Bob Bress, Head of Data & AI at FIRST, joined Julius Solaris, founder of Boldpush, for The AI-Powered Event Strategist to explore what putting AI to work in events looks like in practice.

Their conversation covered data, measurement, AI agents, creative workflows, personalization, and how event teams are beginning to work in new ways.

One main idea kept coming up:

Event teams know more than they’re able to use.

Each event produces decisions, audience data, feedback, meeting notes, budgets, content results, and lessons learned. But when the next event begins, much of that knowledge often ends up stored away, waiting to be rediscovered.

AI offers a chance to change this. Instead of adding another tool where information gets lost, it can help event teams create systems that remember, learn, and get better over time. 

We are already seeing this change in practice. In a recent client project, a FIRST team identified 39 AI use cases, which saved over 60 hours of work in a short period. These savings came from many small improvements across the event workflow, rather than one big breakthrough.

Here are four key ideas from the discussion that you can use for your next event:

An event shouldn’t disappear when it ends

Most teams create some form of post-event report. The issue is that these insights often don’t go very far.

Bob discussed the chance to combine information that usually stays in separate places, like registration data, CRM records, surveys, budgets, and production documents, and use it to guide future decisions.

“Use the data that comes out of an event for predictive purposes — to understand the impact of past events and improve future ones.”
— Bob Bress, Head of Data & AI, FIRST 

That changes the role of post-event data. Instead of just showing what happened, it can help answer what should happen next.

Which audience groups responded differently? Where did engagement drop? What patterns are starting to repeat across a portfolio? Which decisions should we make differently next time?

By doing this, we start to build event intelligence that gets more useful with each experience.

Your event needs a memory

A practical idea from the webinar was to treat each event as its own AI project, which means bringing together the brief, research, meeting notes, plans, past decisions, and relevant data so the AI can better understand the full context of the event. 

This opens up a bigger opportunity: giving each event a “second brain.”

For example, meeting transcripts are usually just stored away. Most people don’t have time to search through weeks of conversations for a decision they suddenly need, especially one they didn’t expect to need again. But if those conversations are part of an event’s working context, they become much more helpful.

It changes your relationship with the information.

The real value isn’t always in having something new. Sometimes, it’s about being able to use what you already have.

The best AI use cases might be unexpected ones

Some of Bob’s most useful examples weren’t especially futuristic, but grounded in the daily realities of running events.

Contract review. Capacity planning. Menu planning. Brand alignment. Drafting recurring documents.

That financial services example is a useful reminder that the impact didn’t come from one major AI breakthrough. It came from identifying dozens of smaller opportunities across the event workflow and improving them one by one.

At FIRST, we’ve already started using this approach with tools like CORA for automated contract review, AI-supported capacity planning, and other focused workflows.

The opportunity may be hiding in the small tasks your team repeats every week – the ones that don’t look transformative on their own, but when taken together can meaningfully change how a team works.

If you don’t know what you’re improving, AI can’t tell you whether it worked

Bob made another point that seems simple but gets more important as AI makes testing easier: define the outcome first.

An event meant to generate leads shouldn’t be measured the same way as one focused on building relationships, educating an audience, or changing brand perception. The same goes for AI.

Before automating anything, ask yourself: What are we really trying to improve?

Time? Consistency? Decision quality? Audience experience? Cost? Revenue?

Without that baseline, teams might build impressive solutions without knowing if they actually improved anything. As building, testing, and automating gets easier, knowing what not to build can be just as important as knowing what to build.

The Bigger Shift

Bob and Julius’ conversation went beyond what individual AI tools can do and focused instead on how AI can support better decisions, stronger event delivery, and more valuable outcomes for clients. 

The bigger question is what happens when an organization starts learning as a whole system. 

Imagine if decisions didn’t get lost in old meeting notes, if data from one event helped with the next, if recurring work could be reused instead of rebuilt, and if teams spent less time relearning what they already know.

Each event can build on the intelligence of the ones before it, so learning compounds instead of resetting. Your next event doesn’t have to start from scratch.