Budget the Meter, Not the Machine: Planning for 2027 When AI Runs on Usage

For hospitality leaders heading into 2027, the hard part is no longer adopting AI. It is measuring it, governing it, and paying for it responsibly.

For decades, hospitality budgeting followed a familiar logic. Technology costs were predictable, capital cycles were manageable, and the annual forecast provided enough structure for decision-making. That reality is changing.

As AI becomes embedded across guest communications, revenue management, operations, and the back office, hotels are encountering a new kind of expenditure. Unlike traditional software, AI costs fluctuate with usage: the more it is used, the more it costs. Many hospitality organizations are not yet equipped to manage that variability.

In a recent interview, Terence Ronson, Founder and Managing Director of Pertlink Limited and Chair of the HFTP AI Collective’s AI Education & Training Subcommittee, explained what this shift means for budgeting, governance, workforce investment, and long-term competitiveness. Ronson, an inductee of the HFTP International Hospitality Technology Hall of Fame and the CHTA Hall of Fame, has spent more than 25 years opening hotels across Asia.

Key Takeaways

Budget AI as a metered operating cost, tracked per guest.

Fund AI literacy as seriously as the technology itself.

Put AI spend on a rolling forecast, not an annual one.

Judge technology on efficiency, and on whether it frees people for better guest moments.

Governance and data quality will separate the leaders from the rest.

AI turns software spend into usage-based spend

Most hotel technology budgets assume software behaves like a fixed asset: licenses, annual renewals, predictable costs. According to Ronson, AI breaks that model. Every guest interaction an AI system handles incurs a cost, and that cost grows as adoption grows.

His answer is a metric he developed called Token Cost Per Guest (TCPG): the AI cost of serving one guest across the whole journey, from the first pre-arrival question to the reply to their post-stay review.

“AI doesn’t come with a subscription fee. It comes with a meter,” he says. “And that meter runs every time a guest says hello. If you can’t tell your owner what AI costs per guest, you don’t have an AI budget. You have a hope.”

Budget for people, not just platforms

One of the largest barriers to AI adoption is people, not technology. Many organizations still treat training, role-specific guidance, and change management as optional line items, then struggle to realize value from the tools they deploy. Revenue managers, housekeeping supervisors, engineers, HR teams, and general managers will all work with AI-enabled workflows.

Ronson recommends ring-fencing a workforce line inside every AI budget rather than funding training from whatever is left over.

“Buying AI without training your people is like installing a grand piano in a hotel with no pianist,” he says. “It looks impressive in the lobby. Nobody gets any music.”

Annual budgeting is too slow for AI spend

Payroll, capital projects, and contracts remain relatively stable across twelve months. AI consumption does not. Usage can rise quickly with guest volumes, adoption rates, and changing vendor pricing, creating swings an annual plan will miss.

“Keep the annual budget for payroll and capex. Put AI on a rolling monthly or quarterly forecast, with a clear trigger for when usage, and cost, outruns the plan,” Ronson says. “Flexibility without a trigger is just drift.”

Look beyond the hotel’s walls

External forces belong in the 2027 budget too. Guests increasingly begin their trip inside AI assistants that shortlist a handful of hotels before anyone clicks, and hotels that budget only for traditional search and OTA visibility risk becoming invisible. Across Asia-Pacific, fragmenting data-localization rules also shape where guest data can be stored and which AI vendors a hotel group can use. These are budget decisions as much as legal ones.

“The booking used to be the battle. Now the battle is the context before the booking,” he says.

Usage is not the same as value

Technology projects are often judged on implementation milestones, adoption rates, or features delivered. Those measures rarely prove value.

“A busy chatbot is not a profitable chatbot,” Ronson says. “The most common ROI mistake I see is counting the conversations and never counting the cost of each one, or what happened to the guest afterwards.”

His framework, grounded in what he calls the Human Experience Orchestrator (HXO) model, asks two questions. Does the investment improve efficiency or reduce the cost of serving guests? And, more importantly, does it create space for meaningful human interaction?

He also urges leaders to separate three outcomes that business cases often blur: efficiency (doing the same work faster), cost saving (removing spend), and value creation (revenue or loyalty that did not exist before). Each needs its own measure.

The same logic applies to balancing guest-facing innovation with back-office efficiency. Ronson advises starting behind the scenes, where data is cleaner, the risk to guests is lower, and returns are easier to prove. “Boring AI pays for exciting AI,” he says. “Get the back of house working first, then use what it saves to fund what guests will actually see.”

Governance is the real differentiator

Regulatory scrutiny and liability concerns mean governance can no longer be an afterthought. Ronson points to Moffatt v Air Canada, in which the airline was held liable for incorrect refund guidance from its website chatbot, and a Munich court ruling concerning inaccurate AI-generated information about a hotel.

He recommends two tools that belong in the budget, not just the policy manual: a Decision Envelope, which documents what an AI system may decide alone, what it must escalate, and what it may never do; and the AI Override Rate, which tracks how often staff correct or reverse AI decisions.

“If nobody ever overrides the AI, either it’s perfect or nobody’s watching,” he says.

Data quality sits underneath all of it. Fragmented or inconsistent data undermines every AI investment built on top of it.

Three lines every 2027 budget needs

Asked what he would insist on if he were advising a hotel CEO preparing a 2027 budget today, Ronson names three priorities:

“None of these are glamorous,” he says. “All of them are the difference between an AI strategy and an AI expense.”

The real advantage is judgment

By 2030, access to AI will be increasingly standardized. Models will improve, costs will evolve, and most competitors will have similar tools. What will not be commoditized is organizational judgment, operational discipline, and the ability to create memorable guest experiences.

The organizations best prepared for the future will not be those that spend the most on AI. They will be the ones that measure carefully, invest deliberately, strengthen their data foundations, and use efficiency gains to give their people more time with guests.

Or, as Ronson puts it: “The intelligence may be artificial. But the experience is human.”