LogiPharma research finds 96% of surveyed pharmaceutical supply chain leaders prioritising AI and machine learning, but regulatory concerns and operational readiness remain significant obstacles
Artificial intelligence and machine learning have emerged as the leading technology investment priority among pharmaceutical supply chain leaders, with 96% of respondents identifying the technologies as a priority, according to new research from LogiPharma.
The survey, conducted among pharmaceutical supply chain professionals ahead of the LogiPharma Digital Supply Chain Connect event, also found that advanced data analytics ranked as a priority for 73% of respondents, highlighting the growing emphasis on data-driven decision-making across the sector.
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The findings suggest that the debate within pharmaceutical supply chains is moving beyond whether artificial intelligence has a role to play and towards questions about where it can deliver measurable operational value and how it can be implemented within a highly regulated environment.
Demand forecasting leads AI applications
Among the areas attracting the greatest interest for AI deployment are demand planning and forecasting, inventory optimisation and logistics orchestration.
These applications could enable pharmaceutical companies to improve demand visibility, manage inventory more effectively and coordinate increasingly complex logistics networks.
However, the research also highlights a gap between strategic interest in AI and confidence in its ability to address some of the industry’s most difficult supply chain challenges.
More than half of respondents remain uncertain about whether AI can deliver a meaningful improvement in disruption prediction and mitigation, indicating that enthusiasm for the technology has not yet translated into universal confidence in its ability to manage supply chain disruption.
Regulation remains a major barrier
Regulatory uncertainty and compliance requirements were identified as the single biggest barrier to wider AI adoption.
That concern is particularly significant for pharmaceutical supply chains, where decisions and processes can be subject to stringent regulatory, quality and traceability requirements.
For companies considering AI deployment, the challenge therefore extends beyond selecting technology. Data governance, validation, accountability and integration with existing operational systems all become important considerations when AI is introduced into regulated supply chain environments.
Ben Sharples, Event Director at LogiPharma Digital Supply Chain Connect, said the industry conversation had moved beyond simply asking whether AI has a role in pharmaceutical supply chains.
“Today’s leaders are focused on where AI can create the greatest value, how it can be deployed responsibly and what foundations need to be in place to support successful adoption,” Sharples said.
Investment gap raises operational questions
One of the more significant findings is the difference between investment priorities for AI and network optimisation.
While AI and machine learning were identified as priorities by 96% of respondents, network optimisation was selected by 53%—a gap of 43 percentage points.
The disparity raises questions about whether companies are investing sufficiently in the operational infrastructure required to act on insights generated by AI.
Recent analysis from SkyCell has similarly highlighted what it describes as an “operational maturity gap” between the visibility that AI can provide and the systems available to respond when that visibility identifies a developing problem.
In practical terms, better forecasting or earlier identification of a disruption has limited value if organisations lack the network capacity, processes or decision-making systems required to respond quickly.
AI increasingly sits at the centre of digital strategy
The survey also found that automation, workforce upskilling and broader digital transformation were not specifically ranked as major standalone budget priorities.
This could indicate a shift in how pharmaceutical companies are approaching digital investment, with initiatives that might previously have been treated as separate transformation programmes increasingly being incorporated into wider AI and machine-learning strategies.
For pharmaceutical logistics, the implications extend across the supply chain, from forecasting and inventory management through to transportation planning and execution.
The findings point to a sector increasingly prepared to invest in AI, but still working through the practical questions of governance, compliance, infrastructure and operational integration.
The next phase of adoption is therefore likely to be less about demonstrating that AI can generate insights and more about proving that those insights can be converted into reliable, compliant and measurable improvements across pharmaceutical supply chains.
