Real-world evidence now supports new indications and safety decisions, and AI increasingly generates it. Boards should know where their company relies on it, how well the design fits the question, and how its credibility is shown.

Most new ventures in healthcare and life sciences fail for reasons visible early. Ten principles test a young company, and the same ten test every AI approval.

A reduction in workforce can protect an organization or quietly remove the capabilities it needs next. The difference lies in whether it follows a strategy.

The EU AI Act is law, and the July 2026 Digital Omnibus pushed its high-risk deadlines to 2027 and 2028. The delay is the risk: the inventory, vendor terms, and technical files it requires take longer to build than the time it buys.

I listed ten precision healthcare trends in 2022. Some reached patients and some faded; the difference is the evidence path, and it is the test boards should apply to AI.

The FDA's January 2025 draft guidance on AI used to support drug and biologic submissions is still a draft, but regulators, partners, and acquirers already expect the evidence it describes. This article explains the framework, what has changed since, and what boards should ask management to show.

Boards rarely decide with complete information. The failure is using one method for four kinds of gaps. This guide shows directors how to name each gap, match their questions to it, and record what they decided, from R&D programs to AI approvals.