Report 2026:
Defining an Agentic AI
Trust Benchmark
This research combined three methods to gather both depth and breadth of perspective on responsible AI in Life Sciences, focused on commercialization. Perspective was gathered from leaders at pharmaceutical, biotech, and medical device companies from commercial, medical affairs, patient services, and market access, as well as the IT and governance functions behind them.
At a glance:
On one side is a hype cycle near its peak. Every vendor's product is “agentic,” every deck promises autonomy, and the distance between the demo and the deployed reality has never been greater.
On the other is a real and rising caution: about job security, about systems that act without being watched, about putting anything near a patient or a regulator that can't explain itself. Leaders are being told to move fast and to be careful in the same breath, and the market has handed them no instrument that does both.
Chicago“Every industry right now is pausing and saying: what does this mean to us — this AI?”
Are you concerned about the use of AI at your organization?
How would you rate your level of comfort with AI? (1 = low, 10 = high)
N = 135
Personal comfort runs high. Yet more than half remain concerned about how AI is being used inside their own organization.
In this environment, agentic AI stops being a curiosity and becomes a practical lever for capacity, quality, and speed. This is why getting the discussions around future of work and the role that AI agents will play in the workplace have become a priority.
Princeton“Every single time we’ve had a breakthrough innovation, it gives human beings more agency and it gave us more ability.”
Are you currently involved in an AI project at your organization?
Do you believe there are clear industry standards for responsible AI in Life Sciences?
N = 135
Two in three leaders are already working on AI projects. More than half say there is no clear standard to build against.
What makes life sciences different is that the adoption question cannot be separated from trust, patient safety, compliance, and human judgment. A marketing-copy generator that hallucinates is an embarrassment; an agent that puts a wrong dose, an off-label claim, or an unmonitored message into a customer-facing workflow is a regulatory and patient-safety risk. This is the healthcare equivalent of confidently wrong information reaching someone who acts on it. The bar is simply higher here than in generic enterprise software.
Existing standards provide useful scaffolding, but they do not fully address the gap. ISO/IEC 42001 offers a certifiable management-system framework for governing AI across its lifecycle, and the EU AI Act classifies AI by risk tier and imposes real obligations on high-risk uses. But neither was written for the specific reality of Life Sciences customer-facing teams. For instance: a field sales rep answering a dosing question from an HCP, an MSL summarizing a new insight from a scientific exchange with an HCP, or a case manager supporting a patient program.
A bounded, industry-specific bar for what responsible agentic AI should look like in Life Science is missing and that absence is what all conversations kept circling.
“Agentic” is used loosely, often to describe very different kinds of AI. The clearest way to distinguish them is by autonomy: how much of the work the AI can carry out without human direction, and where control shifts from the user to the system. This distinction matters because labels can mislead.
A vendor may market a copilot as an “autonomous agent,” while an in-house tool may already be acting as an agent without being recognized—or governed—as one.
Types of AI Solutions
Across the roundtables, six themes emerged. The first three themes describe how responsible AI should be designed and deployed: around patients and human judgment, grounded in the right context, and introduced through bounded use cases with measurable value.
The remaining three themes describe what organizations need to scale it: governance that continues beyond initial approval, value measured against the current human baseline, and thoughtful workforce redesign.
The six themes
Theme 1
Before anyone debated architecture, the roundtables kept returning to a single anchor: in Life Sciences, “responsible” is not a generic ethics label borrowed from tech. It is defined by who you ultimately serve. Participants grounded responsibility in patient welfare, accuracy, and accountability, and drew a hard line that humans, not systems, remain the accountable layer. This is why the most credible early designs are those that are role-based support systems that improve preparation, retrieval, and synthesis while leaving consequential action in human hands.
The implication runs through the rest of the report: responsible AI is a design constraint, not a disclaimer. Every scaled use case should start with who is being served, what decision is being supported, what harm is unacceptable, and when the system must stop and escalate.
N = 135
Every attribute registered as relevant. Validated, ethical, and safe led by a wide margin: the vocabulary of an industry that answers to patients first.
London“We as doctors have a saying: primum non nocere — first, do no harm. My guiding principle would be, it’s got to be about patients. It’s got to help patients — and it’s got to be able to understand whether there’s potential for harming patients.”
Philadelphia“Humans have to be accountable. Regardless of what the agents help us do — accelerate, project-manage, whatever — at the end of the day, we are accountable for the answers we provide. AI agents don’t make this stuff up; they parse what comes out of humans. So unless you’re willing to be accountable for that, you can’t.”
Basel“Being human-centric is actually an accelerant, not a decelerant.”
Theme 2
Asked where AI should start, leaders were strikingly consistent and strikingly unglamorous. Not autonomous decision-making — bounded, repetitive, high-friction work where the value is obvious and the blast radius is small: review and approval cycles, pre-call preparation, insight logging, triage, and coaching. Where the emphasis falls depends on where the work sits. Upstream commercialization roles, such as content development, analytics, and planning, sit further from the customer and can lead with efficiency: speed and volume. Downstream, customer-facing roles lead with effectiveness, for three reasons: they are closest to the customer, each interaction calls for more judgment, and the organization needs consistency and control across thousands of those interactions. Either way, leaders wanted proof: measure the current state, then measure again. The appetite was for compounding, measurable wins with AI, not moonshots.
The pattern matters for sequencing. Start where value is high and governance complexity is low, then build toward harder use cases as AI trust and controls mature.
N = 135
Better outcomes edged out efficiency and productivity as the benefit leaders want most. Budgetary savings trailed well behind.
London“For every initiative, I try to understand the current state — where you are now versus where you were. How much time do you spend doing this? Track it. This used to take 22 seconds, now it’s four. I’m very deliberate, because that’s the only way you keep gaining adoption: when you show value. Not in money yet, but in the time that’s saved.”
Philadelphia“You should not be playing in the efficiency pool. You should be playing in the effectiveness pool.”
Toronto“[The tool] was untethered to the what-for — how’s it gonna help me, help my job? No organization can move as fast as the external environment is moving. It’s crawl, walk, run, fly.”
Theme 3
A recurring correction to the hype was that the differentiator wasn’t the size of the AI model, but the quality of the context it operated within. An agent that didn’t understand the role it supported — the data it should draw on, the customers it serves, and the boundaries it must work within — wouldn’t earn enough trust to be used, no matter how capable the underlying AI model. In a regulated industry like Life Sciences, context quality was also a governance issue. The right approved data sources, role boundaries, and escalation points were what made an agent safe, not just useful.
The design lesson was to build from the role outward, not from the model inward. The acceptable architecture for a field rep, an MSL, a key account manager, and a patient-support workflow would not be the same.
San Francisco“You’re only as intelligent as what you’re given. We were trying to use AI to understand doctors’ prescribing behaviors. Where it falls short is if the information isn’t getting fed. If doctors aren’t reporting what conferences they’re attending, the machine won’t know it.”
London“As an organization, I have a sales rep, a field reimbursement specialist, a medical science liaison, a medical information tech, and a nurse navigator [agent] I use to support customers across home, clinic, and institutional care. The magic is a complete understanding of the patient at the center, and who engages them.”
Princeton“For me, responsible means I’ve got to be absolutely sure I can give the right inputs and context for what I’m trying to discover, without that then being disclosed to the world. Without that context, it’s not going to come up with the best answers.”
N = 135
Data privacy and context integration tied for third. Technical readiness ranked near the bottom. The harder problem is feeding the system the right context, safely.
Theme 4
Governance was the most-cited concern when it comes to AI and, by the leaders’ own admission, the most neglected in practice. The live danger came up directly: people inside the organization are already building AI agents with no governance around them — shadow AI is growing faster than oversight. The fix leaders described is not a one-time IT sign-off but a standing operating model: named business owners, clear escalation paths, audit trails, the ability to deploy and revoke an AI agent, and SOPs that apply at both the AI system and the sub-agent level. Legal was repeatedly cited not as a blocker but as an essential partner.
The Princeton roundtable’s session with a 30-year Life Sciences lawyer sharpened the risk surface further. AI-recorded interactions are discoverable in litigation, adverse-event signals created trip-wire obligations, and disclaimers had to be enforced in the system. The throughline was clear. Governance is the difference between a system you can prove you control and one you merely hope is behaving.
Philadelphia“The standards are there; the AI ultimately has to do the same thing. Humans get things wrong sometimes too, but the decisions the AI is making have to be auditable. We can question them.”
Basel“It’s essential for each AI agent to know: when do I have to escalate to my human, and call him at the beach? We can’t permanently be out at the beach; we need to come back and visit the accountability department.”
San Francisco“MSLs themselves are building AI agents and starting to automate all that stuff, with no governance in place.”
Theme 5
The single loudest demand across the series was not for zero risk; it was for a fair comparison. Leaders pushed back hard on the idea that AI must be perfect; they wanted it to be measured against the current human baseline, which is itself non-zero given cognitive bias, fatigue, and memory lapses. The recurring request: give me a before-and-after on ROI, risk, quality, and adoption. And a sharp test for value was that an AI agent has to be measured against the old way of doing the work, not against perfection.
This is the demand the grading standard answers: a buyer or board needs an at-a-glance way to compare systems, and a function lead needs three numbers measured before and after an AI pilot. Comparison, made simple, is what unlocks funding.
Basel“It’s not that the risk doesn’t exist in a human-only world. If your sales reps are detailing, they’re very prone to cognitive error — but we’ve agreed how we quantify that risk. Give them a knowledge assessment, they score 80–90%; we’ve quantified it. That’s where transparency becomes very important.”
London“The underlying models are non-deterministic. It’s not a perfect environment; it’s prone to errors. The biggest risk I’ve seen is: how do you quantify that risk? That’s the real limiter to pervasive adoption.”
London“As we bring these thinking machines into organizations that shape the lives of our employees and patients — are we making right choices or weak choices? How do we know? What’s that yardstick?”
Theme 6
Roughly a third of every roundtable voiced that the hardest part AI adoption isn’t the technology; it’s the people. Leaders were candid that cultural resistance and workforce change, not infrastructure, is what stalls scaling. The reframe that resonated across the board is that a job description should be a blueprint for the future, not a relic of the past. The work is to separate what the agent handles (the rote, data-processing tasks) from what the human owns (judgment, connection, strategy, the ethical override) and to say so honestly, because people adopt agentic AI faster when they know exactly how it changes their job. When they don’t, they just work around it.
Handled well, the anxiety about AI converts into excitement. People get time back for the work only they can do. Handled poorly, the best technology in the world dies in the org chart.
Toronto“Take a person’s job description and put it in a centrifuge. What surfaces to the top? Data processing, rote tasks — this is what agents pick up. What settles at the bottom — human connection, judgment, strategy — is where humans lean in. That’s your Future of Work Blueprint.”
Boston“We all have to lean in and redefine the future of work for our teams… we have to intentionally spark these conversations internally.”
London“When I’m clear on what the role is, the job description is consistent with the expectation set for the human to do the job successfully. Agentic AI — agents, and ultimately super-agents — should complement that, and be held to the same standard as the individual.”
N = 135
Judgment led clearly, followed by human connection and strategy: the same three skills a redesigned role should protect. Asked about barriers to scale, leaders also placed cultural resistance and workforce change (14%) ahead of technical readiness (9%).
Across the six themes, a clear picture emerged of the kind of AI system leaders would trust in the Life Sciences industry. It keeps a human accountable and central. It creates value you can point to. It works within the rules, can be governed and watched once it leaves the pilot, and keeps the organization’s data and IP safe. Taken together, it can be trusted to protect the patient. This is the RAIL Score.
That gives us six components of trustworthy agentic AI, each tracing back to what RAIL roundtable and survey leaders raised:
Compliant
Capable
The RAIL Score tells you whether an AI system or tool is trustworthy. It doesn’t tell you where to point AI in the first place. And most teams have more ideas than they can sensibly take on at once. So the question becomes: of all the places you could use AI in commercialization, where do you apply it first?
The leaders we spoke to who were furthest along with AI answered the question the same way. They weighed each idea on two simple questions: how much value would it create, and how hard would it be to govern it safely? That sorts every use case into one of three groups.
High value, easy to govern.
The safe, obvious wins: review and approval cycles, pre-call prep, insight logging, triage, coaching. These are where most teams should begin.
High value, but harder to govern.
Worth doing once the controls are in place: connecting systems together, supporting a full product launch, anything that chains several agents together.
High risk.
Anything that could reach a patient or regulator without a human in between: autonomous external messaging, direct patient advice. Not never; just not first.
Whichever you pick first, decide up front how you’ll know it worked. It is difficult to build a business case just on projected savings, so choose a few simple measures, take a reading before the pilot, and take it again after. A real before-and-after on the actual work.
Every other section in this report is about the technology. This one is about the people who have to work alongside it. When we asked what stalls AI in practice, the answer was rarely infrastructure or budget. It was the workforce: the subliminal resistance of people who don’t know what the technology means for their own job.
The fix for this isn’t a reassuring All-Hands Meeting. People fear ambiguity about their place in the new setup, and specificity is what counters that. The moment a role is drawn clearly, showing which work the AI agent supports and augments, and what the person is now freed to own, most of that worry drains out of the room. So the work for leaders is to redraw the job, honestly and in detail.
Redrawing a role comes down to a single move: separate what the AI agent does from what the human keeps. The AI agent takes the rote, repeatable work, retrieval, formatting, first-draft assembly, logging. The human keeps what only a human can hold: judgment, human connection, strategy, and the final call when something doesn’t look right. Done well, this becomes an adoption accelerant, because people can see what they gain by having an AI agent “helper.”
In a rare-disease patient-support team, one specialist’s day was being eaten by retrieval, readying records and paperwork before each patient interaction.
Hand that retrieval to an agent and the role doesn’t shrink, it shifts. The agent does the pulling-in; the person gets the time back for the patient.
Boston“I can take someone who’s trained as an NP or social worker, and let them be all about the passion they have, and serve patients instead of pulling in the form. If I can keep the patient service person focused on the patient, and not on the screen, they can take on additional therapy and indication.”
The hardest part of any framework is the first move. This section turns everything from the previous sections into a single quarter of concrete action. Set up to build momentum, prove value early, and give a team something real to show before the next planning cycle.
Days 0–30
Inventory what’s already running with the AI Inventory tool.
Stand up a small, cross-functional RAIL Council: commercial, medical, IT, legal. Agree your red lines: the uses you won’t allow yet. And pick one AI tool or system to put through the RAIL Scorecard.
Days 30–60
Score that first system with the RAIL Self-Grading Scorecard and see where it lands: High, Medium, or Low. Then start one pilot, scoped with an AI Agent JD template, so it has an owner and clear boundaries from day one.
Lock the few before-and-after metrics by which you’ll judge it before it goes live; not after.
Days 60–90
Re-measure against the baseline you set. Run the Manager’s Team Conversation Script with the team whose work the agent touches. For specific individuals or teams, share the Job Redesign Worksheet so the people side moves with the technology. Then make the call the scorecard points to: scale it, fix the specific gaps, or stop.
Most agentic AI solutions enter a Life Sciences organization through procurement. A demo shows an AI tool or system at its best. The RAIL Score asks how it behaves on an ordinary day: with real data, real users, and a regulator who may one day ask for the record. Buyers carry the risk in the gap between the two.
The Vendor-Grading Checklist turns each of the six RAIL Score components into direct questions a buying team can put to any vendor, in an RFP, a demo, or a security review.
Report 2026:
Defining an Agentic AI
Trust Benchmark
