H1 2026 in Review: AI Adoption, Onshoring, and Data Gaps

Back in December 2025, several industry leaders staked out predictions for what 2026 would bring to drug development and manufacturing. Now, at the midpoint of the year, many of those experts return to see how their predictions held up. They find a pattern showing up across nearly every prediction: the tools arrived quickly, but the organizations meant to run them didn't catch up nearly as fast.

When referring to AI in drug discovery, Manish Garg, Hikma Pharmaceuticals, originally called it a race for speed. Six months in, he's revised that read entirely, telling PharmTech, "What's become clearer in the first half of 2026 is that the more meaningful shift is happening at the strategy level, not just the speed level."

Regulators are catching the same gap from the enforcement side. Mike Stenberg, LGM Pharma, flags a milestone warning letter as proof that the oversight is already here. "Earlier this year, FDA issued its first warning letter that specifically cites improper use of AI in manufacturing with a major oversight related to specifications and procedures," he says.

Underneath both trends sits a harder problem: data. Remco Munnik, Arcana Life Sciences, tracking Europe's shift toward database-driven submissions, put it as "AI only works when the underlying data governance is solid."

Elsewhere, the forecasts are holding steady; US onshoring is advancing selectively, biologics approvals are climbing, and Campbell Bunce, Abzena, says the industry's trajectory matches almost exactly what was predicted six months ago. Megha Sinha sums up where that leaves manufacturers, saying, "the technology has now arrived, and the binding constraint turns out to be the organizational readiness, the employee sentiment, and the data."

The full picture, including what each expert got right, what they missed, and where they're recalibrating for H2, is best heard in their own words.

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