Mike King, Senior Director, Product & Strategy, IQVIA, is an experienced healthcare and life sciences executive with more than two decades of experience spanning product strategy, regulatory affairs, quality assurance, operations, and business transformation. Before joining IQVIA in 2022, King held senior leadership positions at Dentsply Sirona and GE Healthcare, where he oversaw regulatory affairs across complex international markets, including Europe, Russia, the Commonwealth of Independent States (CIS), Israel, the Middle East, Africa, and Asia. Earlier in his career, he held quality, regulatory, operational, and business roles at Bio-Rad Laboratories, Stryker, and Accenture, giving him broad experience navigating the intersection of healthcare technology, regulatory compliance, and commercial strategy.
IQVIA is a global provider of clinical research services, commercial insights, and healthcare intelligence serving the life sciences and healthcare industries. The company combines large-scale healthcare data with advanced analytics, technology, domain expertise, and its Healthcare-grade AI capabilities to help pharmaceutical, biotechnology, medical device, and other healthcare organizations support drug development, clinical research, commercialization, and evidence generation. Through its Connected Intelligence approach, IQVIA aims to turn complex healthcare data into actionable insights that can accelerate innovation and improve decision-making across the healthcare ecosystem.
Your career has taken you from consulting at Accenture through quality assurance and regulatory leadership roles at Stryker, Dentsply Sirona and GE Healthcare, and now into product strategy at IQVIA. How has that progression shaped your view of the long-term technology decisions life sciences companies are making around artificial intelligence?
My career to date has spanned consulting, manufacturing, quality, global regulatory affairs and healthcare technology, giving me the opportunity to see technology decisions from multiple stakeholder perspectives. Across each role, I have found that the most successful investments are those that connect operational and commercial performance with patient outcomes. That is particularly true for AI in the life sciences sector. While efficiency gains are important, the greatest long-term value will come from using AI to strengthen quality, regulatory compliance, decision-making, and ultimately, the delivery of safe and effective healthcare solutions in global markets.
You argue that AI could bring an end to the traditional five-to-seven-year technology replacement cycle. Why does adding AI make a quality or regulatory platform more permanent rather than accelerating its replacement?
Traditionally, companies replaced systems when better functionality became available and when existing provider contracts expired. AI changes that calculation by accelerating the speed of technological innovation. The value is no longer confined to the application itself; it sits in the data, workflows, governance and institutional knowledge accumulated over years of use. In the context of Good Practices, or “GxP,” life sciences applications, as organizations layer AI onto validated business processes and continuously improve outcomes through qualified human professional oversight, they create an intelligence asset that becomes increasingly difficult to replicate elsewhere. In that environment, replacing a platform is not simply a technology decision, it is a decision to rebuild years of accumulated organizational knowledge.
How does the quality of an organization’s data architecture and workflow infrastructure determine whether an AI implementation delivers meaningful value or simply reinforces existing inefficiencies?
AI amplifies the environment into which it is deployed. If data is fragmented, processes are inconsistent and governance is weak, AI will simply automate and exacerbate those inefficiencies. In contrast, organizations with standardized workflows, trusted data and clear ownership and governance can use AI to improve efficiency, consistency, compliance and decision-making. Digitalization and data readiness remain prerequisites for successful AI adoption, while ongoing data governance and AI governance are essential to ensure value, compliance and trust long after deployment.
What is the procurement paradox created when an organization trains AI systems on years of proprietary quality, clinical or regulatory data?
The paradox is that the more successful an organization becomes at using AI, the harder it can be to leave the platform that enabled that success. Years of quality, regulatory and clinical data can become a significant competitive asset when combined with AI, but they can also increase switching costs. The answer is not to avoid AI-driven value creation, but to ensure data ownership, interoperability and portability are built into procurement decisions from the outset.
You suggest that platform decisions made in 2025 or 2026 could influence technology strategies well beyond 2035. Are most compliance, information technology and finance leaders adequately accounting for that extended commitment?
Regulated life sciences companies are accustomed to making long-term investments. Decisions involving Enterprise Resource Planning (ERP), manufacturing, quality, clinical and regulatory systems have always required multi-year planning horizons because of validation, change control and operational impact. AI may extend some of these considerations, but many senior leaders are already experienced in balancing long-term technology risk, regulatory obligations and business value. The difference is that AI raises the strategic importance of data and governance, rather than fundamentally changing the need for long-term planning.
How should life sciences companies distinguish between “good lock-in,” where accumulated data and intelligence create a competitive advantage, and harmful lock-in that limits flexibility, interoperability or negotiating power?
Good lock-in is not about being unable to leave a platform but rather about not wanting to leave because the platform continues to evolve, protects your proprietary knowledge and allows new AI capabilities and agent-native workflows to be adopted as the technology matures. Bad lock-in occurs when the solution innovation from the existing provider stalls, interoperability is limited and years of accumulated knowledge become trapped within a system that cannot easily adapt or be replaced. The goal should be to retain ownership of your data and institutional intelligence while retaining the flexibility to take advantage of the next generation of AI.
As more capital is allocated to AI, predictive maintenance and automation, what risks emerge when organizations postpone replacing aging core systems?
The biggest risk is that aging platforms become bottlenecks to AI adoption. Legacy systems often contain fragmented data, manual or highly customized workflows and isolated processes that limit immediate automation opportunities. Organizations may invest in AI pilots, predictive analytics and intelligent agents only to discover that their core infrastructure cannot support scaled deployment.
There are broader business risks too. As technologies age, vendor support may diminish, specialist resources become harder to find and maintenance costs rise. At the same time, the gap between the current state and next-generation AI platforms widens, making future modernization efforts more complex and expensive. Organizations can find themselves investing to maintain the status quo without generating tangible improvements in process performance, product quality, patient outcomes, compliance or commercial effectiveness. In many cases, modernization and AI readiness need to progress together rather than as separate initiatives.
What technical and contractual protections should companies seek to preserve data portability, model portability and access to their institutional knowledge if they later change vendors?
Organizations should focus on contractual and technical protections that preserve ownership and accessibility of their information. This includes clearly defined data-export capabilities, open integration frameworks, documented APIs, data lineage, migration rights and transparency regarding how proprietary models interact with company information. Institutional knowledge may become one of a company’s most valuable assets, and it must remain portable, secure and controlled.
How should regulatory validation and change-control requirements influence decisions about upgrading, retraining or replacing AI-enabled systems in pharmaceutical and medical device environments?
In GxP environments, AI does not change accountability. Quality and regulatory professionals remain responsible for the governance, integrity and compliance of their systems and processes. AI-enabled technologies must therefore be supported by robust governance, risk-based validation, documented change control and sufficient explainability to withstand regulatory scrutiny. Qualified, professional human expertise remains an essential governance component of GxP AI.
What vendor-selection criteria should life sciences companies prioritize today to ensure that an AI-enabled platform remains secure, compliant, adaptable and economically viable over the next decade?
In the era of healthcare GxP AI solutions, organizations should be selecting a collaborative partner and not simply buying software. Compliant functionality is essential, and equally important is the vendor’s ability to evolve their solution with technological advances while protecting a company’s data, proprietary knowledge and regulatory obligations. The winning platforms will be those that combine deep life sciences expertise with flexible architecture, strong governance and a clear path to adopting future AI and agent-native capabilities without disrupting business operations, compromising global compliance, while positively affecting patient safety.
Thank you for the great interview, readers who wish to learn more should visit IQVIA.
