The AI edge: Redefining commercial excellence in 2025

Biopharma companies are increasingly turning to artificial intelligence (AI) to enhance customer-focused commercial strategies, driving more tailored engagement and improved operational efficiency. But unlocking AI’s full potential hinges on strategic investment in clean, unified data. Without this foundation, even the most sophisticated AI tools may struggle to deliver meaningful impact or scale beyond initial pilots.

Forward-thinking biopharma leaders recognise that the journey to AI excellence begins with trusted, well-organised data. In the coming year, prioritising data integrity and regulatory alignment will enable organisations to tap into AI’s transformative capabilities – from streamlining medical, legal, and regulatory (MLR) workflows to equipping field reps with actionable insights and scaling advanced analytics.

Prediction 1: Clean data will drive compliant AI innovation across Europe

The recent wave of AI innovation has fallen short of transforming commercial life sciences. In 2025, European biopharmas that unlock harmonised internal and external data will start to reap commercial rewards.

Biopharma organisations will combine off-the-shelf AI engines with more harmonised, clean data. Acquiring data from trusted, internally verified sources will lead to greater confidence in AI-generated outcomes. This will make it easier to scale pilots from single-market, single-brand solutions across the enterprise.

The EU recently introduced the Artificial Intelligence Act — the first comprehensive AI regulation by any regulator, designed to ensure that AI is developed and used safely. Along with existing European data privacy rules, European biopharmas will have clear principles to support future investment and innovation. Commercial success will come to those that clean up their data, secure new sources, and interrogate them within this regulatory framework.

Prediction 2: AI will streamline MLR content review process

Growing AI use cases for content creation, hygiene, and quality checks will drive record-high content volume, making it more difficult to get relevant messages to market. As a result, content teams and agency partners that focus AI investments on both targeting high-value content creation alongside improving MLR review will be the first to see ROI.

By decreasing review cycles and reducing cycle times, AI-empowered MLR teams will accelerate compliant, accurate commercial content, despite the growing volume. These efficiencies will help move MLR review from the last stop in the content cycle to a proactive role with greater visibility and input into the content creation engine, further reducing rework.

Organisations that eliminate complexity in their content supply chain by classifying content using standard taxonomy, removing bespoke solutions, and harnessing hosted large language models will move faster and more efficiently. Content and MLR teams that prioritise content quality over content quantity will be early commercial leaders in delivering value from AI.

Prediction 3: People-first advanced analytics will unlock AI’s potential

As commercial organisations consider AI use cases for advanced analytics, they will also be forced to confront AI’s limits. For example, an AI-generated “next best action” would need to consider a wide range of variables that are not easily accounted for, from a rep's call plan to challenging HCP access, to incentive compensation plans. If the recommended next best action is one a rep can’t – or won’t – act on, AI is just adding more noise to the system.

As a result, early leaders will focus on investments in people to get the best ROI from carefully selected initiatives. AI use cases in advanced analytics will be most successful when companies spend time upfront defining problems, structuring data, and training users to act on insights. Early leaders will build a change management culture that addresses knowledge and skills gaps.

But AI tools that are bolted onto disconnected systems or need complex integrations with business intelligence tools will also slow down insights and discourage user adoption. AI that augments decision-making and is embedded in users' workflows – similar to how navigation apps guide drivers through traffic in real-time – will see wider use and set the stage for long-term ROI.

The future of AI in life sciences is full of promise. From expediting content reviews to advancing analytics, AI has the potential to transform processes and enhance customer centricity. However, success will require deliberate strategies: investing in clean, reliable data, embedding AI into workflows, and empowering teams to utilise insights effectively. Companies that adopt these practices will lead the industry’s evolution, achieving significant growth and value in the years ahead.