Two questions are worth sitting with before any organization commits budget or strategy to agentic AI.
Can AI move beyond recommendations to drive action autonomously? And if it can…should it?
The answer to the first is yes, and it’s happening faster than most commercial teams are prepared for. The answer to the second has more nuance to consider. Autonomy without the right architecture around it creates risks in pharma that simply don’t exist in other industries: regulatory exposure, compliance failure, and field teams that disengage from tools they do not trust.
The organizations building agentic AI programs well are not automatically the ones moving fastest. They’re the organizations who have thought carefully about where autonomy creates value and where human judgment needs to stay in the loop.
According to Deloitte, NBA is now a the top strategic focus across their entire life sciences client base. That doesn’t, however, mean most companies are doing it well. Most are not. The gap between a well-functioning NBA program and a struggling one rarely comes down to the technology alone. It comes down to whether teams understand what they are actually building, where it breaks, and how to get field reps to use it.
This post covers all three.
What Agentic AI actually means
Most AI in commercial pharma today is advisory. It analyzes data, surfaces a recommendation, and waits for a human to act. Agentic AI is different. An AI agent can plan a sequence of actions, execute them, monitor the outcome, AND adjust. It doesn’t wait to be told what to do next.
In a commercial context, that means an agent can do things like: identify an HCP who has not been contacted in six weeks, generate a compliant outreach email based on their therapeutic area and recent engagement history, send it through the right channel at the right time, log the interaction in the CRM, and flag the outcome for a rep’s next call. All without a human initiating each step.
That is a meaningful shift from recommendation to execution. And it’sits where the governance questions become important.
A perspective from the field: humans in-the-loop, not on-the-loop
Derek Choy, Head of ProductChief Product Officer at PharmaForceIQ, has spent a decade building AI systems for life sciences commercial teams. His view on agentic AI is shaped by what he saw when the industry first moved to AI-driven recommendations.
When we first introduced AI-driven recommendations, the technology was not the barrier. The barrier was trust. Field teams resisted because recommendations arrived without context, and asking someone to act on a black-box instruction is not a sustainable model.
That experience informed a principle Derek has carried into the agentic AI era: humans must be in-the-loop, not on-the-loop.
The distinction matters. Being on-the-loop means a human reviews everything before it happens, which recreates the bottleneck that automation is supposed to remove. Being in-the-loop means humans set the guardrails, approve the strategy, monitor the outcomes, and intervene when the system flags an exception. The agent handles execution within those boundaries.
This is the architecture that makes agentic AI viable in pharma. Compliance rules, brand tradeoffs, resource allocation constraints, and HCP relationship considerations are encoded into the system at the strategy layer. The agent operates within those parameters. Humans stay accountable for outcomes without being the bottleneck for every action.
The recent ecosystem developments, Veeva AI, Salesforce Agentforce with MCP support, Life Sciences Cloud partnerships, and headless AI tools are making this architecture easier to build. The infrastructure is catching up with the vision. The question is whether commercial teams are ready to operate it well.
What this looks like across a commercial team
Abstract descriptions of agentic AI tend to obscure what it actually changes for the people using it. Here is what the shift looks like at each level of a commercial organization.
Brand Manager
Instead of reviewing a spreadsheet of recommended tactics and manually selecting which to approve, a Brand Manager works with an AI that has already proposed a set of actions based on the current knowledge base, brand strategy, and HCP engagement data. Each suggestion comes with a rationale. The Brand Manager’s job is to set the parameters and approve the approach, not to build the plan from scratch. That marketer will also be able to see the campaign evolve as the system learns continuously and adapts in real-time, instead of needing to go through onerous analytics review and manual order change cycles.
Commercial Operations
A Commercial Ops lead can see in real time that AI-generated recommendations are staying within resource allocation limits, brand tradeoff rules, and compliance requirements. The audit trail is automatic. When something falls outside the guardrails, the system flags it rather than proceeding. The oversight is built in, not bolted on after the fact.
Sales Representative
A rep receives HCP outreach suggestions that include pre-approved compliant content, tailored to the specific HCP’s profile and recent engagement history. That includes alerts at critical moments, like a relevant mutation test result, that reveal an HCP has an eligible patient – not four months later when claims data comes in. The rep can adjust and customize the message before sending, maintaining the personal relationship that no AI can fully replicate. The agent handles the logistics. The rep handles the relationship.
Medical Science Liaison
An MSL preparing for an HCP conversation gets AI-curated clinical insights relevant to that specific HCP’s therapeutic interests and recent publication activity. Less time spent searching for the right data. More time spent on the conversation itself.
The HCP
An HCP receives information that is relevant to their practice, delivered through their preferred channel, at a time that is not disruptive. Whether that comes via a rep, a digital channel, or a virtual agent, the experience is more consistent and less repetitive than the current default.
The practical path to agentic AI in pharma
The organizations making the most progress are identifying the workflows where agent-driven execution removes the most friction, building the governance architecture first, and expanding from there. They’re not trying to automate everything everywhere all at once.
Three things tend to be true of the programs that work:
- Governance is not an afterthought. The compliance and regulatory rules are encoded at the strategy layer before any agent acts.Â
- The human role is clearly defined. Reps and commercial teams know what the agent does, what they are accountable for, and when to intervene. Ambiguity here kills adoption.
- Transparency is non-negotiable. Every action an agent takes is traceable. If a field team cannot see why the system did what it did, trust breaks down fast.
The competitive advantage in agentic AI will not go to the organizations that move fastest. It will go to the ones that build durable programs, with the right architecture, the right governance, and commercial teams that know how to work with agents rather than around them.
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Where PharmaForceIQ stands
PharmaForceIQ’s optichannel platform, now incorporating additional the AI capabilities built by Aktana over a decade of life sciences deployments, is built on the principle Derek Choy has held since the beginning: AI that empowers human judgment rather than replacing it. The platform is designed so that agentic capabilities operate within an optichannel model, deploying resources on the channels that matter most for each HCP, with the compliance and transparency architecture that makes that autonomy sustainable in a regulated industry.
Agentic AI in life sciences is a current decision, not a future consideration.The question is not whether to engage with it but how to do it in a way that holds up under regulatory scrutiny, builds field team trust, and delivers outcomes that improve over time.
Go deeper.
The PharmaForceIQ Agentic AI eBook covers 20+ questions life sciences leaders are asking right now: what governance models work, how to build the compliance architecture, what the field adoption playbook looks like, and how to evaluate whether your AI vendor can support an agentic program. Download it here.