From Webinar Invite to AI Exploration

I was recently invited to speak at a webinar with Onomi on how to turn event engagement into real-world impact. Right away, I knew I wanted to do a live AI demo.

These demos are something I’ve been doing in webinars, at conferences and more often in my AI trainings. When people see real examples of what AI can do, it changes the way they think. They stop asking, “Can AI really help me?” and start asking, “How can AI help me think?”

To prep, Onomi handed me a sample of a congress dataset. Tabs for:

• Booth scans
• 1:1 meetings
• Symposium Q&A
• And more

A little bit of context. No summary. Just raw data.

This is Medical Affairs in a nutshell. It happens all the time: someone sends over an insight tracker or CRM export and says,

“Can you pull the insights and make a report?”

But there might not be no clear medical strategy, no guidance on what matters, or no shared definition of value.

You’re on your own with a spreadsheet.

This is the exact moment that inspired me to build this 3-step AI framework to make data matter.

→ Cut to the chase and just download the 3-step AI Framework to Make Data Matter Prompt Pack here. 

⚠️ Responsible AI Use in Medical Affairs

When using AI tools like ChatGPT, always follow your company’s compliance, privacy, and data governance policies. Do not input proprietary or confidential information into public LLMs.

Instead:

• Use simulated data to run pilots
• Work with internal teams to explore secure, compliant AI solutions
• Treat AI as a thinking partner, not a decision-maker

Your expertise is irreplaceable. AI helps you think faster, not skip the thinking.

The 3-Step AI Framework to Make Data Matter

This simple framework helped me go from overwhelmed to strategic in minutes, and it pairs perfectly with generative AI as a thinking partner.

Here’s how it works:

Step 1: Explore – What do we have?

This step gets skipped all the time.

I opened the congress data and immediately skimmed. The booth scan tab alone had 100+ HCPs. Each row had rep-entered comments like “asked about efficacy” or “interested in Pantrozumab.” The satellite symposia Q&A tab? 84 open-ended questions.

I got overwhelmed almost instantly. And that’s the point. Most people skim, feel that same overwhelm, and never really understand what they’re looking at. That’s where AI helped.

I used a variation of the “Explore the Data” prompt from my 3 Step AI Framework to Make Data Matter Prompt Pack, and in seconds it (this is what I live demoed on the Onomi webinar):

• Mapped out the types of HCP behavior across formats
• Surfaced recurring questions around patient selection and safety
• Pointed out contradictions, like passive booth visitors who were highly active elsewhere

AI didn’t give me answers, it helped me understand what I was looking at and get oriented fast. That’s the value of this AI framework: it helps you make the data usable instead of approaching it at a surface level.

Step 2: Interpret – What might this mean?

After the AI helped me understand what was in the data, the next step was getting to the hardest part:
► What does any of this mean?

This is the moment where most Medical Affairs professionals freeze. You can spot patterns. You can group themes. But actually figuring out the so what? That’s hard.

Because insight without meaning is just more data. And meaning isn’t always obvious. It needs to be crafted. That’s where the Interpret prompt came in.

Instead of just highlighting the top themes, I asked AI to help me:

• Start thinking about what those patterns imply
• Generate different angles to explore
• Suggest how the data might connect to HCP needs or strategy questions
• Spark ideas for how to tell the story in a clear and compelling way

This is where we move from listing data points to building a narrative that clearly demonstrates impact.

And if you’ve ever struggled to figure out how to share insights in a way that actually resonates, this is where the muscle of data storytelling becomes essential. I’ve written about this before and teach it in my Storytelling for Impact course, but here’s the big takeaway:

👉 A great insight doesn’t just say what happened. It says what matters and why.

AI helped me explore that transition. If you’re constantly stuck at the “so what” stage, this is the step that helps you break through.

Step 3: Decide – What’s worth acting on?

Once you’ve uncovered what’s there and started thinking about what it might mean, it’s easy to get lost in possibility.

That’s what makes this final step so important and where the medical strategy really comes into play.

Because AI will give you options. A lot of them.

→ You’ll see ways to segment your HCPs.
→ You’ll see follow-up pathways for each segment.
→ You’ll get ideas for content gaps, internal enablement, insight escalation.

And all of it sounds good. But you can’t act on everything.

That’s where the Decide prompt helps: it’s designed to bring you back to what matters most, based on your strategic priorities. You might use it to:

• Rank follow-up ideas by impact and feasibility
• Align recommendations to specific medical objectives
• Filter out the noise and focus on the next best move

What struck me was how easily the ideas could spiral out of control. AI had me exploring omnichannel follow-up journeys, content bundles, internal playbooks, and poof, 45 min was gone.

And I realized: this is where strategic clarity is essential. Without it, you chase ideas. With it, you make decisions that actually move things forward.