AI is getting plenty of attention across hospitality.
Commercial teams are exploring where it can add value, which tasks make sense to delegate and how much trust they are prepared to place in the technology.
But for all the experimentation, its effect on day-to-day commercial work has been relatively modest so far.
When Lighthouse asked 761 hospitality professionals how much AI affects their commercial strategy today, the average response was 3.02 out of 5. In 2024, it was 3.06.
At least by this measure, AI’s perceived impact on commercial strategy has barely moved.
Part of the reason may be how AI has been used so far. Helping improve a forecast, drafting campaign copy or producing a report faster can certainly save time. But those efficiencies do not fundamentally change the commercial operating model.
Revenue managers are still opening the same systems, reviewing the same dates and working through much of the same information before they know where their attention is needed.
The bigger change starts when AI takes on more of that groundwork, bringing commercial teams closer to the point where a decision needs to be made.
Rather than reviewing everything to find the issues that warrant their time, teams can start with the exceptions. Revenue, sales, marketing and distribution still bring their own expertise, but more of their time can go into using their judgment on the opportunities where they can still influence revenue.
Key takeaways
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AI has had a relatively modest impact on day-to-day commercial work so far. Most of the gains to date have come from speeding up individual tasks rather than changing the commercial workflow.
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The bigger shift comes when AI takes on more of the groundwork, giving commercial teams a clearer starting point for where to focus their time.
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Commercial teams keep their functions but spend less time assembling the picture and more time setting guardrails, supplying context and checking recommendations.
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The capacity created can be redirected into work such as need-date strategy, group decisions, forecast challenge and channel optimization.
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More autonomy makes data quality and clear commercial guardrails even more important.
From reviewing everything to managing exceptions
A significant part of many revenue managers’ mornings is spent establishing what has changed before they can decide what needs their attention first.
Pickup and pace need to be checked. Competitor pricing may have moved. A group may be underperforming against its block. An event may have changed the outlook for a particular date. Distribution issues can appear without warning.
Sales and marketing also have their own signals to monitor, while at portfolio level the challenge becomes deciding which hotels deserve attention first.
With so much data available and so many systems to navigate, the difficulty is often deciding which signal matters most, particularly when several point in different directions.
In a recent Strategy Lab session, Lighthouse commercial strategist Daniel Foreman looked at a hotel that seemed well positioned for an October weekend.
Occupancy was healthy, but the picture underneath it was more complicated. A meaningful share of the occupancy came from a wedding block with weak pickup. One shoulder night was priced well above the compset. At the same time, market demand for the wider weekend remained strong.
Any one of those signals could have led to a different conclusion if viewed in isolation.
Daniel asked Ernest, Lighthouse’s AI teammate for commercial teams, to assess the weekend. Ernest brought the group pickup, rates and market demand together.
That allowed Daniel to focus on the commercial response, which in this case, was to lower the shoulder-night rate, stay firm on the stronger dates and allow the group to reach its cutoff so any unused inventory could return to transient.
With the group, rate and demand picture already in front of him, taken care of by his AI teammate, his time could go straight into deciding exactly what to action, rather than wasting valuable time establishing what was happening.
Commercial teams do not need another dashboard to check
Hotel commercial teams already have plenty of information.
The constraint is the time required to work through it.
Revenue managers routinely work through future dates, pickup reports, forecasts, pricing, channel performance and other indicators looking for the relatively small number of changes that genuinely require their intervention.
Quite often, that review simply confirms that everything is broadly on track.
If routine monitoring can happen continuously in the background, every date does not need the same level of attention. The revenue manager can start with the properties, dates or issues where something has changed materially.
This is the principle behind Ernest’s Routines and Smart Insights. Routines take care of recurring analysis, while Smart Insights proactively monitors the next 90 days, highlighting meaningful pricing risks and opportunities and suppressing low-impact noise so you can quickly investigate what needs attention.
At a portfolio level, prioritization becomes even more important.
A commercial leader responsible for ten, twenty or fifty hotels cannot examine every property in equal detail every day. Whether the focus is one hotel or a much wider portfolio, Ernest can help commercial leaders identify where closer analysis is needed.
That creates capacity for the work that is harder to systematize, like challenging a forecast, working through need dates, assessing group opportunities, reviewing channel contribution or spending more time with the properties where intervention can still change the outcome.
Faster analysis still leaves the difficult decisions
Completing the analysis sooner with the help of AI does not necessarily make the commercial decision straightforward.
Consider a group inquiry, for example. A displacement analysis may show that, even after the expected value of the group is included, the hotel expects to generate more by protecting rooms for transient demand. That is useful information, but it does not settle the decision.
Sales may know the account has another 1,000 room nights coming up for tender later in the year. Revenue may know the same dates usually fill with higher-rated transient business. Ownership may be pushing the hotel to protect rate rather than add volume.
Each factor can legitimately affect the decision.
Ernest’s built-in Group Displacement skill can complete the underlying analysis before sales and revenue review the opportunity together.
The meeting can then focus on the questions that can’t be resolved by the calculation alone: How confident are we in the transient demand? How strategically important is the account? How much future value are we prepared to attach to the relationship? What risk are we willing to take?
A displacement analysis carried out by AI can show the value of the business the hotel may be giving up. It cannot, on its own, determine how much weight to give a wider account relationship, future business potential or the hotel’s current commercial priorities.
Those remain judgment calls for the revenue and sales teams.
More autonomy requires more confidence in the inputs
There is a material difference between asking AI to identify an issue, asking it to recommend a response and then allowing it to execute that response.
In practice, AI can take on more responsibility gradually, moving from identifying an issue and recommending a response to acting on a decision once the organization is comfortable with the guardrails around it.
A revenue manager may be comfortable with AI flagging that a rate appears out of line and suggesting an adjustment. Publishing that rate introduces a different level of risk.
The same applies in distribution. Identifying a parity problem is different from automatically changing availability or channel settings without someone reviewing the consequences.
That distinction becomes more important as AI becomes directly connected to the systems where commercial decisions are executed. The closer AI gets to action, the more important the quality of its inputs becomes. Its decisions depend on having the right commercial context to support them.
For an AI-centered team, that makes the underlying data and intelligence foundation critical. The more an AI system is expected to do, the more important it is that it is built on complete, relevant and hospitality-specific context.
Ernest is built around that principle. He runs on the same platform that powers 80,000 hotels across 185 countries, combining your property, compset and market data with hospitality-specific context and deterministic algorithms informed by Lighthouse revenue management expertise.
Ernest is currently focused primarily on intelligence and recommendations, and shows the sources behind his answers so users can review the information supporting them.
As more actions are enabled, that same foundation makes it possible to give Ernest greater responsibility within approved guardrails, with actions logged and auditable. An AI-centered team can therefore increase autonomy without giving up visibility or control.
What an AI-centered commercial team looks like in practice
An AI-centered commercial team may have the same people and responsibilities as it does today. The difference is that commercial issues no longer need to be worked through separately by each function before the team can decide what to do.
Jeff Hinkle, Vice President of Revenue Management at Stonebridge, is already using Ernest this way. His team works to a rotating commercial agenda, with limited time available for each property. That agenda is fed into Ernest so it can prepare the relevant insights ahead of the discussion.
“The teams still need to review the reports and validate the information, but now they have talking points in front of them, dates that might need to be discussed, or rate changes that look anomalous. We can collect those signals in a much more automated fashion and use them to support a more robust conversation.”
The team then adds what the data cannot fully capture: account value, local market knowledge, ownership priorities, appetite for risk and any change in strategy that Ernest needs to take into account.
After the team decides what to do, Ernest can keep monitoring the factors influencing those decisions. If the same conditions hold, there may be nothing further for the team to do. If they change materially, the issue comes back for review.
For GMs, owners and commercial leaders, that should translate into something more meaningful than simply faster reporting. The bigger benefit becomes having enough commercial coverage to respond sooner when conditions change and catch opportunities that might otherwise be missed.
The real opportunity is commercial capacity
AI is often discussed in terms of the time it saves on individual tasks. Across a commercial team, the more interesting question is what that saved time makes possible.
Lighthouse’s survey suggests hotels are already using that additional capacity in different ways.
Some 14% of respondents said team members are managing more properties with the same headcount. Another 24% said the same people are doing more with the hotels they already manage. Four percent reported reducing headcount.
For some hotel groups, that may mean greater portfolio coverage without increasing headcount at the same pace. For others, it may mean keeping portfolios at the same size and putting more time into the hotels they already manage.
Jeff makes a similar distinction between the work required to reach a decision and the work that can directly influence revenue:
“Pulling reports, aggregating data and doing the analysis are required, but they’re not revenue-generating tasks. The opportunity is to reinvest that time in forward-looking work.”
With Ernest taking on more of that preparation, an AI-centered commercial team can spend more of its time on forward-looking strategy and the decisions that can still influence the outcome.
FAQsWhat is Ernest?
Ernest is Lighthouse’s AI teammate for hotel commercial teams. You can ask him questions in your own words about your hotel’s pricing, performance and market, and he answers based on your property’s data, with a suggested next step. He can also keep an eye on things in the background and flag what needs attention.
How is Ernest different from generic AI?
General-purpose AI tools don’t know your hotel, your market or how revenue management works, so their answers can sound right and still be commercially wrong. Ernest is built for hospitality and works from the Lighthouse platform, which covers 80,000 hotels in 185 countries. He can also be tailored to your team’s workflows, data and commercial priorities.
Does Ernest replace our revenue manager?
No. Ernest takes on much of the groundwork, like checking what has changed, pulling the information together and preparing the first pass at an answer. The decisions stay with your team. Ernest is currently focused primarily on intelligence and recommendations. As more actions are enabled, organizations can decide what requires approval and what can operate within agreed guardrails.
What happens if Ernest doesn’t know something?
Ernest can only work with the information available to him. That’s why he shows the sources behind his answers, so your team can check the reasoning before acting. Teams can also give him extra context, such as reference files and strategy rules, and local knowledge from the people on the ground still matters.
Is our hotel’s data safe with Ernest?
Yes. Your hotel’s commercial data stays private to your organization. It isn’t shared with other customers or used to train shared AI models, and access follows each user’s existing Lighthouse permissions.
Joe Hanly
Joe Hanly is a writer and content creator at Lighthouse, helping hoteliers navigate industry trends with clear, engaging storytelling. He believes the best insights come from real experiences – that’s why when he’s not writing about travel and hospitality, he’s out exploring it firsthand. It’s all research. Sort of.
About Lighthouse
Lighthouse (formerly OTA Insight) is the leading commercial platform for the travel & hospitality industry. We transform complexity into confidence by providing actionable market insights, business intelligence, and pricing tools that maximize revenue growth. We continually innovate to deliver the best platform for hospitality professionals to price more effectively, measure performance more efficiently, and understand the market in new ways.
Trusted by over 65,000 hotels in 185 countries, Lighthouse is the only solution that provides real-time hotel and short-term rental data in a single platform. We strive to deliver the best possible experience with unmatched customer service. We consider our clients as true partners – their success is our success.
Source: View the original article at Lighthouse.