A new survey of senior life sciences executives reveals that 89% of biopharma AI pilots never progress beyond trial stage, despite widespread expectations of sales growth and cost savings. Data quality, fragmented systems, and a lack of global consistency are emerging as the biggest barriers to scaling AI across commercial operations.

Most biopharma AI pilots fail to scale, new industry survey finds

The report, The State of Data, Analytics, and AI in Commercial Biopharma, compiled from responses by 116 senior life sciences data leaders, reveals that 89% of AI pilots in biopharma fail to scale fully in an organization. The finding highlights a growing disconnect between ambition and execution as the industry races to embed AI across commercial operations.

High expectations, low follow-through

Confidence in AI’s potential remains strong. According to the survey, 86% of respondents expect at least a 5% increase in sales once AI initiatives are established at scale, while 55% anticipate cost reductions of 10% or more.

However, the failure rate of pilot projects suggests that the majority of organisations are yet to establish the data infrastructure and operational readiness required for enterprise-wide adoption. Leaders cite poor data quality, fragmented systems, and inconsistent data as key barriers to progress.

Many respondents described a gap between early experimentation and tangible business impact. Pilot schemes often deliver encouraging results in isolated settings but prove difficult to reproduce across markets and product portfolios.

Why scaling fails

The research identifies three interconnected challenges—trust, speed, and consistency—as the main obstacles to scaling AI in commercial biopharma.

1. Trust in data
   A large majority of leaders (73%) reported persistent data quality issues, citing incomplete or inaccurate information as the biggest brake on progress. Poor data reliability undermines confidence at every level of the organisation, from headquarters to field teams.

Several respondents noted that when data accuracy is questionable, commercial teams tend to default to personal judgement rather than analytical insight. As a result, AI recommendations are under-used, and confidence in predictive tools erodes.

2. Speed of insight
   Even when data quality is adequate, many organisations struggle to translate information into actionable intelligence quickly enough to compete. The survey found that 72% of companies spend up to 100 days each year reconciling healthcare professional (HCP) data from different systems before it can be used for analytics.

This administrative burden, described by some as an “operational tax”, delays the delivery of insights and diminishes the value of AI models. Respondents called for greater automation, streamlined data flows and real-time access to trusted datasets to shorten the path from data to decision.

3. Consistency across markets
   The third major challenge is a lack of alignment in how data is defined, structured and maintained across regions. Ninety-five percent of companies said they must manually remap global to local specialties, with most spending up to 100 days a year on this task.

This lack of a unified data model prevents organisations from creating a single, consistent view of their customers and hinders the deployment of global commercial strategies such as next best action (NBA) programmes. As one respondent commented, inconsistency between markets “directly prevents us from launching NBA initiatives”.

A consensus on the solution

Despite these challenges, the survey shows strong agreement on what needs to change. More than three-quarters (76%) of respondents said that global data harmonisation—the ability to manage customer and product data consistently across systems and geographies—is the single most important enabler of AI at scale.

Leaders increasingly see data management as a foundational rather than technical problem. Many described the need for long-term investment in governance, standards and maintenance, rather than short-term fixes or technology upgrades.

The report also notes that while automation and new analytical tools are seen as vital, their success depends on the quality of the underlying data. Several contributors warned that without harmonised, trusted data, even advanced generative AI models will fail to deliver meaningful insight.

Strategic implications for commercial biopharma

For commercial teams, the findings underline a growing divide between companies that have invested in data readiness and those still operating with fragmented systems. Organisations with strong data foundations are beginning to scale AI successfully, achieving measurable returns through improved targeting, faster decision-making and reduced duplication of effort.

By contrast, companies caught in the cycle of pilot-only experimentation face rising costs and declining stakeholder confidence. The survey suggests that the inability to scale AI is now one of the most significant competitive risks in commercial biopharma.

From a strategic perspective, several themes emerge:

• Operational efficiency: Manual data reconciliation and inconsistent standards create hidden costs that delay market execution and dilute early AI gains.

• Field engagement: Inaccurate or incomplete data erodes the credibility of digital tools among field teams, reducing uptake of AI-based recommendations.

• Governance and ownership: Successful companies establish clear accountability for data quality, with cross-functional teams managing standards and updates.

• Global versus local balance: A unified data model supports both global insight and local flexibility, enabling scalable next best action strategies.

Turning pilots into performance

The study concludes that while the vision for AI in commercial biopharma is well established, execution depends on solving practical data challenges first. Building trust, accelerating insight, and enforcing consistency are described as the cornerstones of progress.

Organisations that prioritise these areas are likely to move faster from experimentation to enterprise impact—realising the revenue gains and cost efficiencies that most leaders now expect.

The message from respondents is clear, from R&D to commercial: AI cannot scale without a strong data foundation. Establishing that foundation may require re-engineering data models, investing in automation, and aligning teams globally, but those who act decisively stand to gain a significant competitive edge.

Veeva’s full report, The State of Data, Analytics, and AI in Commercial Biopharma, will be discussed further at the Veeva Commercial Summit, Europe, taking place on 4–5 November in Madrid.

Key Findings: Commercial Biopharma AI Survey

• Pilot failure rate: 89% of leaders mentioned that more than half of their AI pilots failed to scale in their organization.
• AI readiness: 96% of leaders mentioned their data is not AI-ready for fully scaling pilots.
• Project abandonment rate: 67% of leaders have abandoned an AI initiative due to bad data foundation.
• Sales expectations: 86% of leaders expect a minimum 5% uplift in sales from successful AI programmes.
• Cost reduction: 55% anticipate at least 10% savings once AI is scaled.
• Data quality challenges: 73% report significant issues with the accuracy and completeness of their commercial data.
• Time to insight: 72% of companies spend up to 100 days annually reconciling HCP data across different sources.
• Global consistency: 95% must remap global to local specialties; 55% do this annually, and 84% spend up to 100 days annually on this task.
• Top enabler for scaling AI: 76% of respondents identify global data harmonisation as the single most important factor.
• Core pillars for success: Building trust in data, accelerating insight, and achieving consistent global standards.