The path to the agentic enterprise: preparing for Agentforce Life Sciences

Dreamforce 2025 made one thing certain: the agentic enterprise is here. Its entry point has a concrete shape and name. That name is Agentforce Life Sciences and its early production rollouts in big pharma are planned for Q3–Q4 2026.

The implication is immediate: watching already means falling behind. If you wait until 2026 to think about data quality, workflow design, or governance, you’ll be retrofitting foundations under systems that are already in motion.

Up to now, most AI in pharma has lived in pilots and side projects: a chatbot for FAQs, a summarizer for MLR packs, a few “PoCs” around field force productivity. Useful, but peripheral.

Agentforce changes that. When autonomous, goal-driven agents sit on top of unified data, regulated content, and Salesforce workflows, they stop being “tools you try” and start becoming part of how commercialization, medical, and patient services actually run.

This article is about what to do now. If you assume Agentforce will be in your stack, the real work in 2025–2026 is to get the enterprise ready for it:

• Clean and unify data,
• Redesign engagement workflows for AI orchestration,
• Put AI and content governance in place,
• Upskill people and adjust operating models,
• And run a realistic readiness/complexity assessment before any move to Agentforce Life Sciences.

In other words: make sure that when autonomous agents arrive, your organization is actually prepared to let them do valuable work.

Make your data agent-ready

Many life sciences companies today are operating with a patchwork of systems that looks roughly like this:

• multiple CRMs (often by region, BU, or brand),
• different data models and ID schemes for the same HCP or account,
• medical, commercial, and patient teams each running “their” view of reality,
• a long tail of spreadsheets and offline workflows sitting completely outside governance.

That’s fine if your CRM is just a system of record. But it will be a disaster if you want yeild value from Agentic AI to:

• prepare context before a rep or MSL meeting,
• route inquiries or cases based on real history,
• detect adherence or safety risks across channels,
• explain why they recommended a specific action to an auditor.

So the job right now is to move your data into a shape where agents can actually be trusted. Concretely, that means:

• Pick your system of truth. If Agentforce Life Sciences is where you’re heading: design around Salesforce’s data model and Data Cloud, not around whatever your 2014 CRM implementation looked like.
• Rationalize entities and IDs. One HCP, one account, one site – with clear relationships to trials, interactions, content, and consent. If an agent can’t reliably tell who is who, it’s not safe to let it act.
• Decide what “good enough” data looks like for automation. Agents don’t need perfection, but they do need minimum viable completeness and quality for the scenarios you care about (e.g., next-best action, medical inquiry routing, site selection). Make that explicit.
• Start moving unstructured assets into governed stores. Medical letters, MLR’d content, FAQs, study synopses – if this material stays in email threads and shared drives, it’s invisible to agents.

Salesforce has already done the platform homework: Agentforce Life Sciences + Data Cloud give you the place where clean, governed, cross-functional data can live. But the responsibility for getting your data into that state is yours.

If you wait until Q3–Q4 2026, when early adopters are already running agents in production, and only then start cleaning up your data estate, you’re not an “observer” – you’re a follower with a two- to three-year lag.

Redesign engagement so agents are valuable for your organizational context

Most life sciences workflows today are built for humans and hindsight. Reps, MSLs, and patient teams copy-paste between systems, log what happened after the fact, and manually push work from one team to another. It works because people compensate for gaps in process and technology. An agent, however, can’t “work around” ambiguity – it either has a clear role in the flow or it’s dead weight.

Agentforce Life Sciences assumes the opposite model: engagement journeys are explicit, system-driven, and observable. An AI agent isn’t there to decorate an old CRM; it’s there to sit inside the flow of work – suggesting what to do next, drafting responses, routing items, and flagging risks. For that to be safe and useful, you need to know, in concrete terms, what “ready for outreach” means, which data sources are allowed, when a suggestion is appropriate, and when silence is safer. If those boundaries live only in people’s heads or buried in an SOP library, there is nowhere sensible to plug an autonomous assistant.

This is why “workflow prep” is core to becoming an agentic enterprise.

Take a typical brand-team scenario. Today, a rep might get a static call list from one system, check consent in another, open a PDF for talking points, and then dump notes into CRM at the end of the day. That is impossible to orchestrate intelligently.

In an Agentforce-ready world, the same journey is modeled as a sequence the system can see: target selection criteria, consent check, pre-call insight generation, in-call guardrails, post-call documentation, and next-best action. Once that spine exists, agents can safely automate slices of it: generating the briefing, drafting the follow-up, nudging on overdue tasks – all inside defined guardrails.

The same logic applies in medical and clinical. If medical inquiry handling is essentially “email a mailbox and hope someone picks it up,” there’s very little an agent can do beyond triage. If, instead, inquiries flow through a structured queue with standard data fields, response templates, and escalation criteria, an autonomous assistant can summarize the question, propose a draft based on approved content, route it to the right owner, and log the final response.

In trials, if feasibility checks, eConsent, and follow-up are scattered across tools and ad-hoc spreadsheets, you can’t realistically ask an agent to “monitor adherence and trigger interventions” – there’s no coherent process to attach to.

So the preparation work for 2025–2026 is less about “automate everything” and more about making work legible. Pick a handful of high-value journeys – HCP engagement for a priority brand, medical information handling, a flagship study – and turn them from improvisation into explicit, system-level workflows. Clarify the decision points, inputs, approvals, and “no-go” zones, and design them so humans can easily accept, reject, or correct agent suggestions.

By the time early adopters go live with Agentforce production rollouts in 2026, the organizations that have done this redesign will be able to switch on meaningful, low-risk automations almost immediately. Everyone else will still be discovering, uncomfortably, that many of their “processes” only ever existed in people’s heads and inboxes.

Build AI governance before the agents show up

Agentforce Life Sciences runs inside the Einstein Trust Layer, with controls like zero data retention, toxicity detection, and grounding in approved sources baked in. That gives you a technical safety net. But it doesn’t answer the hard questions:

• Which decisions are we comfortable letting an agent propose – and which must stay human-only?
• What data is “in-bounds” for training, grounding, and context – and what is off-limits, even if technically accessible?
• How do we prove, to ourselves and to regulators, that AI-supported decisions remain explainable and auditable?

Those answers have to come from you.

In practical terms, “AI governance” at this stage is less about 40-page policies and more about drawing the first clear lines in the sand. Pick a few near-term Agentforce use cases you know are coming – pre-call briefings, medical inquiry drafting, trial document summarization – and define, explicitly:

• what the agent may do on its own (e.g., draft, summarize, route),
• where human approval is mandatory (e.g., sending any external communication, changing key master data), and
• what must be logged for audit (inputs, model version, final human decision).

Do that work now, while agents are still in pilot mode, and you have a template you can extend across brands, regions, and functions as adoption grows. Wait until Agentforce is live in production and you’ll be trying to retrofit rules around workflows the field has already embraced. That’s when governance turns from an enabler into a brake.

Governance can’t sit with IT or compliance alone. Agentic workflows touch commercial, medical, clinical operations, pharmacovigilance, legal, and privacy. So, if those groups don’t share a forum and a common language for discussing use cases, risk levels, and guardrails, you get the worst possible outcome: teams quietly running their own AI experiments at the edges, while the center responds with blanket restrictions. No coordination, no trust, and no scalable path forward.

The companies that arrive in 2026 “agent-ready” will have a set of agreed principles about what agents are for, where they’re allowed to act, and how humans stay in charge.

Modernize regulated content and MLR so agents can actually use it

If data is one half of the agentic story, content is the other.

Right now, a lot of medical and promotional assets in life sciences sit in shared drives, legacy DAMs, or home-grown MLR tools. They were designed for humans to search, download, and copy-paste from — not for AI agents to reason over, assemble, and reuse safely.

Salesforce’s move with Regulated Content Management (RCM) is a big tell. They’re building the place where claims, references, and approvals live in a structured way, so Agentforce can work with them without breaking the rules. If you want agents to draft emails, slide decks, or responses that would survive an audit, the underlying content needs to be:

• broken into reusable, atomic chunks (claims, references, key messages),
• tagged with indications, markets, channels, and lifecycle status, and
• tied explicitly to their MLR decisions and source documents.

Most current MLR processes weren’t built for that. They’re built to move PDFs through a committee. Agents can’t do much with “MLR_Approved_FINAL2.pdf”.

So the work now is twofold. First, clean up and standardize how you store and describe content: introduce consistent taxonomies, normalize how you capture approvals, and make sure every asset has a clear “where/when/for whom” profile attached. Second, start redesigning MLR workflows for a world where AI is in the loop:

• Agents pre-assemble drafts using only approved components;
• reviewers focus on edge cases, nuance, and risk rather than basic compliance checks;
• every AI-assisted output is traceable back to specific claims and references.

Done right, you will have a clean, well-lit library of content to work with — not a maze of documents agents have to guess their way through.

Get people ready to work with agents

Agentic enterprise is not just a tech story.

If reps, MSLs, or trial teams see AI as a black box that second-guesses them, they’ll ignore it. If they understand what Agentic AI is good at – pulling context, generating drafts, flagging risk – and where human judgment still leads, they’ll treat it as a teammate, not a threat.

Now is the time to:

• Expose teams to realistic agent use cases (pre-call briefings, literature summaries, eConsent support).
• Teach basic “prompt hygiene” and how to validate AI outputs against approved data and content.
• Make it explicit that accountability stays with humans, even as agents take over more of the mechanical work.

The goal isn’t to turn everyone into an AI engineer. It’s to make “I worked with an agent on this” a normal sentence inside the company.

Align with the Salesforce ecosystem and know your own complexity

Agentforce Life Sciences will not arrive in a vacuum. It’s coming into landscapes full of legacy CRMs, regional variants, custom integrations, and old compliance workarounds.

Two practical moves for 2025–2026:

1. Align with Salesforce and its partners
   If Agentforce is your future operating platform, you want to be close to the roadmap, reference architectures, and early patterns. That means talking not only to Salesforce, but to partners who sit at the intersection of life sciences, CRM, and AI.
2. Get a clear view of your own estate
   Before you plan big moves, you need to know what you’re actually running: how many orgs, which modules, what custom logic, where the data really lives.

That’s exactly why we built Avenga’s Migration Readiness Assessment accelerator: to automatically surface configured modules, object relationships, metadata sprawl, and integration points, so you know what you’re dealing with before you commit to timelines and budgets.

You can’t become “agentic” without knowing your starting point and designing a path that won’t collapse under its own complexity.

Culture: decide now if you want AI at the edge

Finally, there’s the uncomfortable question: do you actually want agents at the point of work, or just in strategy decks?

Agentic models only create value if they’re allowed close to real decisions — helping shape HCP conversations, guiding trial operations, supporting patient interactions. That requires a culture that:

• Is willing to expose real processes and data to AI.
• Accepts that “autonomous” doesn’t mean “unsupervised,” but does mean “not every step is re-approved by hand.”
• Treats early pilots as learning tools, not proofs that AI is perfect or useless.

By Q3–Q4 2026, early adopters will be running Agentforce Life Sciences in production. At that point, the gap will be visible: some companies will have agents operating in the field; others will still be “exploring AI” in workshops.

If Agentforce Life Sciences is in your future stack, you need a clear, realistic view of your starting point. Avenga’s Migration Readiness Assessment – powered by our proprietary technical accelerator – gives you that visibility. It automatically surfaces custom modules, metadata sprawl, integration complexity, and hidden dependencies so you can plan a safe, de-risked path into Salesforce’s AI-native future.

If you want to understand what it will take to move to Agentforce Life Sciences – without guesswork – get in touch.