Contractual Intelligence (COIN) platform highlights how compliance-first strategies can deliver significant results. By involving legal teams at every stage and building explainable AI systems, the company improved operational efficiency without sacrificing compliance, as detailed in a Business Insider report.

In contrast, the Apple Card controversy shows the dangers of neglecting ethical considerations. The backlash against its gender-biased algorithms not only damaged Apple's reputation, but also attracted regulatory scrutiny, as reported by Bloomberg.

These cases illustrate the dual role of product managers – driving innovation while maintaining compliance and trust.

Related: Avoid AI Disasters and Gain Trust – 8 Strategies for Ethical and Responsible AI

The way forward

As the regulatory landscape for AI continues to evolve, product managers must prepare to adapt. Recent legislative developments, such as the EU AI Act and updates to HIPAA, highlight the increasing complexity of compliance requirements. But with the right strategies—early stakeholder engagement, transparency-focused design, and proactive risk management—AI solutions can thrive in even the most tightly regulated environments.

The potential of AI in industries such as healthcare, finance and legal services is huge. By balancing innovation with compliance, product managers can ensure that AI not only meets technical and business objectives, but also sets a standard for ethical and responsible development. In doing so, they're not just creating better products—they're shaping the future of regulated industries.