IQVIA Executive Says AI Ends Traditional Tech Cycles

Life sciences companies must fundamentally rethink their IT procurement strategies as artificial intelligence disrupts the traditional five-to-seven-year technology replacement cycle. According to Mike King, Senior Director of Product and Strategy at IQVIA, layering AI onto validated GxP workflows transforms software from a temporary tool into a permanent organizational intelligence asset. Because the value of these systems increasingly resides in accumulated data and institutional knowledge rather than the software itself, replacing a platform becomes a risky decision to rebuild years of learned expertise.

This shift means that platform decisions made in 2025 or 2026 will likely dictate corporate technology strategies well beyond 2035. King points out that while regulated life sciences firms are accustomed to long-term planning for enterprise resource planning and clinical systems, AI raises the stakes for data governance. Practitioners must distinguish between "good lock-in," where a platform continuously evolves to protect proprietary knowledge, and "bad lock-in," which occurs when a vendor's innovation stalls and traps valuable data in an inflexible system.

To navigate this procurement paradox, organizations must prioritize data portability and interoperability from the start. King advises companies to secure technical and contractual protections, such as documented APIs, open integration frameworks, and clear data-export capabilities. Furthermore, postponing the replacement of aging core systems creates severe bottlenecks. Legacy platforms with fragmented data and manual workflows cannot support scaled AI deployments, meaning modernization and AI readiness must progress as parallel initiatives.

Finally, King emphasizes that AI does not absolve life sciences companies of regulatory accountability. In GxP environments, quality and regulatory professionals must maintain strict governance, risk-based validation, and documented change control. Human oversight remains essential to ensure that AI-enabled systems can withstand regulatory scrutiny while safely accelerating drug development and improving patient outcomes.