Six first moves for AI in the pharma cold chain

The questions that matter most to heads of logistics and supply chain in pharma are not whether AI is a bubble, whether it can deliver, or whether it is regulated-industry-ready. What industry leaders really need to know is: where to start, what to fix first, and how to proceed while keeping your quality function happy?

The following recommendations address these questions, without requiring a full transformation program to get started.

1. Audit your data foundation to ensure it’s watertight

With poor real-time data availability, AI recommendations stop being useful and start being noise. If your data availability isn’t close to 100%, that’s your first investment, not the AI layer on top. Validated data, qualified locations, and curated semantic context are the unglamorous parts of the problem, but they decide everything that follows. Set a high threshold and seal the leaks before you scale the AI.

2. Pick one narrow use case

Trying to scope AI across the entire function on day one is unlikely to succeed. Choose a defined trade corridor for a daily risk brief, or a specific set of lanes for a packaging optimization review. Measure outcomes rigorously against the status quo. If the numbers stand up, expand. If they don’t, you’ve learned something for the cost of one project rather than ten.

3. Make lane risk assessment dynamic, not static

A risk score produced at lane qualification and never revisited is no longer fit for purpose. The shipment data exists to keep that score current. Move to a model where lane risk is a living metric, updated as new shipments complete, and use it to graduate lanes to lighter-touch monitoring over time, or to escalate quickly when the signal deteriorates. First-launch conservatism remains the right starting point but stops being permanent.

4. Replace your morning dashboards with a morning decision brief

If your team still starts the day by scanning dashboards for anomalies, replace that with an automatically generated risk brief that surfaces the handful of shipments that warrant attention, with reasoning, geography, and recommended action attached. The team stops processing data and starts processing decisions. Start with one trade corridor and run the brief in parallel to existing escalation for a defined period.

5. Bring quality and validation in from day one

Running a proof-of-concept first and then asking QA to validate it risks slowing down an AI initiative. AI in cold chain operations can enable advanced automation of data extraction and comparison against pre-qualified reference sets, and involving quality teams from the design stage helps ensure this stays within your existing validation practice.

6. Position AI as an analyst, not an authority

Treat AI output as decision support, not decision authority. Keep the qualification trial, the escalation, and the final call in human hands. This is what makes AI defensible in a regulated industry, and it removes the biggest blocker to adoption. The value sits in compressing the analyst’s workload, not in replacing the operator’s judgement.

If you only do one thing

Of the six actions, #1 offers the highest leverage, so audit your data substrate. Without a watertight data foundation, any AI use case springs a leak. If it is not at 99.99% real-time availability, validated end-to-end, and curated with the semantic context to allow AI to reason rather than pattern-match, none of the other recommendations will deliver what they should.

Read the whitepaper

From real-time data to real intelligence:
How AI is reshaping the pharmaceutical cold chain

This whitepaper draws on insights shared by Gísli Herjólfsson (CEO at Controlant), and Saddam Huq (Director of Cold Chain & Logistics at GSK) 15 April 2026 at LogiPharma EU, Track B: Delivering Next-Gen AI Supply Chains.