6 AI Trends for 2026: How AI will change events next 12 months
14 min read • Wed, Nov 19th
trends
AI isn’t a shiny side project anymore. It’s quietly sliding into every part of the event lifecycle—how we plan, price, market, and run shows on the day.
Research from firms like McKinsey points to the same thing: most organizations are already using AI somewhere, and budgets are shifting toward smarter, more tech-enabled experiences—while still being watched like a hawk.
At the same time, event goers are getting pickier. In Loopyah’s 2025–2026 Event Attendee Report, 67% of respondents said lineup, performers, or speakers are “very important” when deciding to attend, and 48% have abandoned checkout because of unexpected fees. Translation: you need to nail relevance, clarity, and convenience—or they’re gone.
That’s where the next wave of AI trends in events comes in. In 2026, the winners won’t be the teams with the fanciest demo. They’ll be the ones who quietly use AI to personalize journeys, automate grunt work, deepen analytics, and keep people safe—without losing the human touch.
Let’s break down six AI trends that will actually change how you plan and run events in the coming months—and how to start using each one without blowing up your team or budget.
Personalization used to mean “you’re in the marketer track” or “you get the VIP email.” In 2026, event personalization AI will feel a lot more like Netflix or Spotify—except it’s recommending sessions, people, brands, and offers instead of movies or music.
AI can ingest data from registration answers, ticket types, content clicks, app behavior, previous attendance, even on-site scans. Then it uses that to recommend a unique path for each person, in real time.
Think experiences like:
A personalized agenda that suggests sessions, meetups, and demos based on someone’s job, topics they’ve starred, and what similar attendees engaged with last year.
Dynamic recommendations in the app: “You liked yesterday’s sustainability panel—consider these two follow-up breakouts and this sponsor activation nearby.”
Smarter networking: AI-matched introductions based on goals (“find partners,” “hire,” “pitch investors”), availability, and proximity on-site.
Personalized marketing: emails and push notifications that change subject lines, images, and offers based on people’s behavior and likely interests, not broad segments.
This isn’t sci-fi. Marketing teams are already using genAI and recommendation engines to lift open rates, clicks, and conversions. Events are just late to that party.
The catch: good personalization runs on good data. That means a modern event stack that actually talks to itself—registration, ticketing, app, email, and CRM—rather than five siloed spreadsheets.
If you’re still stitching everything together by hand, start by centralizing your data with an event platform that can serve as your source of truth. Tools like Loopyah’s event software are built exactly for this: one place to collect, track, and act on attendee behavior across the lifecycle.
You don’t need a full-blown “next-best-experience engine” to get value. Start small:
Clean your data. Standardize job titles, industries, ticket types, and interest tags on your registration form. Decide what really matters for recommendations (e.g., role, topics, budget level).
Pilot one use case. For your next event, send each registrant a personalized ‘Build your agenda’ email where AI suggests 3–5 sessions or tracks just for them.
Measure impact. Did those people register earlier, attend more sessions, or rate the event higher? Keep what works, ditch what doesn’t.
Every planner has the same complaint: the work that burns time is rarely the work that moves the needle. Answering the same questions. Fixing typos in names. Chasing vendors. Tweaking schedules for the tenth time.
This is where AI event automation and agentic AI for events step in. Think of agentic AI as a smart digital assistant that can follow a workflow, not just answer a single question. For example:
A bot that handles registration FAQs 24/7, helps people pick the right ticket, and nudges them to complete payment—then logs everything back in your CRM.
An AI assistant that drafts staff schedules from your run of show, flags coverage gaps, and suggests when you’ll need extra volunteers at doors or bars.
Automated vendor workflows: once AV is confirmed, the agent emails requirements, updates floor plans, and reminds them about access times—without you pinging everyone manually.
Smart ticketing flows that auto-fix common data errors, suggest best sections, and help people move through checkout faster.
Remember: nearly 20% of event goers in Loopyah’s attendee study said they abandoned checkout because the site was slow or error-prone, and another 17.8% dropped off due to account-creation friction. That’s revenue you can rescue with better automation, not more ad spend.
The warning label: not every automation project works. Analysts expect a significant share of agentic AI projects to be canceled because they’re over-scoped or under-governed. The answer isn’t to avoid AI—it’s to keep humans in the loop and start with tightly defined workflows.
Look at your current process and ask, “What do we repeat 100+ times per event?” Typical quick wins:
Registration and ticket support: AI chatbots on your landing page and confirmation emails that answer questions, resend tickets, and update details.
Speaker and sponsor coordination: auto-generated reminders, deadlines, and content checklists based on your event timeline.
On-site wayfinding and FAQs: AI assistants in your app that help people find rooms, amenities, and relevant sessions without queueing at an info desk.
If you want to get more systematic about this, pair AI with rock-solid processes. Our guide on event operations walks through how to design workflows worth automating.
Immersive tech has been “the future of events” for a decade. The difference now: hardware is getting cheaper, AI is doing the heavy lifting, and audiences are much more comfortable with playful, interactive experiences.
Analysts expect VR and AR headset demand to grow sharply as AI-powered features and lower costs kick in, according to IDC data reported by Reuters. For events, that doesn’t mean you need to build your own metaverse. It means you can finally create immersive AI experiences that are practical, affordable, and easy to access from a phone or a few shared headsets.
Examples you’ll see more of in 2026:
AI-enhanced AR trails: attendees scan a code and get a guided tour of the venue or expo floor, with overlays that adapt based on what they’ve already visited.
Interactive installations that react to crowds: visuals, soundscapes, or lighting that change depending on movement, volume, or mood in the space.
AI co-created art walls: attendees answer a few prompts and watch an installation generate a unique artwork or avatar they can download and share.
Gamified sponsor journeys: AI-powered scavenger hunts in the app that reward people for visiting certain booths, answering quiz questions, or hitting engagement milestones.
If you want to go deeper into creative formats, check out our playbooks on experiential events and interactive event ideas. They pair nicely with AI-powered experiences.
To keep these experiences magical instead of gimmicky:
Start with the story, not the tech. Ask: what emotion or message do we want people to leave with? Then pick AI + XR elements that serve that story.
Design for first-timers. Clear signage, short instructions, and visible staff make or break adoption. Assume many attendees have never worn a headset before.
Make it optional and inclusive. Never force people into VR to access core content. Offer alternative formats (screens, live demos, printed summaries).
Most teams are sitting on a mountain of event data and using about 5% of it. Spreadsheets, half-used dashboards, random survey exports—it’s all there, but rarely tied together in a way that helps you make decisions in the moment.
In 2026, AI event analytics will feel a lot more conversational. Instead of digging through reports, you’ll ask questions in natural language and get answers in seconds:
“Which sessions attracted the most first-time attendees who bought VIP tickets?”
“Which exhibitors had the highest badge-scan-to-meeting rate last year?”
“Show me a heatmap of areas that felt overcrowded based on scans and session capacity.”
Under the hood, AI connects registration data, app engagement, scans, spend, and surveys into one standard model. Then it can surface patterns humans wouldn’t spot quickly:
Predictive attendance and no-show risk by segment, helping you tune capacity, F&B, and overbooking safely.
Which marketing channels are driving attendees who actually engage (not just click ads).
Which sponsors, formats, or topics create the highest ROI across multiple editions of your event.
Loopyah’s attendee study is a good reminder of why this matters. For example, 59.8% of ticket buyers say service fees make them compare platforms before buying, and 48% have abandoned checkout due to unexpected fees at the end. If you’re not tracking how pricing, fees, and UX impact conversion at a granular level, you’re flying blind.
AI won’t magically fix messy data. Before you plug in a smart layer, do this:
List your data sources. Registration, ticketing, email, app, on-site scans, F&B, merch, surveys. Decide what you’ll actually use for decisions.
Standardize keys. Make sure each attendee, company, sponsor, and session has a consistent ID across systems so AI can connect the dots.
Define your questions. What do you want AI to answer? For example: “Where are we losing attendees in the funnel?” or “Which content themes should grow next year?”
Once that’s in place, AI analytics can go from “cool graph” to “we just avoided wasting €50k on the wrong format.”
Your audience lives in feeds. Loopyah’s research shows 65% of event goers discover events through social media posts or ads, and 40.6% say exciting visuals in content make them click buy. Meanwhile, brands are expected to produce several times more content by 2026.
Doing that manually is brutal. This is where AI-powered content tools shine—if you use them as accelerators, not replacements for taste and strategy.
A modern AI content supply chain for events looks something like this:
Planning: AI helps analyze past campaigns and event performance to suggest themes, angles, and content formats that historically drove registrations and engagement.
Creation: AI writing assistants draft landing-page copy, email sequences, paid ad variations, and speaker intros. Video tools turn long sessions into highlight reels, shorts, and trailers.
Personalization: content engines auto-variant your creatives and copy for segments—by industry, role, or interest tags pulled from registration data.
Distribution: AI chooses best send times, channels, and subject lines based on each contact’s behavior and preferences.
Measurement: analytics loop performance back into the model so your next campaign starts smarter, not from scratch.
Behind the scenes, you still need humans who know your audience, brand voice, and event goals. AI just removes the blank-page panic and versioning hell.
Want to plug this into your broader strategy? Our guide to event digital marketing strategies walks through how organic, paid, and email fit together—AI or not.
To avoid generic or off-brand output, put guardrails in place:
Train AI on your assets. Feed it past campaigns that performed well, your brand guidelines, tone of voice, and real attendee feedback.
Never ship raw AI. Every asset still needs a human edit for clarity, accuracy, and flavor. AI drafts; humans decide.
Measure everything. Track which AI-assisted assets actually convert. Keep humans focused on strategy and creative direction, not churn.
Done right, AI doesn’t replace your marketers. It makes them feel like they suddenly have three more competent teammates who never sleep.
Nothing kills event buzz faster than feeling unsafe or cramped. In Loopyah’s attendee report, 62.6% of respondents said overcrowding is a top negative experience, and 14% flagged safety concerns.
This is where AI crowd management is starting to matter. Modern computer-vision systems can analyze camera feeds in real time and flag:
Areas where density is getting risky (crowd crush potential).
Unusual movement patterns like people suddenly running or pushing against barriers.
Abandoned objects, smoke, or blocked exits.
Crucially, the most promising examples use non-biometric analytics—no facial recognition, just patterns. France’s legal framework for Paris 2024, for instance, allowed time-limited AI analysis of video feeds to detect predefined risks with mandatory human review, not fully automated enforcement.
Beyond video, AI can combine data from ticket scans, Wi‑Fi, and sensors to:
Monitor queue lengths at entrances, bars, and restrooms and prompt staff redeployment when lines blow out.
Alert you before a room hits uncomfortable density so you can redirect attendees or adjust door policies.
Simulate crowd flows in advance to optimize layouts, signage, and staffing plans.
Because safety tech touches privacy and trust, you need clear rules:
Stay within local law. Some regions restrict biometric tech or demand impact assessments. Work with legal and your venue early.
Be transparent. Tell attendees, in clear language, what’s being monitored, why, and how long data is retained. Use signage, FAQs, and your privacy policy.
Keep humans in charge. AI flags anomalies; trained staff make decisions. False positives will happen—plan for them in your operations.
AI should make your crowd feel safer, not watched. If the tech undermines trust, it’s not worth whatever risk score it spits out.
Across all six trends—personalization, automation, immersive moments, analytics, content, and safety—the pattern is the same:
AI is moving from experiments to everyday infrastructure.
Attendees expect smoother, more personalized, more secure experiences—without higher prices or more friction.
The teams that win will pair AI with clean data, clear processes, and human oversight.
If you’re feeling behind, you’re not. Most event organizations are still in the early innings. The key is to stop treating AI as a side project and start building it into how you run events, one use case at a time.
Pick one use case per trend. For example: • Personalization: AI-powered session recommendations in your confirmation email. • Automation: a registration FAQ chatbot. • Immersive: one AI-driven interactive at the entrance. • Analytics: a promptable KPI dashboard for your next event. • Content: AI-assisted ad and email variations. • Safety: non-biometric crowd-density alerts in your busiest zone.
Set clear KPIs. Faster response times, higher NPS, more add-on revenue, fewer queue complaints—whatever matters most to your event.
Run controlled pilots. Start with one event or a subset of attendees. Document what worked, what broke, and what should be scaled or scrapped.
Most importantly: keep your focus on the humans in the room. AI should give you more time and headspace to do the things no algorithm can—curate brave content, host meaningful conversations, and build communities that want to come back.
Explore Our Full Ticketing SystemIf you want a platform built for this new wave of AI trends in events—centralized data, flexible ticketing, and automation-ready workflows—take a look at Loopyah’s tools on our event software page and start mapping where AI can quietly make your next event your best one yet.
The Loopyah Content Team shares expert insights, practical guides, and industry updates to help event organizers create unforgettable experiences and stay ahead in the event planning world.
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