Chapter 3:
Barriers to AI Adoption
Summary
While enthusiasm for AI in pharmaceutical supply chains is strong, scaling adoption remains a significant hurdle. Survey results reveal that the biggest barrier is internal resistance to change (70%), followed by regulatory uncertainty (58%) and a shortage of skilled talent or AI literacy (48%). Interestingly, only 24% cited budget constraints, suggesting that financial resources are not the main limitation - instead, cultural readiness and regulatory clarity are the key obstacles.
When asked what would most help justify greater investment, leaders placed the highest value on access to external funding or innovation grants (39%) and government or regulatory support (38%), closely followed by internal pilot success (37%) and scalable, low-risk AI pilots (37%). By contrast, peer benchmarks (9%) and industry maturity frameworks (5%) were rarely viewed as critical, implying that organisations want to see tangible, contextualised evidence of AI’s impact rather than external comparisons. This underscores the importance of practical proof points over theoretical roadmaps.
Looking ahead, respondents identified the factors they believe are most important to successfully scaling AI. The top choice was scalable technology platforms (32%), reflecting the need for flexible infrastructure that can grow with adoption. This was followed by strong leadership and change management (24%) and clear, measurable ROI (23%), highlighting that technology alone is insufficient without cultural and managerial alignment. Only 10% selected skilled talent and training programs, and 7% clean, integrated data infrastructure, even though these are often cited as foundational. This suggests that many organisations may underestimate the depth of ongoing investment required in people and data.
Together, these findings paint a picture of an industry at a crossroads: eager to accelerate AI adoption, but held back by cultural resistance and regulatory ambiguity. Moving from isolated pilots to scalable platforms will require not just advanced technology, but strong leadership, clear returns, and a workforce ready to embrace change.
Question 1: What are the biggest barriers currently preventing your organisation from scaling/increasing the adoption of AI in the supply chain?
(Respondents were asked to select three options)
Internal resistance to change
Regulatory uncertainty
Lack of skilled talent or AI literacy
Unclear ROI or business case
Competing digital priorities
Siloed or poor-quality data
Lack of budget or resources
No perceived need for AI
“What we hear directly from customers mirrors the survey data: resistance to change (70%), regulatory uncertainty (58%), and poor data quality (32%) are slowing adoption. Yet the same respondents highlight scalable technology platforms (32%) and strong leadership with clear ROI as success factors. We’ve seen adoption accelerate when companies connect siloed capabilities into orchestrated agentic flows — for example, linking supply planning agents with logistics visibility and quality agents — all running on a unified platform. Technology alone won’t transform operations; it’s when trusted data, agentic orchestration, and bold leadership converge that AI scales.”
Lucy Deus, Senior Vice President, Supply Network Products, TraceLink
Question 2: Which of the following would most help your organisation justify greater investment in AI-enabled supply chain solutions?
(Respondents were asked to select three options)
Access to external funding or innovation grants
Government or regulatory support
Internal pilot success
Availability of low-risk, scalable AI pilot programmes
Better cross-functional collaboration
Improved vendor transparency around AI model performance
Clearer vendor case studies
Executive sponsorship
Clearer alignment with ESG or sustainability goals
Proven ROI from peer companies
Industry-specific AI benchmarks or maturity frameworks
"If I had to highlight a couple of areas, I would point to the availability of low-risk programmes that help build credibility and prove the hypothesis behind these solutions. It’s really about establishing that evidence base so the industry can move forward with confidence.
I also think proven returns on investment, both from within the industry and from peers, can open the door for wider adoption. We hear plenty of success stories, but when you look beneath the surface, not all of them are truly embedded. What we still need to see are solutions that are firmly integrated and delivering tangible, lasting results."
David Ruiz Perret, Strategy & Execution Lead Digital Supply Chain, MSD
Question 3: In your opinion, what is the single most important factor in successfully scaling AI across a life sciences supply chain?
Scalable technology platforms
Strong leadership and change management
Clear and measurable ROI
Skilled talent and training programmes
Clean, integrated data infrastructure
Alignment with regulatory compliance
“Measuring ROI is very important, but I’d also say we’re in a bit of a bubble at the moment. Everyone is talking about solutions that claim to solve everything, but we need to distinguish between those that genuinely bring added value and those that are just promises.
Right now, there’s a lot of noise and hype, but not every solution is actually delivering on the costs and benefits being promised. We need to be clear-eyed in separating real impact from marketing.”
Stefano Chiei, Director Operations EMEA/EE, Advanced Bionics
