The budgets for pharma digitalisation continue to expand at a pace where operational reality is struggling to keep up. In just four years, AI spending will nearly triple to $11.12bn, up from $4.79bn in 2026. Yet these numbers don't tell the whole story.
Looking at what's actually happening on the ground - 70% of digital projects are stalling and 89% of workflows are still trapped in spreadsheets. The industry is facing an "execution gap" that needs to be addressed before it gets even wider. The skill shortage becomes one of the main barriers that's holding pharma back from reaching digital excellence.
Bridging this gap requires direct dialogue between those who build the tech and those who use it on the shop floor. AUTOMA+ 2026, the closed-door pharmaceutical automation and digitalisation congress, explores the pace of digital transformation across every stage of pharma operations. Each track addresses a specific challenging point where the gap between ambition and execution is the widest.
The first and most visible area is Generative AI. According to a survey of senior biopharma leaders, 89% were unable to scale more than half of their AI initiatives, and only 5% of AI solutions made it beyond the pilot phase to production. The Executive Opening Panel "AI: Exploring New Frontiers, Facing New Challenges" at AUTOMA+ 2026 features cases that can help companies successfully integrate this technology.
The speakers on the panel are discussing the relevant questions: collaboration models between humans and AI, the real risks of moving fast in the industry and the shift from treating AI as a novelty to adopting it as a standard tool. Among the presenters offering an answer is Scarletred — a global leader in standardised skin imaging and AI-powered skin analysis. Its CEO Harald Schnidar and business developer Avani Gupta show the real case on how they've moved beyond the "human vs machine" debate and how AI helped them to enhance human decision-making.
The next area where digital excellence is needed is R&D. In many high-throughput lab environments, scientists still spend up to 10 hours per week manually processing experimental data. Saving that time is crucial for efficiency.
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