AI has officially entered its awkward teenage phase in hospitality.
Everyone’s talking about it, most hotels are using it in some form, and yet behind closed doors many leadership teams are asking the same uncomfortable question – are we actually doing this right? As the industry moves into 2026, AI adoption is accelerating faster than clarity, confidence and consistency.
The result is a widening gap between ambition and impact. While global hotel groups are investing heavily in AI across revenue, marketing and operations, many are still grappling with fragmented systems, unclear ownership and hesitation around letting algorithms influence high-stakes commercial decisions. In essence, AI is no longer the challenge. The issue is how it’s being used. In this environment, competitive advantage increasingly hinges on whether hotels are using the right type of AI for the right decisions, rather than chasing the latest capability.
To unpack what that really means, we spoke with Michael McCartan, Area Vice President EMEA at IDeaS Revenue Solutions. Bringing a pragmatic, executive-level lens, McCartan cuts through the hype to explore where hotels are misapplying generative AI, why mathematical AI remains foundational to pricing and forecasting, and how leaders can close the trust-reliance gap holding AI back from its full potential. Below, he tackles the critical questions shaping the future of revenue, talent and decision-making in hospitality.
In the current environment, where many hotels may be adopting AI without a clear strategy, can you provide a real-world example of how a hotel might be mistakenly using Generative AI for a function that should be handled by Mathematical AI, and what the immediate commercial risk or loss of revenue is from that mistake?
To clarify, it’s not that hotels are turning to ChatGPT or Gemini and asking what they should charge for suites this week – or at least we hope that’s not the case. Where we see hotels potentially getting lost in the AI discussion with revenue management are when tools tout generative AI capabilities that are built upon a poor foundation of data analytics. A generative AI summary of a flawed forecast might save some summarising time, but the results of subpar forecasting, pricing, etc. will remain the same. That’s why it’s critical to use the right tool – what we call mathematical AI – for the right job.
The H2C AI & Automation Report revealed a significant trust-reliance gap, where hoteliers trust AI in concept, but are sometimes reluctant to fully rely on it for critical decisions. Given that IDeaS’ core business is AI-driven decision automation, what non-technical governance or operational steps must a hotel executive take to close that gap?
AI-driven decision automation requires governance and operational discipline, not a plug-and-play deployment. Strong data governance to ensures high-quality, consistent inputs across commercial, operations, and finance teams. Hotels also need to define clear decision boundaries and system controls that reflect the hotel’s strategy, risk tolerance, and brand positioning. Human oversight is particularly critical during solution setup and onboarding to validate AI outputs against market realities, especially in atypical demand conditions. Ongoing monitoring and structured feedback loops are required to avoid over-reliance on rules-based overrides and to maintain trust in the system’s recommendations.
For a hotel group starting its AI journey, what is the most foundational / fundamental piece of AI or automation technology they must implement first, and why?
The outputs of hospitality AI fundamentally rely on data. With that, a well-integrated combination of RMS, PMS and/or CRS remain the key building blocks. Getting these right is critical, whether you’re sceptical of AI or all-in on the potential.
Setting technology aside, it’s also important to consider organisational alignment – or realignment – as foundational to their success. How is automation going to influence your staffing needs? That’s not just a consideration of raw headcount, but also of skillsets and capabilities. We’re poised to spend far less time at work tending to the routine, time-intensive tasks automation handles exceptionally well. Hotels need to identify where skills gaps exist, reevaluate staffing plans and priorities, and consider operational impact of tech-driven process and workflow changes.
As AI automates more tasks, it is often said that the Revenue Manager role is shifting from tactical execution to strategic analysis. What strategic question should Revenue Managers focus on that AI can’t answer, and how does IDeaS provide the necessary insights?
The strategic considerations weighed for group business opportunities are a great example of this coming into play.
From a strictly numbers standpoint, the decision to pass or press for substantially more hotel-friendly terms with a group opportunity during a period forecasted for high transient demand may seem like the right move for your bottom line. But what if that group opportunity is from an organisation that brings steady business in year-round? Is the short-term gain worth potentially harming the long-term relationship? Revenue managers and their sales counterparts need to carefully weigh these factors before making judgement calls. We do offer solutions that help commercial teams quickly gauge the projected impact of displaced transient business and guide potential negotiation terms so these judgement calls are well-informed.
For independent hoteliers with limited budget, which AI type offers fastest ROI, and what’s the biggest risk for global chains with fragmented AI adoption? How can IDeaS assist in these instances?
With independent hoteliers, I’d question the wisdom of prioritising “fast” ROI from AI – the better guiding star is what’s most effective and proven.
My guidance for independent hoteliers – and it applies for any hotel, really – is that any AI initiative should be led by the senior leadership. It can’t be a side project for one siloed department. Secondly, this initiative needs to consider and tackle complete workflows. This requires collaboration across departments, process alignment and structured, high-quality data inputs.
Also read: IDeaS reveals 2026 hospitality predictions