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changed who can build things. Anyone with an idea can now prototype an analysis, workflow, or even a product. The challenge is no longer whether something can be built, but how experiments become scalable, production-ready capabilities.
This is particularly visible in sales and marketing. Much of the infrastructure supporting highvalue, high-complexity exhibition sales and marketing is becoming increasingly automated. The human touch still matters – relationships, trust, and judgement remain central – but the mindset around technology spend is shifting. Increasingly, the question is not“ What system should we buy?”, but“ How can this technology make our people more effective?”. That reframing, from systems to employee efficiency, is subtle but critical.
Designing data, not inheriting it In the classroom, I often use advances in generative models as a way of bringing the pace of change to life. In the early years of the course, image generation models were novel and imperfect; they provided rich teaching moments. The mistakes they made were visible and instructive. Last year, many of those examples no longer held and I found myself reaching for video generation as the new edge case. This year, even that is becoming harder. As models develop deeper spatial, reasoning and analytical capabilities, the obvious failure modes are fewer. From a teaching perspective, it means the ground is constantly shifting. From an industry perspective, it should prompt reflection on how quickly yesterday’ s differentiators become today’ s baseline.
Despite this progress, there remains a degree of nervousness within the exhibition industry. Much technology capability is still outsourced to thirdparty providers. That often makes sense, but there is a growing risk that
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we outsource not just execution, but thinking. Understanding your own data – interrogating it, questioning it, exploring it – cannot be fully delegated.
This is where one of the core themes of my teaching comes in: data as infrastructure. Over time, I’ ve increasingly applied frameworks such as the business model canvas to exhibitions, not as an academic exercise but as a way of forcing clarity. When students think explicitly about the sources of first-party data a show creates, and the value that data might generate, they develop a richer understanding of what makes an event distinctive within its competitive landscape.
Most importantly, I try to impart the idea that data collection should be designed intentionally. My students are designers, and they instinctively understand that physical spaces are designed with purpose. Data should be no different. Shows exist to bring communities together. The data they generate is a by-product of those interactions – but only if we choose to treat it that way. If we do not design for it, we simply inherit whatever happens to fall out of our systems.
Two questions for the industry This brings me back to the classroom as a framing device for the industry more broadly. The students I teach are curious. They assume they should be able to explore data, test ideas, and learn through doing. That mindset doesn’ t have to be generational; it can be is cultural, and it can be learned. Anyone can adopt it.
So rather than ending with predictions, I’ ll end with two questions I increasingly encourage organisers to explore. First: What data does our show uniquely create that no competitor can replicate? And second: To what extent are we designing our data intentionally or inheriting it by accident?
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“ AI makes it possible to analyse vast volumes of unstructured data – from sales calls to exhibitor feedback – in ways that would have been hard to imagine in 2021” |
Neither question requires a wholesale transformation to answer. But both invite a more curious, more deliberate way of thinking about the assets our industry creates every day. EW
n About the author Mark Parsons is the founder of Events Intelligence, a data business which helps organisers find their next 100 exhibitors. He has worked with exhibition organisers for the last 20 years, and holds degrees from Cambridge, LBS and NYU Stern in various business and data science disciplines. He teaches Exhibition Design at Scuola Politecnica di Design in Milan.
Photos from SPD
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www. exhibitionworld. co. uk |
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