Founder, Kolter AI | Managing Partner, Kamet Consulting Group | Pharma & medtech M&A integrations, divestitures, tech transfers & CMC changes | Ex-PwC

Megha Sinha

Megha Sinha, Kolter AI, talks on how Orchestrator roles surged in pharma AI, but teams need computed plans, not titles, to close the skills gap.

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

In part 2 of a 3-part interview, Megha Sinha, founder and CEO of Kolter AI

, returns from earlier in the year to speak with 

 about how the industry has developed in the first half of 2026. She turns to the skills gap behind pharma's AI rollout. The orchestrator role, she says, has gained the most visible traction across the industry, but too often it's been title-deep rather than skill-deep. "People got called the orchestrators without being taught how to orchestrate or use the engine to help them," Sinha says. That gap has been especially stubborn in lifecycle change execution and geographic expansion, where no system existed to generate a plan for teams to challenge. "You cannot train a team to pressure test a plan for site transfer across 40 markets when no system generates that plan," she notes, arguing the absence of a tool, not a lack of training, hid the real problem.

Sinha says that is now shifting as pharma companies bring engines like Kolter AI into live programs, computing an entire cross-market change, including filings, label updates, and country-by-country implementation waves, before a single affiliate is contacted. Local teams then validate a computed plan rather than building one from scratch over months, a shift she frames as a change in the job itself, from constructing plans to interrogating them.

She also pushes back on industry timelines that place connected, cross-functional orchestration a decade out. "The connective tissue that everyone described as years away is exactly what we built," Sinha says, describing the missing piece as automated impact assessment spanning regulatory, quality, manufacturing, and labeling functions. Companies approaching Kolter AI, she says, aren't asking whether this kind of orchestration is possible after years of doing it manually. They're asking how quickly they can adopt it, which Sinha takes as confirmation that the shift from aspiration to operational reality is already underway.

Videos

Megha Sinha, Kolter AI, talks on how orchestrator roles surged in pharma AI, but teams need computed plans, not titles, to close the skills gap.

Midyear 2026 Check-In: Megha Sinha on Pharma's AI Skills Gap

Megha Sinha, Kolter AI, discusses how digital twins run pharma supply chains but says they miss the regulatory execution needed to launch changes across markets.

 Midyear 2026 Check-In: Agentic AI Cuts Deviation Investigations by 50%

Megha Sinha, founder and CEO of Kolter AI and Kamet Consulting Group, 

spoke with PharmTech at the beginning of 2026 about her predictions for the industry this year.

 Now that the year is halfway through, In part 1 of a 3-part interview, she discusses how she believes that the infrastructure around digital twins is "Half built, and the built half is the easier half."

Companies such as Merck, Novartis, and BMS now run real-time what-if simulations on select S&OP processes, and the market for plant-level digital twins has surpassed a billion dollars. Inside the plant, agentic systems are already detecting out-of-spec results, pulling batch records, drafting root cause analyses, and cutting investigation time by 50% or more in validated GxP environments. "That is a shift from AI as a productivity layer to AI as an operational participant," Sinha notes.

But Sinha argues these systems stop short of where the real cost lies: translating a network, process, or manufacturing change into its regulatory consequences market by market. A minor change on the plant floor, she explains, can trigger a major variation requiring prior approval in Brazil, a six-month grace period in Germany, or a bundling opportunity across other markets, none of which existing digital twins or agentic tools are built to compute. Tariff planning, she adds, is still largely a quarterly review exercise rather than a live decision engine.

That gap between physical execution and regulatory execution is what Sinha says Kolter AI was built to close. "To me, that is the next frontier, and companies should be moving on it now," she says, describing an engine that takes a network or process decision.

Megha Sinha, Kolter AI, discusses how digital twins run pharma supply chains but  miss the regulatory execution needed to launch changes across markets.

Megha Sinha

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