Practical AI in healthcare & life sciences: How defined use cases are delivering real returns

AI in healthcare and life sciences is moving past experimentation into a phase where ROI matters. In new research from Modus Create, over 100 healthcare and life sciences product leaders share how rising pressure to prove value is reshaping AI-driven projects. The findings show a clear shift toward practical, low-risk use cases that deliver measurable outcomes. Explore insights from 100+ leaders in our healthcare and life sciences AI report →

Healthcare and life sciences organizations have spent years experimenting with AI. Now leaders want to know what it delivered. The answer, increasingly, depends less on AI ambition and more on how clearly the business problem is defined.

ROI pressure is making low-risk use cases more popular

In our survey of over 100 healthcare and life sciences product leaders, 92% reported growing pressure to prove ROI. This evolution in expectations is hardly surprising. Whenever a new technology wave hits, proofs of concept and experiments dominate the early years. As the technology matures, executives start asking about outcomes. This shift is accelerating broader life sciences transformation across research, compliance, and product development teams.

“Boards and investors aren’t asking how many releases you did this quarter, they’re asking what it delivered.” — Sharon Lynch, Chief Executive Officer, Modus Create

In response, we are seeing organizations choose practical use cases they can measure over speculative bets. Interestingly, the most speculative use of AI, prototyping, sees the lowest adoption (22%), while research (52%) and monitoring (50%) lead the pack.

Research and performance monitoring are at the top because leaders prefer to start their AI journey in safer, more controlled functions, such as quality, security, and research, before extending it to more creative tasks like prototyping.

This article might interest you: 7 AI use cases driving real returns in life sciences

Governance is slowing most AI deployments

Governance remains the single greatest challenge in scaling AI. Last year, 79% of healthcare and life sciences organizations slowed their AI deployments due to unexpected regulatory or ethical concerns. Most have some form of governance framework in place, yet keeping up with evolving regulations and industry-specific best practices remains a challenge.

Part of what makes governance so difficult is that healthcare and life sciences operate across multiple overlapping regulatory domains: Health Insurance Portability and Accountability Act (HIPAA), FDA guidance on AI/ML-based Software as a Medical Device (SaMD), payer requirements, and increasingly, state-level AI legislation. A use case that clears one hurdle can still get stuck on another. And unlike other industries, the stakes of getting it wrong directly impact patient safety.

This is another reason why giving AI a clearly defined task tends to work better in healthcare and life sciences.

Consider these two examples:

• Example 1: You use AI to scan patient records and match them against trial criteria before sending them to researchers
• Example 2: You use an AI system that recommends treatments or generates full care plans across conditions for patients.

The first example is a clearly defined, rules-based task with a finite input set and a binary output: match or no match. There's a human downstream who makes the final call, keeping the AI in an assistive role and satisfying most regulatory expectations for clinical decision support. The second example carries far more legal and ethical exposure: treatment recommendations touch on liability, informed consent, and the standard of care. Every edge case becomes a potential failure mode.

That's not a reason to avoid these use cases forever. Still, it does mean they require a level of clinical validation, explainability infrastructure, and regulatory engagement that most organizations aren't yet equipped to sustain.

Ready to assess your AI readiness? Use our AI readiness assessment for regulated industries to identify gaps, assess risks, and prioritize the capabilities needed to scale AI responsibly.

How automation helped EVERSANA reduce costs by 35%

Modus Create has partnered with several healthcare and life sciences companies to apply AI within their workflows. One example is EVERSANA, a leading provider of commercial services to the life sciences industry, which uses AI to solve an often-overlooked problem.

The Medical, legal, and regulatory (MLR) process is a cornerstone of pharmaceutical content approvals. MLR reviews ensure that every claim, message, and promotional material meets strict legal and ethical standards. But the process is slow, tedious, and complicated by multiple stakeholders, complex regulations, and manual verification methods. This is precisely the kind of use case where AI can help.

EVERSANA developed a tool called EVERSANA ORCHESTRATE™ MLR that uses generative AI to automate medical claims processing and MLR approvals. It automates over 90% of routine MLR tasks, reduces submission errors by 86%, speeds up content updates by over 90%, and delivers approximately 35% cost savings for most teams. Because EVERSANA built the solution around a specific use case, it was easier to make it compliant and generate tangible ROI from it.

“EVERSANA ORCHESTRATE™ MLR is a game-changer for the life science industry. Through the power of AI and our collaborations with leading technology providers like AWS and in-house MLR experts, we’ve set a new standard for efficiency, compliance, and quality.” - Jim Lang, CEO, EVERSANA

AI outcomes come from clarity & control

Organizations that benefit from AI focus on problems that are already well understood, where inputs, outputs, and constraints are well defined.

This is why early adoption clusters around research, compliance, and monitoring. These areas offer clarity, repeatability, and a direct path to measurable impact. The advantage compounds when teams treat AI as part of the system rather than an isolated tool. When you structure data before it reaches the model and redesign workflows to absorb outputs, sporadic wins turn into repeatable outcomes.

Over time, this approach builds more than efficiency. It creates the governance, trust, and operational maturity required to extend AI into more complex workflows with confidence. In regulated industries like healthcare and life sciences, sustained performance depends on systems that are reliable, accountable, and aligned with how the business operates.

The organizations seeing the strongest AI returns aren't the ones with the most ambitious plans. They're the ones disciplined enough to solve one problem before moving on to the next.

Our research shows how product leaders are scaling AI while balancing ROI, governance, and operational efficiency. Explore the full healthcare and life sciences AI report →