Insight
Teaching the future
Mark Parsons shares his thoughts on what five years in the classroom reveals about how exhibition organisers are really changing
ne of my guilty pleasures
O each year is teaching an MSc course on building data-led organisers at SPD Scuola Politecnica di Design Milano, Italy, sponsored and jointly organised by Fondazione Fiera Milano. It certainly strokes the ego – Professor Parsons has a satisfyingly hubristic ring to it – but, more importantly, the course has become a reliable mirror of the pace of change in our industry. What shifts in the classroom often surfaces in organiser strategy and operations a few years later.
Had I chosen an easier subject – exhibition M & A, for example – I could reuse the same material each year with minor tweaks. Instead, teaching how to build a data-led organiser has become an annual exercise in controlled discomfort. Each January I reopen last year’ s slides with trepidation, quietly deleting whole sections and reworking others as new capabilities move from“ interesting experiment” to“ baseline expectation”. The rate of change, particularly driven by AI, has been anything but incremental.
Above: Mark Parsons
“ Each January I reopen last year’ s slides with trepidation, quietly deleting whole sections and reworking others as new capabilities move from“ interesting experiment” to“ baseline expectation”
This article is an attempt to step back and reflect on what has genuinely changed since I first started teaching this course five years ago – and what that might mean for the class of 2030. Not as a set of technology predictions, but as a lens into how our own businesses are evolving in practice.
The students entering the industry over the next few years are increasingly AI-native, and the assumptions they bring about data, automation and decision-making will quietly but profoundly reshape the day-to-day reality of exhibition organisers, whether we are ready for it or not.
From‘ data-led’ to transformational Five years ago, teaching how to be a‘ data-led organiser’ was a relatively contained ambition. The focus was on achieving a single view of the customer: breaking down silos, integrating systems, and building dashboards to better understand event performance. There were early attempts at clustering audiences, analysing intent, and experimenting with propensity scores to guide sales and marketing. But this was a world before deep personalisation, mass automation, inexpensive enrichment – and certainly before what I now describe to my students as building an organiser using‘ infinite AI interns’. Data work was expensive, slow, and largely confined to specialist teams and skillsets.
Those foundations still matter. Data hygiene, integration, and lineage remain essential. But from today’ s vantage point, the scope of what was possible then now feels narrow. The shift has been transformational. 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. The ability of AI to generate usable code has also
30 Issue 1 2026 www. exhibitionworld. co. uk