Orchestrating the Pharmaceutical Future: Living Decision Engines and AI Integration

PharmTech connected recently with Megha Sinha, CEO, Kamet Consulting Group to look ahead into what’s in store for the industry in 2026. Sinha explores how the pharmaceutical sector is leveraging intelligent orchestration layers and AI-driven decision engines to navigate complex geopolitical risks and manufacturing inefficiencies. The discussion focuses on utilizing digital twins and unified knowledge layers to integrate regulatory intelligence and supply chain data, thereby accelerating speed-to-market. Additionally, Sinha highlights the necessity of cultivating a digitally fluent workforce capable of supervising these automated systems to manage end-to-end product lifecycles.

PharmTech: How is the sector reimagining supply chains to protect against geopolitical disruptions?

Sinha: By 2026, I expect leading organizations to design supply chains around tariffs and geopolitics as encoded rules, not just risks on a slide. The shift I see is from static network maps to living decision engines: platforms that connect products, SKUs, licenses, sites, tariff exposure, and regulatory requirements in one knowledge layer and then let you simulate, “If this lane fails or this tariff goes live, which tech transfers, marketing authorization holder changes, label updates, and tenders are affected, and what’s the fastest viable plan B?” Practically, that means regionalized but orchestrated networks: dual or backup sites, local finishing, and pre-baked regulatory pathways, all managed by an orchestration layer that can re-route both physical product and regulatory work when the next disruption hits.

What role do AI and digital technologies play in improving drug discovery and manufacturing efficiency?

AI is starting to look less like a collection of pilots and more like an embedded reasoning layer. In discovery, AI will increasingly sit on top of curated scientific and clinical knowledge, prioritizing what to explore—targets, molecules, indications—rather than “replacing” the science. In manufacturing, the real step-change will come from agentic systems that combine digital twins, process data, and regulatory rules to recommend parameter changes, highlight risks, and propose implementation plans instead of just flagging anomalies. The blockers are familiar: fragmented data, weak knowledge models, and clunky user experience. Until organizations invest in a structured lifecycle knowledge layer and interfaces that feel like a co-pilot—“Here is the proposed plan, here are the assumptions, accept/change/reject”—AI will stay powerful in pockets rather than truly end-to-end.

How can organizations bridge the widening skills gap created by rapid digitalization of pharmaceutical manufacturing?

I think the skills gap is often framed in the wrong way. The goal is not to turn operators, scientists, or regulatory experts into software engineers; it’s to help them understand the cross-functional nature of their own business and work effectively with digital systems. The leading organizations will:

1. Create T-shaped “orchestrators” and translators, people deep in tech ops, quality assurance, regulatory affairs, or supply who also understand how the business fits together end-to-end and who can work with AI-generated impact assessments, task lists, and critical paths.
2. Train teams to supervise agents, not configure tools, teaching experts how to interrogate a system-generated lifecycle or supply plan (“Why did you sequence countries this way? What happens if the US site is delayed 3 months?”), rather than expecting them to build workflows from scratch.
3. Embed learning in live programs, using real rebrands, site moves, or portfolio clean-ups as “labs” where people learn how to work with orchestration engines and refine the underlying rules.

The goal isn’t a workforce of software engineers; it’s a workforce fluent enough in their own cross-functional reality to collaborate with intelligent systems that are orchestrating work on their behalf.

Which recent innovations have had the broadest impact on cost, quality, or speed-to-market in manufacturing operations?

The most powerful innovations join plant-level optimization to lifecycle and market decisions. Digital twins and advanced analytics are already improving yield, stability, and cycle times. The next frontier is when those same models plug into a knowledge-driven orchestration layer that understands products, markets, licenses, and change rules. For example, when a process change or site move can automatically trigger a proposed set of filings, label updates, and implementation waves by country, complete with timing options and trade-offs. That’s where I see the broadest impact: when process innovation, regulatory intelligence and execution planning sit on one backbone and AI can propose end-to-end paths from “idea” to “product released in 40 markets,” with humans focusing on judgment calls, not stitching spreadsheets together.