AI Value

AI experimentation time is over. Now it’s about value and accountability.

Life sciences companies invested early and aggressively in AI. Discovery teams deployed models to accelerate target identification, clinical groups experimented with protocol optimization, safety functions tested enhanced signal detection, and commercial teams piloted AI-assisted HCP engagement and medical writing. Early results looked promising, but the targeted value has been slow to materialize at enterprise scale. ​

The curve is steeper in Life Sciences

The top sixteen pharmaceutical companies spent $159 billion on R&D in 2025, down 3.6% from the prior year, as pipeline prioritizations forced difficult choices about where investment continues and where it stops. Biopharma’s internal rate of return on R&D investment has fallen to 4.1%, well below the cost of capital, while Phase I success rates have dropped to 6.7%, down from 10% a decade ago. ZS’s 2026 CDIO research found that nine in ten pharma technology leaders now see growth pressures from competitors’ scientific advancements to AI disruption and regulatory friction as active threats to business growth. The question is no longer “where can AI work?” but “where will AI drive the growth that matters?”

The AI value reckoning has started, and most enterprises are at risk of not passing it.

The organizations that will are making three design choices the majority systematically deferred. They are designing for trust, for accountability, and for capacity reinvestment. These are operating-layer decisions that determine whether AI investment compounds into enterprise capability or evaporates into activity. That is the work the strategy decks tend to leave out.