AI readiness in medtech

Scaling AI across the enterprise may no longer be a question of “when” but “to what extent.” Medtech companies should view AI and GenAI investments as core enablers of their enterprise strategy rather than as experimental projects. While executing a multitude of use cases can deliver value, a focus on depth versus breadth could be more beneficial.

Building distinct operating structures, such as establishing an AI COE, could help medtech companies cross-leverage talent and resources to weave AI and GenAI into their business. Such COEs, while not necessarily a requirement for success, should enable prioritizing and managing AI and GenAI investments and should include an explicit capability for value realization.

Sustaining the enthusiasm and momentum to scale AI involves a disciplined approach to assessing and making appropriate investments and communicating their value. AI appears to be another step on the digital transformation path for companies. By focusing on building and communicating an ongoing value narrative from AI use, companies could prevent disillusionment, especially as newer digital technologies become the next “shiny thing.”

Build enterprise architecture that collates key components involved in quickly creating and deploying AI and GenAI applications, tools, and capabilities. Such architecture should include access to open-source and proprietary platform capabilities to utilize large language models, sandbox environments, and common solution archetypes.

While initial experiments may have progressed, large-scale AI deployment involves a stronger focus on bridging the trust gap with AI, which GenAI could likely exacerbate. Some organizations would like to make AI a part of their culture and the way they do things going forward. This involves fostering willingness among business users by clearly demonstrating value, providing adequate training, and creating guardrails for the responsible use of AI and GenAI tools.

Medtech companies should establish clear guidelines and promote ethical practices around AI to help ensure responsible deployment. The string-of-pearls approach can be effectively leveraged if companies confirm AI use is harmonized with the evolving regulatory environment.