AI across life sciences demands proof

Two forces reshaping AI in life sciences

Leaders are expected to move faster with AI while operating against a higher bar of evidence and control.

1. Regulators are setting the bar. Regulators are raising expectations for validation, documentation and control. Anything that cannot be audited or reproduced introduces risk regardless of performance. AI is now a capability that must withstand scrutiny.
2. The pace of innovation is accelerating. At the same time, pressure to capture value continues to increase. The result is a persistent tension between speed and defensibility. Organizations are required to move quickly while ensuring outputs remain defensible under review.

In medtech, emerging digital surgery and connected-device ecosystems illustrate how that tension is being managed. These environments are increasingly designed as controlled platforms for AI-enabled applications, where interoperability, data standards and compliance are embedded into the architecture from the outset.

The rapid rollout of tools such as ChatGPT, Claude and Microsoft Copilot has accelerated AI adoption across the enterprise. Yet many organizations are discovering that broad deployment does not automatically translate into measurable business value. While these tools can generate meaningful productivity gains, they represent only one portion of the broader AI ecosystem. In life sciences, sustainable value is often created through AI-enabled transformation of research, clinical, manufacturing, commercial and enterprise processes rather than productivity improvements alone.

Much of the published guidance on AI assumes centralized ownership and mature governance structures. Yet audit and regulatory expectations remain high regardless of operating model, creating a persistent challenge: enterprise-level expectations applied to a non-enterprise foundation.

Where proof becomes visible

Finance is often where AI becomes visible first because value can be measured quickly. AI is helping accelerate close processes, automate reporting narratives, improve forecasting accuracy and surface exceptions earlier. These gains can reduce manual effort while improving consistency and decision support.

As adoption expands, however, finance leaders must ensure those improvements remain explainable to management, auditors and regulators. The greatest value comes not from faster outputs alone, but from outputs that can withstand scrutiny when decisions or financial statements are challenged.

Regulatory operations provide another example of how AI can create measurable value while raising new expectations around accountability. AI is deployed to assemble, draft, and validate clinical study report (CSR) content directly from structured study data and prior submission artifacts, using a controlled workflow aligned to regulatory submission processes.

This reduces the drafting cycle time from months to hours for initial CSR assembly (AI-generated first draft). There is significant reduction in rework cycles, as gaps and inconsistencies are identified earlier, and improved consistency across submissions, leveraging structured data and prior regulatory artifacts.

Two test cases where AI quietly breaks

Weak AI assurance rarely fails in obvious ways. Regulatory submissions may face delays, audits may expand and clinical programs may encounter setbacks when assumptions cannot be validated. In some cases, outputs appear correct but embed flaws that only surface during regulatory review, inspection findings or cross-functional validation.

These failures rarely appear in model accuracy scores, but emerge later through regulatory scrutiny, audit challenges, operational disruption and lost confidence in decision-making.

Gross-to-net

If AI governance is going to fail first anywhere in life sciences finance, it will fail in the gross-to-net (GTN) process. GTN concentrates the conditions that make AI difficult: financial materiality, judgment-heavy assumptions, fragmented data, and direct exposure to revenue recognition. GTN is also where finance teams are under the greatest pressure to modernize.

GTN is a natural starting point for proving AI value and a place where failures can remain hidden until they surface in a restatement or audit challenge. Often, outputs appear reasonable while obscuring how key judgments have been affected.

Several patterns tend to recur:

• Assumptions become antiquated, particularly in Medicaid and 340B, when the models regress toward historical norms and miss structural changes.
• Channel mix shifts are absorbed rather than surfaced, obscuring changes in revenue composition.
• Contract complexity is oversimplified by the model, introducing risk precisely where margin pressure is highest.

These issues often emerge later through audits and financial reviews, long after deployment.

Effective audit governance requires models to assess assumption drift and other macro-level errors. Without that visibility, outputs are not defensible regardless of back-test performance.

Clinical development

Clinical development presents a parallel challenge in operations, but with more subtle failure modes.

In clinical workflows, AI does not typically produce incorrect protocols or visibly flawed outputs. Instead, it can err by producing results that appear valid while embedding assumptions or omissions that only become visible later in data, regulatory feedback or inspection findings.

Patterns emerge here as well:

• Patient identification models can reinforce historical bias, systematically under-representing populations that were not well captured in training data.
• Site feasibility scoring can concentrate activity among previously high-performing sites, improving efficiency while reducing diversity and representativeness.
• Generative drafting can carry forward language from prior submissions even as the underlying science evolves.

As in finance, these issues rarely appear in model performance metrics. They surface later in regulatory questions, enrollment outcomes and cross-functional review conversations.

The ability to scale AI depends less on model sophistication than on the strength of the underlying data environment. Fragmented systems produce inconsistent outputs and conflicting views across reporting contexts. Even strong models struggle when the underlying data cannot support a reliable result.

Progress is typically fastest when organizations focus on a single high-value domain and bring it to a defensible standard. A complete foundation in one area creates more value than partial progress across many.

Building proof into practice

The primary challenge in scaling AI depends on how effectively organizations align data, processes, workforce capabilities and governance into a repeatable operating model. Organizations need a practical way to monitor assumptions, validate outputs, redesign workflows and identify when results no longer reflect business conditions.

Yet significant gaps remain. Grant Thornton’s 2026 AI Impact Survey found that 78% of leaders lack strong confidence they could pass an independent AI governance audit within 90 days.

Putting proof into practice does not require a multi-year transformation. Most organizations make progress by starting with a single high-value, high-scrutiny use case where business impact can be measured and sustained. Across life sciences, that often means beginning in areas such as clinical development, regulatory operations, patient safety or financial reporting.

Many organizations are also establishing AI leadership teams, functional champions and workforce upskilling programs to define where human judgment remains essential and where autonomous systems can create value. As AI adoption expands, success depends not only on governance and controls, but also on redesigning workflows, clarifying decision rights and preparing the workforce to operate effectively alongside AI.

Leaders looking to scale AI should start with four practical questions:

• What business outcome are we trying to improve (faster submissions, better trial execution, stronger forecasting or improved patient safety, for example)?
• How will success be measured in operational, clinical or financial terms?
• Which assumptions could affect clinical, regulatory or financial decisions if they go unchallenged?
• What information would we need to explain or defend the output during a regulatory review, audit or executive decision-making process?

Organizations that answer those questions early create a repeatable operating model for scaling AI across the enterprise. Those that do not often find themselves deploying AI faster than they can redesign processes, prepare the workforce or sustain outcomes over time.

AI is increasingly being used to ingest multi-source safety data, classify adverse events and draft case reports. Human review remains embedded within the process, with physicians validating causality assessments before submission. The result is faster identification of potential safety signals, reduced manual effort and more efficient case processing while maintaining appropriate clinical oversight.

A defining moment

Life sciences companies do not have an AI adoption problem. They have a proof problem.

AI is already delivering measurable gains across the life sciences value chain—from research and development through clinical, regulatory, manufacturing, supply chain and commercial operations, as well as enterprise functions such as finance and IT. Yet productivity alone will not determine the next phase of AI maturity. To scale AI across regulated environments, organizations must be able to explain where outputs came from, what assumptions were made and how results were validated.

The winners will not be those who deploy the most AI, but those who can translate it into measurable business outcomes and defend it under audit, regulatory and board scrutiny.