AI has finally gone from a proof-of-concept to a full-on thing in the travel and events industry. The days of wondering if this thing called AI would ever make a dent in how we handle corporate travel and events are long gone. What we’re left with now is figuring out which bits of AI are actually making a real difference in our day-to-day work and where the tech is still just aspirational fantasy. And then there’s the whole governance thing – making sure that we’re not getting too cocky about what the tech can do and losing sight of where human judgement still needs to come in.
We at Uniqueworld spend a lot of time tracking where the tech meets real-world expertise across our global network of destinations, and our DMC Technology Trends 2026 guide goes deep on what tools are actually useful and where they fall short. The biggest risk with AI in event management is getting too confident in what the tech can do, and then getting burned when it doesn’t quite live up to the hype.
This article is all about looking at where AI is really making a difference in concrete, high-value workflows that DMCs and travel managers are using today, and also where we need to set some guardrails to keep those deployments from getting out of hand.
Where AI is actually landing first
From manual receipts to automated GL
GBTA‘s poll in October 2025 found that a big chunk of buyers (51% of them) are planning to use AI for expense reconciliation – automatically matching bookings to expenses and flagging any discrepancies for human review. That’s one of the clearest early use cases out there, and it’s no surprise. Expense processing is a high-volume, rules-based job that’s a perfect fit for where AI can make a real difference.
Modern AI tools for expense processing read receipts on the fly, apply the right GL codes based on company policy, generate compliant transaction descriptions and route any exceptions to human reviewers instead of making them sort through every single line item. For events with big groups and complex multi-seller spending, this can cut down on month-end reconciliations by a big margin – and that’s a big operational saving when you’ve got a big portfolio of events.
The thing to keep in mind is that AI should be routing exceptions to human reviewers, not automatically greenlighting them. Any automated action on an out-of-policy item creates audit risk, and that’s not something we want to have to deal with.
Policy enforcement at the point of search
AI policy enforcement is something that validates trip requests and fare selections against company travel policy before the booking is even made, rather than trying to catch policy violators after the expense comes in. This shift from reactive to proactive compliance reduces policy leaks, eliminates the hassle of manual approval for compliant bookings, and gets genuinely out-of-policy requests to human approvers with the full context already assembled.
For event programmes, this is especially valuable for managing delegate self-booking within a managed framework. When delegates book their own flights and such, AI policy enforcement keeps those bookings on track without having to review every single transaction manually. The result is better compliance at lower administrative cost, provided the policy rules are well-defined and regularly updated.
Traveller assistance available 24/7
AI-powered traveller assistance tools handle routine service requests (like itinerary changes, disruption rebooking, hotel check-in queries and ground transfer updates) through 24/7 conversational interfaces. For events with delegates arriving from all over the world, this reduces the number of people on call at DMC operations teams, while keeping service quality on track outside normal working hours.
The most effective implementations aren’t entirely automated, they can handle routine requests on their own, but also route genuinely complex situations to a human agent with the full context assembled. This hybrid model captures the efficiency gains of automation without losing the service quality that comes with human judgement. A delegate stranded at a closed airport at 2am needs a person they can talk to, not a chatbot. The AI’s job is to triage, escalate and get the response ready, not to substitute for it.
The NDC problem: why data overlay layers matter
New Distribution Capability (NDC), IATA‘s XML standard for airline content distribution, has created a world of hurt for corporate travel data integrity. Several big carriers (like American, United, Lufthansa and British Airways) have started moving meaningful chunks of their content into NDC channels, sometimes withholding it from legacy GDS distribution. The result for event travel managers is content fragmentation: the same itinerary might return different prices, availability and ancillary options depending on which channel you search.
The numbers make it clear that this is no longer just a niche issue. IATA’s NDC tracker says that NDC is now around 24% of indirect airline ticket sales globally, up from 11% in 2023. And Accelya has reported that corporate NDC bookings on its platform rose 168% year on year in the last quarter of 2025.NDC bookings also behave in some pretty unique ways in the back office. Airlines often reference them by their order IDs, and lets be honest the ticketing & servicing data can get pretty out of whack from what you’d expect from your standard gds records, which of course is a major pain for those legacy expense and reporting feeds to reconcile. The result is unless you deliberately handle this stuff, reconciliation breaks, and that’s when AI-powered data overlay platforms come in. They normalise the content from all the different distribution sources into a single interface so you can finally start comparing gds and ndc fares on a level playing field and get some bloody consistent expense data across all your booking channels.
But for organisations that stick their heads in the sand and wait, the data debt they’re accumulating is only going to get bigger. As ndc bookings make up a bigger and bigger chunk of your travel portfolio, those mismatched records are going to get progressively harder to get sorted out, and lets be real fixing them downstream is going to cost a lot more than just building the necessary data overlay now.
DMC-Specific AI Use Cases
Predictive Venue Recommendations
With AI trained on programme history, budget and delegate profiles plus destination performance data, you can get recommendations that are so much more specific than any manual research could ever hope to do at scale. Uniqueworld’s incentive event planning guide goes into just how much of a difference data-driven design can make ; combining expertise from a local dmc with some actual rigour in your analysis, you get better delegate outcomes from your programmes than from just taking a wild stab in the dark.
In practice what that looks like is tools that can recommend venues that are totally calibrated to your group size, budget range, delegate profiles and programme objectives, not just a generic list of five-star hotels in a given city. The AI really just expands the list of options, and the dmc can select what’s actually best for the job.
Budget Scenario Modelling
With AI on your side, budget modelling lets you crank out and compare cost scenarios for all sorts of different destinations, accommodation and activity combinations in minutes rather than hours. This is not meant to replace all dmc judgement, its just meant to expand the number of options you can sensibly consider within a realistic planning timeline and get a feel for the actual cost implications of all your different design choices, that much earlier in the whole brief process.
This stuff is particularly valuable when you’ve got a flat-budget environment where programme designers need to show they’ve genuinely evaluated cost-optimised alternatives and not just fallen back on the usual suspects. And scenario modelling gives you a nice big audit trail to back up these decisions internally ; a nice big document that says this programme was budgeted properly and not just bunged together on the fly.
Real-Time Risk Alerts
AI-powered risk monitoring tracks all the major geopolitical developments, weather events, transport disruptions and public health developments that affect your programme destinations, and can even surface alerts that are relevant to specific upcoming programmes. This is a real game-changer for a global dmc with a lot of programmes on the go across a lot of different destinations, because you can finally get that early warning system that was never possible with manual monitoring alone.
The really useful implementations are the ones that integrate risk alerts directly into your programme management workflow, automatically flagging up the affected itinerary elements and even giving you some initial scenario options to review. This way you can keep human judgement in the loop while still getting that information you need to make good decisions in a timely manner, rather than finding out about it hours later when all your options have already gone.
Automated Post-Event Impact Reporting
Post event reporting is one of those areas that’s consistently woefully under-resourced, and one of the most valuable for demonstrating ROI and informing future design. AI tools that can automatically assemble your spend data, attendance figures, satisfaction scores, emissions data and business outcome metrics into nice structured reports make a huge difference, they save you a ton of time and give you a lot more consistent output than if you were doing it all manually.
For organisations that care about emissions, automated post-event reporting can also generate the scope 3 category 6 data you need – turning what used to be a separate data gathering exercise into an automatic output of programme management. This is another great example of AI finally delivering value that just wasn’t possible to achieve at scale before.
A Worked Example: From Brief to Debrief
Imagine a 150-delegate programme. At the brief stage, AI surfaces a shortlist of venues based on comparable programmes, and scenario modelling lets you compare three budget options, and your dmc can just select and negotiate from there. During planning, policy enforcement keeps your delegate self-booked flights in line. In the run-up to the event, risk monitoring flags a transport strike affecting the day two transfer and generates three alternatives for the programme manager to review. On site, a 24-hour assistant handles delegate queries and escalates the more complicated ones to the operations team. Afterwards, your spend, attendance, satisfaction and emissions data is all assembled into a nice structured post-event report.
The pattern at every stage is the same – the AI does the prep work, and a person decides. That’s the key to keeping your programme both efficient and accountable.
Governance : What Must Stay Human
AI can really deliver the goods in travel and events when it is deployed within clear governance frameworks that define exactly where automation is a good idea and where human judgement is needed. Now GBTA’s October 2025 poll flagged data privacy as a concern even as agentic AI adoption accelerates, which is a reminder that governance needs to keep pace with deployment, and the following principles should be documented in any AI governance policy covering travel and event tech.
- Audit logs for every automated decision: Any automated action that’s automatically triggered to affect bookings, policy enforcement or financial transactions needs a clear, easy-to-read audit log. This is absolutely crucial for proving compliance with laws against corruption, like the FCPA and UK Bribery Act, and for any sustainability reports that use this kind of data. Without this, you’ll never be able to spot systematic errors in how your AI is behaving before they start causing problems across heaps of different bookings.
- Human-in-the-loop for policy exceptions: When a guest books something outside of agreed-upon policy (approval for an out-of-policy booking, for example) or when there’s unexpected hospitality expense, unusual security-related changes to a traveller’s itinerary – all of that needs to go to a specific person for final approval. The AI can do the leg-work and provide all the necessary background, but ultimately a human needs to make the call. You should keep a record of who made the decision and why.
- Data privacy for international programmes: If your AI system is processing traveller data across multiple countries, you need to make sure it’s following GDPR, UK GDPR and all relevant local laws on data protection. You shouldn’t be processing traveller data in an AI system hosted in a country with woefully inadequate standards for data protection unless you’ve got some serious legal backing to justify it – and even then, a data processing agreement will probably be required.
- Regular accuracy auditing: The AI systems you train on past programme data are only as good as that data itself – and if there’s biases or errors in that data, then your AI system will be biased or have errors too. So you need to be doing regular checks to see if the AI’s recommendations are actually working in practice. This will help you catch any problems with how the AI is behaving before they start causing issues on a large scale.
- Transparency and regulatory awareness: Where your AI is actually interacting with travellers, like through a 24-hour chatbot, people should know they’re dealing with a machine. Starting from 2nd August 2026, under the EU AI Act, this will be a requirement. Most event and travel tools will probably not need to comply with the more onerous aspects of the Act, but you should at least check with a lawyer to make sure.
Frequently Asked Questions
Which AI applications are actually ready for big companies to start using in 2026?
Our view is that the ones that really stand out are expense automation, policy enforcement and traveller assistance – these are all fully-fledged, tried-and-tested capabilities that are available from major travel technology companies. Predictive recommendations and budget scenario planning have been around for a while now in specialist platforms, although less as standalone tools, and real-time risk monitoring is available through a few well-established providers. Automated post-event reporting, on the other hand, is the most new-ish capability. and it’s still a bit patchy, so companies looking to use it should ask for specific examples and results before committing.
How does AI change the role of a DMC?
AI takes care of the dull, rules-based stuff, like data processing and policy checking – and leaves the bits that need human expertise to the people with the local knowledge and relationships. So, for the DMC, less time is spent on admin duties and more on the things that really require creativity and planning. The DMC’s value proposition is no longer just about being able to do lots of things quickly – it’s about the quality of their expertise.
What governance documentation should we ask a DMC partner to provide?
Ask to see their AI governance policy (covering audit logs, human-in-the-loop stuff and model accuracy checks), their approach to handling traveller data across borders, how they plan to be transparent about AI use and what data overlay architecture they use. If a partner can’t give you clear answers on these points, they’re not at the governance standard you need for your business.
Where should we start if we’re new to AI in event operations?
Start with the simple, lower-risk applications like expense reconciliation and policy enforcement, these give clear returns and have straightforward governance requirements. Do a trial with a human checking on any exceptions that come up, measure how well the AI is doing and how much time you’re saving – and only then move on to the applications that are a bit more complicated.
Putting AI to Work Without Losing Your Head
AI’s changing the game for corporate travel and events, but the companies that are getting the most out of it aren’t the ones that are automating the most tasks – they’re the ones who are being thoughtful about what they’re automating and what they’re leaving to people. For DMCs and travel managers, that combination of automation, expertise and accountability is where the real value comes in.
Want to see how this all works in practice across a global programme? Contact Uniqueworld’s global operations team to discuss our approach to AI governance.
