Summary

“Let’s treat AI governance by design as the connective tissue of a product lifecycle ecosystem.”

AI governance is often introduced as another layer of oversight—more procedures, approvals, documentation, and checklists. Ankita Mishra offers a different way to think about it: governance should be designed into the digital health product lifecycle from the beginning.

In this episode of the Let’s Talk Risk! conversation, host Naveen Agarwal and Ankita Mishra explore how systems thinking and risk-based decision-making can help organizations innovate responsibly without applying the same level of rigor to every product or use case. They discuss flexible quality systems, the importance of defensible rationale, third-party AI solutions, supplier dependencies, privacy and security, model performance, and the need to monitor AI after deployment.

The conversation also addresses what AI means for quality, regulatory, and risk professionals. Rather than making human expertise less relevant, Ankita argues that AI increases the need for critical thinking, judgment, collaboration, and continuous learning.

Listen to the full 25-minute podcast or jump to a section of interest listed below.

Chapters

00:00 – Introduction and Welcome01:04 – Ankita Mishra’s Career Journey04:36 – Reframing AI Governance by Design06:40 – Shifting Critical Thinking Upstream09:11 – Matching Development Rigor to Risk12:20 – Building Defensible Risk-Based Rationales14:17 – AI Governance Across the Product Lifecycle16:03 – Evaluating Third-Party AI and eQMS Solutions18:22 – Preparing Quality and Regulatory Professionals for AI23:00 – Closing Takeaways on Responsible AI

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Suggested links:

LTR: Proactive AI Governance in MedTech.

LTR: Building Trustworthy AI and MedTech Readiness.

LTR: Evolving Regulatory Landscape for AI in MedTech.

Key Takeaways

* AI governance should begin with the design decision—not after the technology has already been selected or deployed.

* Start with the intended use and the underlying problem. The first question is not simply how to use AI, but whether AI is necessary.

* The level of lifecycle rigor should reflect product risk, regulatory status, and the consequences of failure. Not every requirement needs to be applied identically to every solution.

* A flexible, risk-based quality system depends on clear reasoning. Organizations must be able to justify both why a requirement applies and why it may not apply.

* Responsible AI governance extends beyond the algorithm. It includes privacy, cybersecurity, infrastructure, suppliers, contracts, deployment, maintenance, performance monitoring, and drift.

* Organizations evaluating third-party AI tools should understand whether their data will be retained or used for training, what information may be disclosed, and what additional controls may be needed.

* A sandbox approach can reduce uncertainty: start with a controlled, lower-risk application, evaluate whether it produces meaningful value, and scale based on evidence.

* AI will change professional responsibilities, but it will not eliminate the need for experienced judgment. Critical thinking, systems thinking, collaboration, and organizational knowledge will become even more valuable.

* Lifelong learning is no longer limited to formal training. Professionals can learn by engaging with thought leaders, attending conferences, following emerging standards, sharing ideas publicly, and allowing others to challenge their thinking.

Keywords

AI governance, responsible AI, digital health, systems engineering, risk-based decision-making, quality management systems, third-party AI, model drift, regulatory compliance, critical thinking

About Ankita Mishra

Ankita Mishra is the Digital Health Quality Director at Evinova, where her work focuses on AI governance and responsible innovation in healthcare, digital health product strategy, lifecycle management, GCP compliance, global standards, and quality systems.

She brings more than 20 years of experience spanning software development, biomedical engineering, medical devices, systems engineering, and software quality. Her career has included roles at AstraZeneca, Senseonics, Medtronic, Terumo Cardiovascular Systems, Integra LifeSciences, and Infosys.

Ankita holds a master’s degree in public health from Johns Hopkins University, an MS in biomedical engineering from Drexel University, and a BE in electronics from Nagpur University. Her work centers on enabling innovation and regulatory rigor to advance together rather than treating them as competing objectives.

Let’s Talk Risk! with Dr. Naveen Agarwal is a bi-weekly live audio event on LinkedIn, where we talk about risk management related topics in a casual, informal way. Join us at 11:00 am EST every other Friday on LinkedIn.

Disclaimer

Information and insights presented in this podcast are for educational purposes only, and not as legal advice. Views expressed by all speakers are their own and do not reflect those of their respective organizations.

Parts of this article were created using AI-generated content, which was subsequently reviewed, edited, and fact-checked by the author to ensure accuracy and alignment with our standards.

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