AI maturity in Life Sciences doesn’t happen by accident. Across the industry, organizations are experimenting with generative AI, connecting it to multi-modal data and frontier LLMs, extending it to business teams and, in a smaller number of cases, embedding it into the systems that run the business. Four consistent maturity phases emerge from that progression, regardless of which platform an organization is building on.
Why Life Sciences Needs an AI Maturity Model, and Why Now
There is no shortage of AI enthusiasm in Life Sciences. The gap between proof of concept and a production-grade system that is adopted and operational is where many AI programs flounder, and closing it requires more than better prompting.
Komodo Health® created Marmot™, an AI platform specifically for Life Sciences, and this framework is built on structured feedback from 50+ organizations, including six of the top 10 biopharma companies. But the phases, the sequencing, and KPIs below are not specific to Marmot alone. They map how analytic maturity actually progresses in this industry and apply to any organization evaluating or building this kind of capability, regardless of vendor.
Each phase has a distinct payoff and its own KPI(s). That last part matters. Measuring a Phase 1 program on Phase 3 outcomes is one of the fastest ways to kill a promising AI program. None of these KPIs are ROI metrics. They measure maturity, operating discipline, and trust an organization has built at each stage, not dollars returned. ROI conversations belong later, once the foundation below them is real.
Phase 1: Experimentation. A team deploys a generative AI solution that lets users ask questions in plain language against a healthcare data foundation and receive (hopefully) trustworthy, traceable answers. Success is measured by speed to insight. In practice, this looks like a small group getting answers quickly against a limited dataset, with your analytics team still operating the tool.
The work in Phase 1 isn’t just technical validation. It’s building credibility. The smartest teams start with questions that are low-risk but recognizable, the kind a user can immediately validate against what they already know to be true. That’s what earns the room’s trust before you ask it to accept something unfamiliar.
Phase 2: Integration. Customer and other multi-modal data are integrated with the healthcare data foundation, enabling questions that no single source could answer alone. This is where governance gets established: access rules, a locked-down platform, often a center of excellence to own the relationship going forward. Recurring analyses begin to run automatically, shifting teams from rebuilding analyses to making decisions. Success is measured by decision throughput: the number of decisions made per period, not analyses produced. You’ll know you’re here when customer and other relevant data is integrated with the foundation, and recurring analyses now run on a schedule instead of being rebuilt.
Phase 3: Democratization. Answering analytic questions is no longer exclusive to the analytics team. Brand, field, medical, and market access teams can get their own answers through agents configured around the decisions they make and the way they work. The analyst comes out of the loop, and answers convert directly into action. This is also the phase where organizations get out of what we call “dashboard prison,” the experience of seeing an insight on a dashboard with no way to interrogate the question behind it. Success is measured in business actions started, stopped, or maintained. You’re in Phase 3 when a brand lead, field director, medical leader, or market access user gets to the answer without routing through an analyst and makes tangible changes as a result.
Phase 4: Transformation. AI stops being a destination users log into. Instead, intelligence and agents are embedded directly into the systems where work happens (like CRM, next best action, clinical trial management, revenue, finance, etc.) and managed like a product. The shift moves from AI supporting individual decisions to AI initiating and orchestrating governed actions continuously and at scale. Success is measured by the number of systems integrated and workflows created. This is when the answer or action appears within the system where the work already happens, without anyone opening an analytics tool at all.
The Sequencing Matters
The phases are sequential and each one establishes something the next requires. Skip Experimentation and you integrate on an unproven foundation. Skip Integration and self-service users are still constrained by fragmented data. Skip Democratization and you automate business processes before users have developed the trust and operating discipline required to support autonomous action. Organizations that rush ahead typically end up returning to an earlier phase later, at real cost to credibility and momentum.
Organizations also rarely occupy a single phase enterprise-wide: one function or use case may be transforming while another is still experimenting. Apply this model to a specific business capability, workflow, or domain rather than as a single, blanket score for the entire company.
Where Most Life Sciences Teams Actually Sit Today
Overwhelmingly, Phase 1.
That reflects where the technology itself stands. Generative AI in Life Sciences is roughly three years old as an enterprise consideration (agentic is even newer), and the phases take real work. Honest self-assessment matters more than aspirational placement.
What’s Ahead in This Series
Over the next four posts, we’ll take each phase in turn: what it demands, who leads it, what success looks like, and how to measure it without fooling yourself.
- Phase 1: What separates trustworthy experimentation from a demo that impresses a room and survives nothing.
- Phase 2: What changes when customer and other relevant data join the foundation, and why converging the ontology is the step that can’t be rushed.
- Phase 3: What it actually takes to enable governed self-service for business users, and why usage volume is not the same as value.
- Phase 4: The evolution from human in the loop to agents in the loop, and what governed action at full scale looks like in practice.
In the next post, we start where every team starts: Phase 1, Experimentation.
Frequently Asked Questions About the AI Maturity Model
What Is an AI Maturity Model?
An AI maturity model is a framework for gauging how far an organization has moved from testing AI to running it inside core business systems. In Life Sciences, it defines four phases (Experimentation, Integration, Democratization, and Transformation), each with its own owner, payoff, and KPI.
How Do You Measure AI Maturity at Each Phase?
Each phase has its own KPI: speed to insight (Phase 1), decision throughput (Phase 2), business actions started, stopped, or maintained (Phase 3), and systems integrated and workflows created, (Phase 4). These track maturity and trust, and ROI measurement comes once that foundation is in place.
Can Life Sciences Teams Skip a Phase of AI Maturity?
Each phase builds something that the next requires, so skipping one creates gaps. Teams that rush ahead typically return to an earlier phase later, at a cost to credibility and momentum.
