Priya Setty
MS, MBA, PMP, RAC-Devices
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Geethapriya (Priya) Setty is a regulatory affairs strategist and systems builder with a deep belief in purpose-driven innovation. With over nine years of experience in global regulatory affairs and more than 15+ years in healthcare, she brings a unique blend of clinical insight and policy expertise to the evolving world of medical technology.Starting her career as an occupational therapist, Priya’s focus has always been on impact- adding life to years, not just years to life. Today, she leads regulatory intelligence and digital transformation initiatives at a global medical device company, where she designs scalable systems to track emerging regulations, accelerate compliance, and enable access to life-changing technologies. Her work includes spearheading the development of an AI-powered Regulatory Intelligence platform that cut manual effort by 94 percent and brought real-time insights to global regulatory teams. She has also led the enterprise-wide implementation of a global Regulatory Information Management platform, driving requirements gathering, process alignment, and change management to standardize regulatory data and submissions across her organization. She has supported cross-functional teams in advancing regulatory strategies for high-risk and breakthrough devices, contributed to early responses to policies like the EU AI Act and FDA's evolving digital health guidance, and partnered across R&D, clinical, and quality functions to help build compliance into innovation from day one. Priya holds dual MBAs and is certified in RAC (Devices), PMP, ISO 13485 auditing, and medical device compliance. She thrives at the intersection of technology, policy, and strategy, and is known for her ability to bring clarity to complexity. Her mantra "make a choice, and make it happen" guides how she leads, builds, and mentors. Whether supporting teams through regulatory change or architecting AI-enabled solutions, she remains grounded in the mission that first drew her to healthcare: to serve, to simplify, and to make healthcare innovation more human.
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Area of Expertise
From Pilot to Practice: Why AI Struggles to Scale in Regulated Industries
Artificial intelligence is generating excitement across regulated industries, from manufacturing and healthcare to government and critical infrastructure. Organizations are piloting AI to automate knowledge work, improve decision-making, and increase operational efficiency. Yet despite promising demonstrations, many AI initiatives never become dependable parts of day-to-day operations.
Why?
The challenge is rarely the model itself.
This session explores the gap between a successful AI pilot and sustainable enterprise adoption. Drawing on real-world experiences from regulated environments, including manufacturing, quality management, regulatory operations, and compliance workflows, the presentation examines why organizations struggle to move AI from experimentation into governed, scalable use.
Participants will explore five common barriers that prevent AI systems from achieving lasting business value:
• Lack of organizational and business context
• Fragmented and poorly governed information
• Inadequate evaluation and success criteria
• Weak integration into existing workflows and systems of record
• Unclear ownership of outputs, risks, and decisions
The session introduces the concept of the review paradox: AI can generate work in seconds, but without trusted evaluation methods, traceable evidence, and clear accountability, organizations often spend more time reviewing AI outputs than they save creating them.
Attendees will learn why scaling AI is not simply a technology challenge but an operational, governance, and trust challenge. The presentation offers practical questions leaders can use to assess whether an AI use case is truly ready to move from pilot to production.
Learning Objectives
By the end of this session, attendees will be able to:
1. Explain why successful AI pilots often fail to scale into sustainable business processes.
2. Identify common organizational, governance, and operational barriers that slow AI adoption.
3. Evaluate whether an AI use case has the necessary controls, context, and success criteria to support production deployment.
4. Recognize the role of evaluation, workflow integration, and human oversight in building trust in AI-enabled processes.
5. Apply a practical readiness framework to assess whether an AI initiative is ready to move from experimentation to operational use.
Beyond Validation: Governing AI That Changes After Deployment
For decades, organizations validated software once and trusted it for years. That approach worked because traditional software followed fixed logic. If the code did not change, neither did the behavior.
AI changes that assumption.
An AI system can produce different outcomes over time even when no developer touches the code. As data patterns shift, operating conditions evolve, users interact with systems differently, and models encounter situations they were never trained to handle, performance can quietly drift away from what was originally validated.
For manufacturers adopting AI for quality management, complaint handling, predictive maintenance, supply chain decision-making, and operational intelligence, this presents a new challenge:
How do you know an AI system remains trustworthy after deployment?
This session explores a growing governance gap facing organizations as AI moves from experimentation into production environments. Using manufacturing-focused case studies, including AI-enabled complaint routing and predictive quality decision support, participants will examine how AI systems can change behavior without any traditional software change occurring.
The session introduces the concept of moving from a validated event to a validated state. Attendees will learn why validation approaches designed for deterministic software are often insufficient for AI-enabled systems and will be introduced to a practical continuous assurance framework built around three operational controls:
• Predefined change boundaries
• Continuous monitoring for drift and performance degradation
• Targeted reassessment when meaningful changes occur
At the center of the framework is qualified human oversight, ensuring governance evolves alongside system behavior rather than lagging behind it.
While the examples focus on manufacturing environments, the framework is equally applicable to AI systems operating in government, maritime, healthcare, and other regulated industries where trust, reliability, and accountability are critical.
Attendees will leave with a practical model they can immediately apply to their own AI initiatives to help maintain confidence, performance, and governance long after deployment.
Beyond the Hype: AI Adoption Across Regulated Industries
Artificial intelligence is reshaping how regulated organizations approach submissions, compliance, post-market activities, quality management, and operational decision-making. Yet adoption remains uneven, largely unmeasured, and often misunderstood. While some sectors are perceived to be further ahead than others, there are few established benchmarks, limited standardized metrics, and little cross-industry data to validate those assumptions. In practice, AI adoption varies significantly based on organizational maturity, data readiness, governance models, regulatory expectations, and risk tolerance.
This panel brings together leaders from pharmaceutical, medical device, combination product, and regulatory technology organizations to examine AI adoption through a candid, cross-industry lens. As manufacturers of highly regulated products, these organizations face many of the same challenges: governing data, validating systems, managing risk, and scaling AI responsibly within complex operational environments.
Rather than focusing on success stories alone, panelists will discuss where their organizations actually are in their AI adoption journeys, what is working, where progress has stalled, and what lessons are emerging as AI moves from experimentation into governed use. The discussion will also explore perspectives from regulatory technology providers that support organizations across multiple sectors and maturity levels.
The session will focus on three key questions:
• How are organizations defining and measuring AI adoption, and if they are not, why?
• Where is AI delivering measurable value versus creating new governance, validation, or compliance challenges?
• What does a practical AI maturity model look like for regulated organizations seeking to scale AI responsibly?
Attendees will leave with a realistic view of AI adoption across regulated industries, a framework for assessing organizational maturity, and practical insights that can help advance AI initiatives in a compliant, sustainable, and business-focused manner.
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