Midyear 2026 Check-In: AI Investment Skipped Pharma's Costliest Operational Problem

Access Part 1 of this interview on how digital twins run pharma supply chains, but says they miss the regulatory execution needed to launch changes across market.

Access Part 2 of this interview on how Orchestrator roles surged in pharma AI, but teams need computed plans, not titles, to close the skills gap.

In part 3 of a 3-part interview, Megha Sinha, founder and CEO of Kolter AI and Kamet Consulting Group, says the industry's AI bottleneck has shifted. For 3 years, model capability was assumed to be the constraint. “The surprise of 2026 is that the technology has now arrived, and the binding constraint turns out to be the organizational readiness, the employee sentiment and the data,” Sinha says, adding that sentiment is the most telling signal of where companies stand.

She points to a deeper surprise in where investment went. Capital poured into AI drug discovery and pilot programs, the frontiers that photographed well, while the harder, costlier problem, computing compliant execution of a geo expansion or lifecycle change across dozens of markets, drew almost no funding. That work remains manual, tribal, and reinvented on every program, which is the gap Kolter AI was built to close.

Sinha argues that pharma has shown real courage rethinking drug discovery with AI but hasn't extended that same willingness to operations. Looking to 2030, she sees 2 possible outcomes: a fragmented landscape of siloed, brilliant point solutions, or a connective layer that carries a single change across every function and market it touches. “My bet is on the second because the first cannot survive the pace AI-native discovery is about to create,” Sinha states. She notes that pressure is already visible in this year's wave of M&A activity, reshoring, and network redesign forcing decisions faster than manual processes can keep up.

“Strategy moves at the speed of a boardroom. Execution has moved at the speed of a spreadsheet,” Sinha says, framing the real choice facing pharma as whether to keep AI as disconnected dashboards or wire operations into one auditable backbone now, before 2030 arrives.