Pharma teams are under pressure. You need to move faster, keep messages accurate, and prove impact, all while staying inside strict rules. Traditional AI can help, but it often stops at suggestions.

Agentic AI goes further. It does not just analyze. It can actually take action inside your workflows, like a digital teammate. For life sciences, that is a big shift.

In this blog, we will break down what agentic AI is, how Salesforce’s Agentforce is being used in real healthcare settings, and what that means for pharma marketers like you.

What Is Agentic AI for Pharma?

Most people know generative AI as a tool that helps you write copy or summarize data.

Agentic AI is different:

• It can watch what is happening in your systems
• It can decide what to do next based on rules and data
• It can act on those decisions inside your CRM or patient platforms

Salesforce describes Agentforce for Healthcare as an agentic AI layer where agents can be triggered by changes in data, operate in the background of business processes, and interact with users across different interfaces.

For pharma and healthcare, that means agents can help with things like:

• Benefits checks and patient support program enrollment
• Daily patient or HCP outreach
• Drafting and logging call notes for field reps
• Summarizing medical records or inquiries for review

Done right, agentic AI becomes digital labor that works beside your team, not a black box that replaces them.

Case Study: Transcend and Precina with Agentforce

Salesforce has shared several examples of healthcare organizations using Agentforce to improve speed, care quality, and cost. A good overview is captured in their article on how agentic AI will ease healthcare’s workforce crisis.

Transcend: Faster responses for patients

Transcend is a mental health and addiction treatment provider that runs intensive telehealth programs. As demand grew, their staff could not keep up with all of the questions, intake checks, and follow up messages.

Transcend brought in Agentforce to help:

• AI agents review client data across Health Cloud and Data Cloud
• Agents can check eligibility for services and suggest tailored treatment options
• Agents assist with benefits checks and routine questions, so humans can focus on complex cases

According to Salesforce’s workforce crisis analysis, Transcend expects Agentforce to improve response times by about 30 percent and help them support more clients, faster, primarily by cutting paperwork and manual tasks by up to 30 percent across their teams. That data is backed by Salesforce’s healthcare AI agent research on administrative burden and AI agents.

In simple terms: patients get answers sooner, and staff are less burned out.

Precina: Scaling diabetes support without more headcount

Precina is a diabetes care provider that supports patients with daily digital coaching and clinical oversight. To scale their model nationwide, they needed to keep costs under control while still giving each patient regular attention.

Salesforce’s customer story on Precina and its news article on how Precina scales operations with Agentforce explain what they did:

• AI agents handle routine outreach and check ins
• Agents help paraprofessional staff manage large patient panels, surfacing who needs help today
• Providers can focus on high risk or complex cases, not manual monitoring

Salesforce reports that Precina expects to save about 80,000 dollars per year for every 5,000 patients in reduced administrative overhead, and to reduce training costs per clinical provider, by using Agentforce. A separate feature on how AI agents scale personalized diabetes care notes faster improvements in A1C levels and major gains in staff productivity.

These are healthcare examples, not branded drug campaigns. But the pattern is exactly what pharma marketers want:

• Better use of data
• Faster, more relevant engagement
• Lower operational cost

Why This Matters for Pharma Marketing

So what can pharma commercial and marketing teams learn from these agentic AI stories?

1. Agents turn insight into action

A common problem in pharma is this: you have data and dashboards, but acting on them still takes manual work. A marketer might see that a segment is under engaged, but someone still needs to build the list, choose content, and set up sends.

Agentic AI can close that gap.

For example, in a pharma context, an agent could:

• Watch for HCPs who view a new clinical video but do not follow up
• Automatically schedule a compliant follow up email using approved content
• Summarize response patterns for the brand team each week

Salesforce’s life sciences AI survey insights found that commercial leaders estimate 30 percent of their sales and marketing efforts are wasted on the wrong targets or the wrong message. Agents that can act on better segmentation in real time are a direct answer to that problem.

If you want help mapping agentic use cases to your own brand or franchise work, CI Life can partner with your team to design compliant workflows and guardrails, not just tools.

Talk with CI Life about agentic AI use cases for your pharma brand.

2. Digital labor frees humans to do human work

In both Transcend and Precina, the point was not to replace clinicians. It was to give them back time:

• Less manual paperwork
• Less repetitive outreach
• Less time hunting through records

The same logic applies to your field reps, marketers, and medical teams. Many teams still spend hours each week on:

• Building slide decks and emails by hand
• Logging calls and engagements into CRM
• Pulling basic analytics for recurring reports

Salesforce’s research on AI agents in healthcare shows that agentic AI can cut administrative work by up to 30 percent, letting staff reclaim time that can be spent on higher value activities. You can see this described in their AI agents cut paperwork study.

That means more time for:

• Deeper HCP conversations
• Better strategic planning for launches and campaigns
• Stronger collaboration with medical and market access

The value story is simple: less time on tasks that software can do, more time on work only your people can do.

How To Bring Agentic AI Into Pharma Marketing Safely

Of course, pharma does not have the same risk profile as a consumer brand. You need to think about compliance from day one.

Here are practical steps we recommend when clients ask CI Life about agentic AI:

1. Start with one focused workflow
   Pick a narrow process where:
   • Rules are clear
   • Content is already approved
   • The manual work is painful and repetitive
2. For example: benefits verification, event follow up, or basic CRM call logging.
3. Connect to trusted data and content
   Make sure your agent can only pull from:
   • MLR approved content blocks
   • Clean HCP and account data
   • Up to date labeling and safety information
4. Platforms like Salesforce Life Sciences Cloud and Agentforce for Life Sciences are already built to unite CRM, Health Cloud, Data Cloud, and agentic AI on one stack.
5. Build guardrails with medical, legal, and regulatory
   Bring MLR in early. Together, define:
   • What the agent is allowed to say
   • What must always be routed for human review
   • What must be logged for audit trails
6. Test with a pilot, then scale
   Measure:
   • Time saved per user
   • Change in engagement rate
   • Any compliance concerns raised
7. Use these results to refine the agent and to build the case for expanding into other workflows.

If you want a partner that understands both AI systems and pharma compliance, CI Life was built for that exact intersection.

Reach out to CI Life to design an agentic AI roadmap for your life sciences team.

Where To Go Next

Agentic AI is not science fiction anymore. Providers like Transcend and Precina are already using it to answer patients faster, scale support programs, and reduce administrative cost. Those stories are highlighted in Salesforce’s workforce crisis overview and the Precina customer story.

For pharma marketing, the same pattern can unlock:

• Smarter targeting and follow up
• Faster, cleaner execution across channels
• More time for strategy and relationships

If you are already exploring AI, a good next step is to learn why models that are trained on your own content can still make things up, and what guardrails you need around any agent.

Read our related CI Life blog, “AI Hallucinations Are Driving More MLR Rejections” to go deeper on that risk and how to control it.