Pharma Supply Chains Tackle AI Gap Between Visibility and Action

Pharmaceutical supply chains have largely solved visibility but remain immature in prediction and weaker still in prescription — the core message from Business Session 4 at the Global Pharma Logistics Summit 2026 (GPLS 2026).

"We are good in terms of visibility now," said Vipul Jain, Vice President – Supply Chain Strategy and Analytics at Intas Pharmaceuticals, while rating prediction as less mature and prescription as an area with much further to go. His prescription for the industry: fix data quality first, but start applying AI in parallel rather than waiting for perfect foundations.

The session, moderated by Girish Bassavaraju, Country Head-India at Cosette Pharmaceuticals, brought together voices from pharmaceutical manufacturing, air cargo, warehousing, distribution and consumer healthcare — a spread that reflects how many hands a pharma shipment passes through before reaching a patient.

Why prediction alone is not enough

For the air cargo side, the real test of any AI system is whether a forecast triggers an intervention in time. "The value is created in the action based on the prediction," said Vijayant Vikram Malik, Area General Manager, Indian Subcontinent, at Etihad Cargo.

The window for that action varies sharply by failure mode. Weather disruptions may allow hours to respond, Malik explained, while handling and connectivity issues can leave only minutes. Monitoring shipment milestones can flag a potential failure, alert the relevant stakeholder and support corrective action before the milestone is missed — turning a data feed into a decision pipeline.

Where is AI adoption actually standing?

On the outbound side of pharmaceutical supply chains, adoption remains limited. "First step would be to train the supply chain professionals on where to use AI," said Ashu Gupta, Vice President – Supply Chain at Koye Pharmaceuticals.

She identified concrete candidate applications:

• Inventory replenishment and demand forecasting
• Warehouse picking
• Truck loading

Gupta framed management commitment, cost and the choice of application as the three variables that determine whether an AI project survives contact with the business.

Can AI replace human judgment?

Vineet Kumar, Senior Manager, SCM-Warehouse & Logistics at animal health company Elanco, kept people at the centre of the answer. "I consider AI as a complementary tool," he said, pointing to route changes, inventory risk assessment and warehouse efficiency as areas where AI can genuinely help.

His caveat is structural: data remains fragmented across ERP systems, warehouse management systems and transport management systems. Skilled staff must still assess AI recommendations and take the critical decisions — the tool proposes, the human disposes.

What about the missing demand signals?

Chintan Ghiya, Head Customer Care & Operations at Sanofi Consumer Healthcare India, speaking in his individual capacity, targeted a blind spot between trading partners. "We are still very, very heavily dependent on what's happening on just on the primary part," he said, describing an industry that plans on primary sales while secondary demand stays opaque.

He shared a case from a previous role in which a simple AI tool read distributor information from Excel, PDF and text files — an unglamorous fix that unlocked better secondary sales and stock data, feeding improved forecasting and supply planning. The example underlines that usable AI does not always require a platform overhaul; sometimes it starts with reading files nobody had time to consolidate.

What comes next?

Beyond operational efficiency, the panel widened the lens to product portfolio strategy, workforce planning and change management — the organisational terrain where AI projects typically succeed or stall. Data privacy, cybersecurity and the guardrails needed around AI tools also featured in the discussion.

The session closed on the outcomes that matter most in pharma logistics: better replenishment, fewer temperature excursions, and tools that help people protect product efficacy and temperature integrity across the chain. As the Intas and Etihad comments together suggest, the industry's next competitive edge lies not in seeing disruptions earlier, but in compressing the time between the prediction and the corrective act.