In the biopharmaceutical industry, 9 out of 10 AI projects fail due to lack of reliable data. A globally harmonized foundation is the key to transforming skeptics into ambassadors.
For years, the biopharmaceutical industry has accumulated immense volumes of data. Today, as the sector moves towards an AI-driven future, this data must become a true strategic asset.
A study shows that while 95% of biopharmaceutical companies actively pursue AI initiatives in marketing and sales, 89% fail to industrialize more than half of their pilot projects. This lack of results often stems from a lack of maturity: 67% of managers abandon their AI projects due to insufficient data quality.
Clean, reliable data is the foundation of any AI initiative, and 73% of executives say poor data quality is the biggest barrier to industrialization. However, most companies’ strategy is based on attempts to harmonize siled sources — an unsustainable approach in the face of continued volume growth. The data scientists I spoke with estimate that they spend up to 80% of their time preparing and cleaning data to make it usable.
For AI to move from failed pilots to value creation, we need a paradigm shift from fragmented management to a globally harmonized data base with proactive governance.
Poor data quality remains the main obstacle to the industrialization of AI
The industry’s reliance on imperfect and fragmented data has led to growing skepticism: 96% of executives believe their data is not ready for AI. The impact is visible on the ground: 72% of companies plan to use AI to summarize updates on healthcare professionals in preparation for visits, but adoption remains lagging.
Field teams reject AI recommendations because they don’t trust the underlying data. When a “next-best-action” model suggests an action based on a three-month-old affiliation change, the delegate doesn’t just ignore the suggestion: they lose trust in the entire platform.
Erika Husing, business analyst in commercial operations at GSK, sums up: “If we don’t trust the data, how can we draw conclusions from it? It is essential that we move from current skepticism of data to real promotion of data.”
The Real Cost of Manual Data Governance
The other resource drain comes from the countless hours spent manually mapping local specialties and healthcare professional typologies to global standards — a considerable administrative burden each time a market opens.
In a top 20 biopharmaceutical company, data scientists ask field teams to review customer segmentation data every six months, taking them away from their primary mission. Even more frustrating: this company estimates that only 10% of its data is clean enough to be used, and that only 1% is actually used. Faced with growing volumes, companies can no longer afford to resolve these issues locally.
A globally harmonized data base to enable AI at scale
Data management is inherently complex, with local subsidiaries maintaining their data differently to meet regional regulations. The result is a fragmented system making cross-national analytics and AI particularly difficult.
Before implementing a global data model, Bayer AG faced inconsistent definitions and a lack of a single customer view. “Our global data landscape was fragmented — different countries relied on different sources,” says Stefan Schmidt, head of digital capabilities at Bayer. “To have a complete picture, we needed a unified customer repository.”
For Bayer, this centralized foundation provided a single source of truth and increased trust in AI-generated insights. Field teams are now less likely to question the system and more willing to use its recommendations.
But the real efficiency gain lies in the data source itself. Starting from a better foundation, the challenge shifts from error correction to maintaining continued excellence through agentic curation.
Maintain high integrity data through curation combining human expertise and AI agents
For decades, the industry has relied on manual governance to maintain data quality. We now have the opportunity to elevate millions of records to a new level of quality by combining human expertise and agentic curation.
AI agents take care of repetitive tasks — source crossing, duplicate detection — by examining 100% of records daily, before a human data steward validates the results. Operating continuously, they capture changes immediately, often before they even appear in public records. This real-time precision allows relevant recommendations and avoids the frequent pitfall of informing field teams about events that they already know about.
By shifting the burden of curation to autonomous agents, we move from a reactive model — which breeds skepticism — to a proactive model. Agentic curation combined with human governance provides the verified and trusted data needed to industrialize AI.
We cannot industrialize what we do not have confidence in, and we cannot trust what we have not harmonized. A globally consistent data foundation allows biopharmaceutical companies to focus on leveraging data rather than cleaning it. It is this refocusing that will transform the biggest skeptics of AI into convinced ambassadors.
