Winners with AI and data quality aren't the organizations with the largest budgets, most data or highest IT spend. They’ve just built the best governance foundations.

There’s no debate. Today, pharmaceutical, biotech and life sciences companies are making AI a strategic priority. The ambition is clear and even heroic: Accelerate drug discovery, improve clinical trial design, automate routine tasks, generate real-world evidence and bring therapies to patients faster.

The pharma-specific value estimate is substantial, too. McKinsey Global Institute estimated generative AI could unlock between $60 to $110 billion a year in economic value, spanning drug discovery, clinical development, commercial functions, and medical affairs.1

McKinsey recently shared a more recent analysis on agentic AI for Pharma and Life Sciences, estimating revenue growth potential of an additional 5 to 13 percentage points from successful deployments (and an estimated EBITDA increase of 3.4 to 5.4 percentage points over the next three to five years).2

What is far less clear is whether today’s pharma- and lifesciences-siloed data foundation are capable of supporting the weight of its AI ambitions and potential.

True progress (like successful science) requires real measurable, observable and repeatable evidence, which is often lacking in AI deployments today. Still, the cost of the gap between reality and potential is measurable now in months, not just basis points.

Seven months before work even starts

A 2024 Frost & Sullivan survey of 300 life sciences professionals — spanning commercial, Health Economics and Outcomes Research (HEOR), and clinical development teams — put a precise number on the industry's data problem. On average, life sciences teams spend seven months integrating and cleaning data before they can use it to generate a single insight. And approximately 70% of teams reported spending six months or more in this pre-analytics preparation phase alone.3

That’s seven months of data remediation before any AI model training, any trial analysis runs or any commercial decision gets made.

For the largest life sciences enterprises (specifically, those with over $1 billion in revenue) the burden is heavier, as those organizations wrestle with data sourced from an average of four different data providers, integrated with the help of four or more consultants (on average), across siloed teams with uneven internal data expertise.

This is not just a technology problem, as the root issue is a governance problem. The absence of consistent data and metadata standards, defined ownership, and quality controls across the data lifecycle is what produces seven-month prep cycles. And it is precisely what a mature governance strategy is designed to eliminate.

The AI ambition gap in pharma

BCG's landmark 2024 study of over 1,000 senior executives across 59 countries found that 74% of companies have yet to generate tangible value from their AI investments, still trapped in pilots and proofs of concept that never scale.In life sciences specifically, the stakes are higher still. In fact, BCG found AI drives 27% of its value in biopharma from R&D alone, which is the single largest category of AI-generated value in the sector, with commercialization and medical affairs applications adding further upside potential.4

BCG's analysis of what separates the 26% of AI leaders lacks ambiguity. The primary differentiator is not model sophistication, compute capacity or the size of the AI team. It is a resource allocation discipline.

AI leaders follow a 70-20-10 principle:

70% of investment into people and processes
20% into technology and data infrastructure
10% into algorithms.

And among all technology capabilities, BCG identifies data quality and management as the single most important factor for successfully scaling AI. Not cloud infrastructure. Not tooling. Not model performance. Data quality.

The Frost & Sullivan findings explain exactly why. When HEOR and clinical development teams report "data management" is their single greatest barrier to insights they are describing the same deficit BCG identifies as the root cause of AI underperformance.5 Organizations cannot scale AI on data they spent seven months trying to make usable.

Failed trials, submissions and real-world evidence

Over half of clinical development teams identified linking disparate data sources, data quality and data context as top challenges. And these are problems that, in trials can translate directly to delayed database lock or Complete Response Letters from the FDA. The impact can cost a pharma company 12 to 18 months and hundreds of millions in delayed revenue.6

Life sciences organizations operate under 21 CFR Part 11, GxP data integrity requirements, and ICH E6 Good Clinical Practice guidelines7; these are frameworks that require auditable evidence of how data was created, transformed, and used. Best-practice frameworks, including Appsilon's Data Governance Best Practices for biopharma, make clear that governance infrastructure capable of meeting these standards also directly accelerates R&D timelines, reducing the remediation cycles that currently consume those seven months before any analysis begins.

In life sciences, data without context is a liability. A genomic dataset, a clinical trial result, or a manufacturing record means something different depending on how it was collected, under what consent, for what population, and under which regulatory framework.8 As FDA and European Medicines Agency (EMA) guidance increasingly demands traceable data lineage and documented governance decisions across the AI lifecycle, context becomes the differentiator between a defensible model and an unauditable one. Collibra's approach treats context (including provenance, lineage, business meaning, regulatory tags) as a first-class layer of the data itself, not an afterthought bolted on for compliance reporting.

The investment case: What governance actually returns

The ROI argument for data governance in pharma is not theoretical. It compounds across every phase of the drug development and commercialization lifecycle.

Various industry benchmarks and analysts place data governance and security at roughly 8–12% of total analytics and AI program spend as a cross-industry baseline. For life sciences organizations operating under regulatory data integrity requirements, the defensible range rises to 10–15% of total BI and analytics investment, with AI-mature programs allocating toward the higher end.1 Most pharma organizations are spending well below that floor — and absorbing the difference as remediation cost, delayed timelines, and AI initiatives that stall because their data cannot be trusted.

Research found that organizations achieving strong AI outcomes invest up to four times more, as a percentage of revenue, in data quality and governance than those reporting poor results. And, life sciences organizations that have invested deliberately in data programs report an average 124% ROI on data investments, per Hakkōda's Healthcare State of Data 2024.9

The data governance market is 9 And life sciences is among the fastest-growing end-user segments, driven by AI adoption, regulatory pressure and the expanding complexity of real-world data environments.

What governance-mature pharma organizations do differently

The life sciences organizations generating real AI value share a recognizable pattern.

They govern before they scale. Governance frameworks applied from protocol design through data lock eliminate the integration debt that produces seven-month preparation cycles. Retrofitting governance onto a scaled AI program is exponentially more expensive than building it in from the start, and we see that both in time and in the organizational credibility lost when AI outputs cannot be defended.
They unify data ownership across the product lifecycle. From raw trial data to derived biomarker endpoints to real-world evidence inputs, high-performing organizations assign clear stewardship at the dataset level, eliminating the ambiguity that causes inconsistencies to compound across systems, study phases and business units. The Frost & Sullivan finding that siloed teams are among the most significant barriers to effective data use is, at its core, a governance design problem.
They pair policy with purpose-built tooling. Governance policies without technology infrastructure produce compliance theater. Leading pharma organizations deploy metadata management platforms, automated data lineage tools and quality monitoring systems that make governance operationally sustainable at the scale modern drug development demands, across four data providers, multiple consultants and geographically distributed teams.
They treat AI readiness as a governance outcome. BCG's 70-20-10 framework is instructive: the organizations achieving outsized AI returns invested in data and process infrastructure before scaling models. In pharma, that means the seven months currently spent preparing data for use becomes weeks, and the AI programs built on that foundation produce outputs that clinical, regulatory, and commercial teams can actually trust and act on.

The compounding cost of waiting

The pharmaceutical companies investing deliberately in data governance strategy and tooling today are building the operational foundation for faster trials, cleaner submissions, more defensible real-world evidence, and AI programs that scale. Those that don't will keep paying the compounding cost of ungoverned data: Seven months at a time.

Learn more about how Collibra can help.

Sources: Komodo Health / Frost & Sullivan, "State of Data Mining in Life Sciences" (2024); BCG, "Where's the Value

1 https:// www.mckinsey.com/industries/life-sciences/our-insights/reimagining-life-science-enterprises-with-agentic-ai

6 https://www.collibra.com/blog/clinical-data-is-only-as-credible-as-its-context

7 https://www.collibra.com/blog/clinical-and-operational-data-is-as-trustworthy-as-the-processes-behind-it

8 https://www.collibra.com/blog/clinical-and-operational-data-is-as-trustworthy-as-the-processes-behind-it

9 https://www.prnewswire.com/news-releases/healthcare-state-of-data-report-2024-healthcare-organizations-report-an-average-124-roi-on-all-data-investments-according-to-hakkda-302094568.html