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5 Insights on Data Foundations From Top Pharma Leaders
Feb 05, 2026 | Sebastian Wurst
Feb 05, 2026 | Sebastian Wurst
As the pharmaceutical industry moves from AI experimentation to execution, the scale of its ambition is undeniable. Our recent State of Data, Analytics, and AI in Commercial Biopharma report shows that 86% of leaders expect a 5%+ sales uplift from AI. Yet, 89% of leaders fail to scale more than half of their AI initiatives because data foundations lack the integrity and connection required for sustained growth.
The gap between ambition and reality is widening. To close it, leaders must stop treating data quality as a background maintenance task and start treating it as a strategic imperative.
Pharma data leaders from Bayer, Boehringer Ingelheim, GSK, and Orion Pharma provide a roadmap for turning pharma’s AI ambition into reality. These leaders aren’t just piloting; they are operationalizing.
Here are five strategic insights from them on building the foundation for scalable AI.
1. Shift the data conversation from cost to value
For years, reference data and master data management (MDM) were seen as necessary but unstrategic back-office costs. This perception created a barrier to investment: 53% of leaders cite budget constraints as the main reason for poor data quality. The data gap prevents AI initiatives from scaling.
Raffaele Torti, former global customer engagement lead at Orion Pharma, recognized data would be a strategic enabler, powering business priorities like AI. To get organization-wide buy-in, he had to stop talking about data governance. Instead, he started demonstrating how a fixed foundation would benefit the organization by directly expanding their market reach and targeting potential.
“The crucial element was demonstrating the impact of gaining full visibility of all potential leads, leading to an expansion of our target list that potentially can drive at least 10% increase in sales.”
2. Rethink and adapt your data systems
With the need to reevaluate data strategies, the selection of next-generation CRMs, and the mandate to scale AI globally, this is the perfect moment to rethink systems.
Last year, Boehringer Ingelheim announced their global move to OpenData. Alexander Ullrich, head of platforms and data, says a unified data strategy is essential for de-risking major infrastructure changes, like their migration to Vault CRM.
“A key driver for us was simplifying the upcoming CRM migration. We knew we had to get customer MDM right first. Therefore, implementing a single data provider before the Vault migration was a major priority to de-risk the project.”
3. Win the field’s trust with speed and accuracy
A global data strategy is only as successful as its adoption in local markets. Our survey reveals that when field teams believe the data isn’t trustworthy, they rely more on their intuition than their CRM.
Leaders at GSK, Bayer, and Boehringer Ingelheim say the true measure of success is trust by local teams. For Erika Husing’s team at GSK Nordics, data change request (DCRs) resolution time is an important factor to ensure data accuracy and freshness. That helps restore field confidence after dealing with frustrations from legacy systems.
“The win for the team is that we have improved the resolution time of DCRs. In Sweden, it’s around two hours, which I think is extremely good, compared to what the field teams got used to with our legacy data.”
4. Build the backbone before the brain
While the industry scrambles to launch AI pilots, Hari Krishna Iyer, business capability lead at Bayer, reveals that their success came from doing the opposite: prioritizing the “boring” work first.
Bayer started their data journey years ago, long before their current AI strategy, specifically to “get the basics right.” By resisting the urge to leapfrog to advanced analytics and instead focusing on global harmonization, they built a trusted environment that now serves as a high-speed launchpad for innovation. The lesson? If you want AI speed later, you need data stability now.
“We started this journey two to three years back to get our basics right. Our AI strategy came way later. But it actually helped us, because data is the backbone for AI.”
5. Assess your data quality
Reference data has evolved dramatically in the last few years to provide the foundational, high-quality data necessary for scaling enterprise-wide AI. Conducting a data assessment helps reveal roadblocks in your data that may prevent you from achieving key business outcomes.
As Andreas Hackl from Boehringer Ingelheim notes, a data assessment allows teams to evaluate their current data alongside a vendor’s technological improvements. Then, teams can evaluate how those improvements meet their business requirements. For example, OpenData has continually invested in technologies like agentic data curation to ensure AI-ready quality, coverage, and consistency.
“We did a data assessment with OpenData five years ago and it turned out that the quality at that time was not sufficient. But this time, the quality is really good.”
The future belongs to the data-ready
The era of “good enough” data is over. A broken data foundation isn’t just a technical hurdle; it’s a strategic liability that keeps your best AI ambitions grounded. As we move through 2026, the divide between leaders and laggards will be defined by data integrity. Focus on standardization today so you can lead on innovation tomorrow.
The roadmap provided by leaders at GSK, Bayer, and Boehringer Ingelheim is clear: Stop fixing the past and start architecting a future where data doesn’t just support your strategy, it accelerates it.
We are proud to partner with these forward-thinking teams. The real-world progress we are seeing across the industry gives us the energy to keep pushing for new ways to deliver the trusted, high-quality data that Biopharma deserves.
See how OpenData helps biopharma leaders build a cleaner, faster foundation for AI.
When you are ready to benchmark your own data quality, reach out for a free assessment to identify specific opportunities for improvement.