I opened my email to one of the latest Event Industry reports for AI, where we see a world operating on tight margins, late decisions, rising costs, and attendees who crave connection more than anything. It’s a practical, boots-on-the-ground snapshot of what planners, marketers, and producers are wrestling with right now.
And then I couldn't help it, but put it next to McKinsey’s State of AI in 2025, and the contrast hits you like a strobe light in a dark ballroom.
One report speaks in the language of day-to-day execution. The other speaks in the language of economic pressure, organizational reinvention, and AI as a competitive weapon.
Same world. Two completely different altitudes.
What emerges is a simple truth:
The event industry isn’t suffering from a lack of innovation. It’s suffering from a lack of alignment with the pace of innovation happening everywhere else.
Because while event teams are wrestling with:
- late registrants
- cost inflation
- limited ROI measurement
- overpacked agendas
- sponsor hesitation
- and a very human desire for connection
the broader business landscape is wrestling with:
- AI governance
- workforce transformation
- agentic systems replacing entire workflows
- multi-million-dollar productivity gains
- risk, compliance, safety
- AI-driven competitive pressure across every function
Put together, these reports show a gap that’s wider than most people realize.
On one side: Events are using AI to save time.
On the other: Enterprises are using AI to change their operating model.
And that gap has real consequences for how events are funded, evaluated, staffed, and expected to perform in 2026 and beyond.
Which is why comparing these two reports isn’t just interesting. It’s necessary. It shows where the industry is aligned with the world around it and where it’s lagging behind.
So let’s break it down.
1. OVERALL STORYLINE: HOW THEY DIFFER
Event Industry AI Report 2025
- Tactical, experience-focused, grounded in event operations
- Concerned with budgets, late registrants, networking value, sponsorships, and ROI confusion
- AI viewed as a productivity tool
- Pain: “We’re squeezed on time, costs, and expectations.”
McKinsey State of AI 2025
- Strategic, economic, and organizational
- Focuses on enterprise adoption, AGI-level capabilities, risk management, workforce shifts
- AI viewed as competitive advantage and transformation engine
- Pain: “We’re leaders or we’re dead.”
Translation: Your industry report shows micro-level operational stress. McKinsey shows macro-level corporate pressure to adopt AI faster, strategically, and responsibly.
Put together, they say: Events are behind the wider corporate AI curve and underusing AI’s strategic potential.
2. AI ADOPTION: WHERE THEY ALIGN AND WHERE THEY DON’T
Both reports agree on these points:
- AI adoption is accelerating everywhere
- Efficiency is the first (but not best) use case
- Companies want AI for summarization, personalization, workflows
- Skills gaps and internal resistance slow adoption
- GenAI is becoming a baseline expectation, not an innovation point
But here’s where they diverge:
Event Industry View
- AI = automation + time saver
- Top applications: content summaries, matchmaking, recommendations
- Adoption is high, but maturity is low
- Risk awareness barely appears
McKinsey View
- AI = revenue growth engine
- Top applications: enterprise search, agentic workflows, software dev acceleration, customer support automation
- GenAI leaders outperform non-adopters on revenue, cost reduction, and product velocity
- Risk governance & safety frameworks are now required
Critical overlap: The event report shows interest in AI-powered personalization and session takeaways. McKinsey shows that companies who operationalize AI at scale increase productivity by double digits and shift entire workflows.
Conclusion: Events are treating AI as “nice-to-have efficiency” while the broader market treats AI as “business model redesign.”
3. BUSINESS & ROI: WHAT EACH REPORT SAYS
Event Industry
- ROI measured with surface metrics: registrations, attendance, satisfaction
- Only 25% measure pipeline influence
- Sponsors are cautious with spend
- Networking is the top value driver
- Costs are rising faster than budgets
McKinsey
- Companies using AI in core workflows see:
- Leaders insist on:
What this means: Events talk about ROI as attendance. Enterprises talk about ROI as growth and margin improvement.
4. EXPERIENCE DESIGN VS. ENTERPRISE OPERATING MODELS
Events
- People attend for connection
- Personalization is increasingly expected
- Last-minute behavior makes personalization harder
- AI interest is centered around attendee-facing UX
McKinsey
- AI is shifting operating models from:
- The next evolution = agents that plan, analyze, decide, and act
Cross-analysis insight: The event industry wants personalization but doesn’t yet have the infrastructure (data maturity, AI orchestration, governance) to deliver it.
McKinsey’s data says that industries with strong AI operating models win.
Events could adopt that same sophistication to solve their biggest pain:
- unpredictability
- attendee chaos
- sponsor conversion gaps
- content overload
- lack of measurable ROI
5. RISK, SAFETY & GOVERNANCE
Events
- No meaningful discussion of AI safety, bias, model monitoring, or governance
- AI perceived as low-risk because it’s mostly content generation
McKinsey
- AI governance is now a top-three concern
- Companies are building AI risk units and safety guardrails
- AGI-level capabilities raise ethical, security, and operational complexity
- Organizations worry about hallucination, compliance, model drift, and harm
Gap: Events are behind the risk curve. This becomes a problem once events expand into AI matchmaking, automated scheduling, real-time assistants, or decision-making tools.
What This Means for You
The two reports together reveal something important:
The event industry is two steps behind the enterprise AI curve.
Not because it lacks vision, but because it lacks:
- integrated data models
- AI operating systems
- pipeline attribution infrastructure
- AI-enabled workflows
- leadership alignment
If events want to meet enterprise expectations, they must shift from:
AI for efficiency → AI for outcomes AI for tasks → AI for workflows AI as add-on → AI as operating model