Agentic AI vs generative AI: what life sciences marketers need to know

Plus 3 use cases

Beth Cooper, JD / MBA,

VP of Marketing + Sales

We attended Microsoft’s AI Tour in NYC and learned about the latest cutting-edge advancements in AI. The one question I keep getting is: how is agentic AI different from generative AI? So let’s break that down, specifically for one of my favorite groups of people—fellow life sciences marketers. 

Artificial intelligence isn’t new to the life sciences. From early drug discovery to clinical trial design and real-world evidence analysis, AI tools are already transforming how healthcare moves forward. We know this. 

But as the market grows, marketers in biotech, pharma, and health tech need to understand the evolving AI capabilities behind the buzzwords, specifically for what we do.

Two terms in particular—agentic AI and generative AI—are reshaping how marketers think about automation, creativity, and efficiency. Understanding the difference can help you stay ahead of the curve and unlock practical use cases that deliver real ROI.

What’s the difference between agentic AI and generative AI?

The easiest way to understand the difference is this:

• Generative AI creates content or responses when prompted.
• Agentic AI takes action on its own, usually using multiple tools or steps to accomplish a goal.

Here’s a simple analogy. Think of generative AI as a digital chef that can whip up a recipe when you ask for one. Agentic AI is a digital sous-chef who sees you’re running late, plans the whole dinner, orders groceries, preps the ingredients, and texts your guests with an update. One answers. The other acts.

Comparison table: generative vs. agentic AI

How this impacts life sciences marketing

Marketers in life sciences face unique challenges:

• Regulatory compliance
• Complex buyer journeys
• Long sales cycles
• Cross-functional stakeholder engagement

Generative AI can help with creative production at scale, but agentic AI has the potential to truly transform marketing operations.

Use case 1: campaign content creation (generative AI)

You need ten versions of an email announcing a new clinical milestone for different HCP personas. Generative AI tools like ChatGPT can:

• Create drafts tailored to each segment
• Rewrite with different tones for MSLs vs. CMOs
• Suggest subject lines and preview text

This saves hours of manual writing while still allowing for regulatory review.

Use case 2: conference follow-up automation (agentic AI)

After attending ASCO, your team gathers 150+ leads. An agentic AI system can:

• Auto-assign leads to appropriate reps in your CRM
• Trigger follow-up emails personalized to the event
• Add notes from scanned badges into contact records
• Monitor replies and escalate to sales automatically

This is where generative AI alone isn’t enough. You need a system that acts.

Use case 3: pipeline prioritization (agentic AI)

Marketing leaders often juggle dozens of content initiatives. Agentic AI tools can:

• Analyze campaign performance by persona and channel
• Recommend shifting resources to high-performing assets
• Auto-prioritize tasks in project management tools
• Align with sales goals and MQL conversion data

Think of this as an always-on strategist helping your team focus on what works.

Why this matters now

As generative AI becomes more embedded in marketing workflows, it’s tempting to stop there. But agentic AI is where the real transformation happens—especially in a field like life sciences, where precision, timing, and personalization are everything.

Google’s Search Generative Experience (SGE), Microsoft Copilot, and enterprise tools like Salesforce Einstein are all exploring more agentic functionalities. These systems are moving beyond answering questions to making marketing decisions, executing tasks, and optimizing continuously.

At KNB Communications, we help life sciences clients stay ahead of the curve by implementing smart, scalable AI solutions that respect compliance requirements while driving visibility, efficiency, and engagement.

What to do next

Life sciences marketers don’t need to become AI developers. But understanding the difference between generative and agentic AI can unlock smarter strategy and better execution.

Start by asking:

• What tasks do we currently do manually that could be automated?
• Where are we still dependent on follow-up or handoffs?
• What tools do we already use that have AI capabilities we haven’t turned on?

Then, look for partners who can help you assess and implement the right AI models and integrations.

Because in a world where visibility in AI search is becoming just as important as Google, and where speed matters more than ever, agentic AI won’t just be a nice-to-have. It’ll be a competitive advantage.