By Mary Tucker | Senior Communications and Content Manager | IAEE
The global exhibitions and events industry is no stranger to disruption, but artificial intelligence is taking things to new levels – not just as a new tool, but a new way of seeing the market. From decoding competitive dynamics to identifying emerging audience segments, AI offers exhibition organizers a level of strategic clarity that was difficult to achieve even just a few years ago.
In IAEE’s webinar, How AI Insights are Reshaping Exhibition Strategy: Understanding Markets, Segments, and Competitors, industry expert Matthias “Tesi” Baur will explore exactly how this shift is playing out. Tesi serves as the CEO of MBB Consulting Group and has more than 24 years of experience across some of the industry’s most influential organizations, including Messe Frankfurt, Reed Exhibitions and UBM/Informa. He brings both the strategic depth and the hands-on perspective to cut through the AI hype and focus on what actually moves the needle for exhibition professionals.
The session is designed to be immediately actionable. Attendees will learn to apply AI insights to real strategic and commercial decisions as a practical shift in how they approach planning and performance evaluation. Tesi will also address the honest reality of working with AI tools and agents, including the friction points that don’t always make it into the sales pitch.
Perhaps most importantly, the session reframes how exhibition professionals should think about AI altogether: not as a system that replaces human judgment, but as a decision-support tool that makes that judgment sharper and better informed.
Here, we sit down with Tesi to explore the concepts he’ll be covering. Whether you’re trying to make sense of shifting market conditions or looking for smarter ways to benchmark performance, this conversation is a preview worth reading before taking a deeper dive at the webinar.
AI promises a lot, but exhibition organizers are busy people with limited bandwidth. What is the most immediate, practical way AI is helping organizers make better decisions right now?
Tesi: The biggest immediate benefit is speed, but it is important to be precise about where the speed actually sits. Tasks that used to take an analyst two to three weeks such as mapping an industry, sizing a market segment, listing competitors or profiling a country, now take a couple of days and the output is more detailed. That is the data-collection layer, and that is where AI compresses the timeline. The consulting layer on top – sense checking the output against what we know about the market, quality checking the sources and interpreting what the numbers actually mean for a specific organizer – still has to be done by people. AI does not remove that work; it just lets you get to it sooner.
At MBB we do this work with our AI Data Scanner, and organizers tell us the real value is not that AI produced the analysis. It is that the commercial team can have the strategic discussion two weeks earlier, with better data and a human-checked interpretation in front of them. That earlier discussion changes how you set prices, decide whether to launch a show in a new country and shape your overall event portfolio. You are making decisions based on current evidence, not on last year’s assumptions.
For a busy organizer, the practical benefit is simple: less time between asking a question and getting an answer you can defend.
You’ve worked across major markets across the globe. How does AI change the challenge of understanding and comparing performance across markets that have very different attendee and exhibitor behaviors?
Tesi: Comparing markets has always been difficult because the basic inputs are not the same. A square foot of exhibition space in Frankfurt sells at a different price, to different exhibitors, with different visitor expectations than a square foot in São Paulo or Bangkok. Visitor behavior, what exhibitors want and the local competition all start from different baselines.
AI lets you put all of that information on a single, comparable basis. We can combine data from many different sources such as exhibitor lists, visitor demographics, nearby events, trade flows and regulation, and structure it so you are genuinely comparing like for like. AI also shows the patterns behind the numbers: which segments are growing in one country and slowing in another, where international exhibitors are shifting their spending and which industries are starting to overlap. Instead of only looking at the headline revenue or attendance figure, you can see what is actually driving it. That is the level of detail you need to make real decisions about your event portfolio.
When professionals hear “AI insights,” they may picture dashboards and reports they don’t have time to read. How do you make sure AI becomes a decision-support tool that people actually use, rather than just another layer of data noise?
Tesi: This is the most common mistake I see, and it is why many AI projects fail. A team launches another dashboard, people use it for about a week and then it joins the long list of tools that nobody opens. The solution is to stop building dashboards and start with the decisions you actually need to make. Before building anything, list the three or four decisions each year that genuinely matter. For example, launching a show in a new country, changing the price of booth space, closing a segment or responding to a new competitor. Then design the AI tool to support exactly those decisions.
The second part of the solution is the interface. People will not read a 40-page report, but they will spend five minutes asking questions of a chat tool that answers in plain language. The third part, and the one teams skip most often, is the data processing and quality check behind it. You need a defined way to collect the data in the first place and a quality-check step that catches errors, gaps and stale figures before anything reaches a decision-maker. This is the foundation of any credible industry analysis: in our world a single number can drive a pricing change or a country launch, so the data behind it has to be defensible. A chat interface on top of bad or unchecked data is worse than no tool at all, because it sounds confident.
Decisions, interface, data process: get those three right and AI becomes something the team actually uses.
Competitive intelligence has always been a sensitive area in the exhibition industry, with organizers often reluctant to share data. How does AI navigate that reality, and what can it tell you about your competitive position even when competitors aren’t cooperating?
Tesi: Our industry has always treated its own data as confidential. The irony is that a large amount of competitive information is already publicly available. It is simply spread across many places and time-consuming to collect. Exhibitor lists, floor plans, conference programmes, sponsor materials, association press releases, trade press articles, job postings and social media posts from sales teams visiting shows are all public sources of information. AI lets you combine these public sources into a clear view of a competitor’s event portfolio: which segments they are investing in, how they price and which geographies they are expanding into.
You do not need the competitor to share any internal data. This is not a data leak; it is the structured combination of information that is already public. The discipline is staying on the public side of that line, and remembering that even public sources can mislead if you do not stress-test them. The same logic works in reverse: you also need to understand what your own public information is telling competitors. The organizers who get ahead in 2026 will be the ones who treat public, external data as a regular part of their strategy work, not as something they only review when a competitor surprises them.
You will discuss both the challenges and the opportunities of AI tools. What do you see as the challenge that most industry professionals are least prepared for when they start integrating AI into their strategic process?
Tesi: There are two challenges worth naming.
The first is the one most people underestimate because of where we are in the cycle. We are in the middle of an AI hype phase. Thousands of tools and applications are being launched, which sounds like good news but is actually a problem: it is genuinely confusing which ones to use, which ones to integrate, what the real value is, what the integration effort is and what the risks are. A large share of these tools will not exist in a year. Every hype phase carries that uncertainty and exhibition teams have to make integration decisions inside it.
The second challenge, and the deeper one, is data quality and trust in the output. Most teams expect the technical integration work to be difficult, and it is, but teams have done integrations before and know how to resource them. What teams are not prepared for is that AI can produce a confident, well-written, professional-looking answer that is partly or entirely wrong, with no indication that something is off. There is growing evidence that a significant share of the sources AI tools cite either do not exist or do not say what the AI claims. In our industry, where a single number can determine a pricing change or whether to launch in a new country, that level of error is not acceptable.
The teams that succeed build a quality-check step into their process: they verify the AI’s sources, have a human review the output at the key decision points and set a clear rule about which decisions AI can inform and which require a human sign-off. That discipline is what will separate the organizers who scale AI successfully in 2026 from those who quietly stop using it after one bad decision.
Looking ahead, where do you see the clearest opportunity for AI to reshape how exhibitions are positioned and grown? Is it in audience development, market expansion, commercial strategy or somewhere else entirely?
Tesi: If I had to pick one area, it would be the personalized visitor experience, because that is where AI changes the product itself rather than just internal operations. For the last 20 years we have sold a fairly standard experience: a badge, a floor plan, a catalogue and the visitor figures out the rest of their event themselves. AI allows us to reverse that, and it lines up with two of the bigger consumer trends going into 2026: chat-style interaction is replacing menus and multi-step forms, and AI is doing the summarisation work that visitors used to do themselves. The same event can deliver a different, tailored experience to a buyer, a technical specialist, a first-time visitor and a returning VIP across the full show lifecycle.
Before the show, it sharpens pipeline management, content planning and pre-event matchmaking. During the show, it powers navigation, on-floor matchmaking and service. After the show, it drives follow-up, ROI analysis and the learnings that feed the next edition. That changes how you price tickets, design sponsorship, plan content and, ultimately, how many visitors return next year.
Audience development and commercial strategy also benefit, and expanding into new markets becomes faster because the analysis is cheaper. But personalization is where the difference between organizers will become most visible. 2026 is the year the leading organizers move beyond pilots and experiments. The ones who use this window to rebuild the visitor experience around AI will set the standard for the next phase of the industry.
Click here to register for How AI Insights are Reshaping Exhibition Strategy: Understanding Markets, Segments, and Competitors and learn more about upcoming IAEE webinars here.
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