What Would Your Ideal Revenue Management AI Agent Do?

“If I could design one AI Agent for hospitality, it would be a Holistic Revenue Agent that gives a complete view of all hotel revenue streams — not only rooms, but also F&B, meetings & events, spa, and other ancillary income.

Today, many hotels still manage these areas in separate systems, which means decisions are often made in silos. In an ideal world, this agent would connect the PMS, RMS, POS systems, event planning software, CRM, and marketing data into one real-time commercial view. It would automatically show which segments are ahead or behind target, for example transient rooms, corporate, groups, restaurant covers, banquet revenue, or conference space utilisation, and compare this to budget, last year, and future pace.

The real value is moving from fragmented reporting to coordinated action. Instead of every department optimising its own revenue, the hotel would optimise total profit across the entire business.”

“This is definitely taking rate management to a new level; a tool that can read the granular detail, taking data from more than just the PMS, but rates shoppers, excel files, BI tools, benchmarking tools and other online channels.

The resulting factor: shifting rates more regularly, by segment, change one change all, and the opportunity to maximise far more overall segments than just retail. Within this also, given the current environment, a scenario development tool, where it can pull global macro data and use this to give multiple scenarios in terms of where the market may go subject to XYZ happening.”

“If I could design one ideal AI Agent, it would be a Commercial Strategy Agent that combines revenue management, distribution, competitor intelligence, and demand forecasting into a single decision-support system.

Rather than only recommending rates, it would monitor pickup, pace, market demand signals, competitor moves, parity issues, OTA visibility, and profitability by channel, then proactively suggest actions. For example, it could say: “Pickup for next weekend is lagging by 12% versus trend, competitors have softened by 8%, direct channel conversion is down, recommend tactical package offer instead of BAR reduction.”

What would make it powerful is not just data aggregation, but reasoning. It would evaluate displacement risk, group versus transient trade-offs, and simulate revenue outcomes before action is taken.

A second capability I would want is automated exception management. For example, flagging wholesaler leakage or identifying when a pricing decision at one property could create conflict across a portfolio.”

“It would be a Commercial Performance Agent; a kind of always-on strategic assistant connecting revenue management, distribution, marketing, CRM, and ancillary revenue in one place.

Why this one? Because in hotels, the problem is rarely lack of data. The real problem is fragmentation. We have PMS data, RMS data, OTA performance, web analytics, CRM campaigns, spa revenue, F&B capture, upselling results… but very often nobody is looking at the full commercial picture at the same time.

My ideal agent would do three things:

1. First, diagnose: explain not only what is happening, but why. For example, “Weekend occupancy is strong, but GOP may underperform because mix has shifted to high-cost channels.
2. Second, recommend actions: Close lower-yield channels, push direct with breakfast included and adjust cancellation conditions.
3. Third, learn from results: which actions actually improved ADR, NetRevPAR, ancillary spend, and profit.

A practical example: instead of seeing a booking pace report in isolation, I would want the agent to tell me that a lower ADR is acceptable because the guest profile coming through direct is generating stronger total revenue per stay.

For me, that is the future: not AI that gives more reports, but AI that helps us make better commercial decisions, faster and with more context.”

“In an ideal world, I would design a single “Revenue Orchestrator AI Agent” that connects all commercial functions: Revenue Management, Marketing, Sales, and CRM.

For example, if the system detects weak demand for a future period, it wouldn’t just lower prices. Instead, it would: identify the most valuable customer segments, launch targeted campaigns through CRM, adjust distribution channels to favor higher-margin bookings, and suggest value-added offers instead of pure discounts.

At the same time, it would recognise high value repeat guests and treat them differently, focusing on long-term value rather than short-term revenue.”

“In an ideal scenario, I would design an AI agent focused on data quality control.

For example, we are currently building an agent that reviews all daily reservations to ensure there is no “garbage in” the system. By validating and cleaning the data at the source, it allows our AI-powered RMS to perform accurate analysis and generate reliable pricing recommendations.

This type of agent is critical because even the most advanced AI models depend on high-quality input—without it, their outputs lose value.”

“If I could design just one AI Agent, I would design a Growth Agent with two main missions: client acquisition and talent acquisition.

For a Revenue Management consulting company, growth depends on two things: finding the right hotel clients and finding the right Revenue Management professionals to support them. One without the other is not enough. If we bring in more clients but do not have the right talent, we risk compromising service quality. If we build a strong team but do not have a healthy commercial pipeline, growth becomes harder to sustain.

On the client acquisition side, the agent would help identify and prioritise potential hotel clients. It would research properties, destinations, ownership structures, decision-makers, positioning, online reputation, distribution visibility and potential Revenue Management opportunities. It could then help prepare personalised outreach, suggest the right commercial angle and manage follow-ups in a more consistent way.

For example, it could identify an independent hotel with strong guest reviews but weak pricing structure, limited direct booking visibility or possible distribution challenges. The agent could then suggest how to approach that hotel with a relevant message focused on revenue potential, commercial strategy and performance improvement.

On the talent acquisition side, the agent would help identify Revenue Managers who could integrate into the organisation and support our growth. It would look not only for technical skills, but also for mindset: analytical thinking, curiosity, ownership, adaptability, communication skills and the ability to work with technology. In Revenue Management, knowledge is important, but attitude and continuous improvement are just as critical.

For example, the agent could help screen potential candidates, summarise their experience, identify whether they have worked with relevant tools such as RMS platforms, channel managers or BI systems, and assess whether their background fits the type of clients and service model we provide.

What would make this agent especially valuable is that both sides would be connected. It would understand that commercial growth and team growth need to move together. If we are targeting more hotels in a specific segment or destination, the agent could also help us understand what type of Revenue Management talent we may need to support that expansion.

The reason I would choose this agent is that it would help future-proof the business, not only the workflow. It would allow us to be more proactive in finding opportunities, more consistent in follow-up, and more strategic in building the team needed to deliver high-quality service.

In short, I would design an AI Growth Agent that helps us attract the right clients and the right Revenue Management talent, so that growth remains both ambitious and sustainable.”

“Mainly I would say that AI can be a perfect tool for customer support (reservations and concierge). Regarding revenue management, I would say that I would like it to scan competition by room type, check policies and prices, and based on our budget, propose strategies.”

“I would design an AI Revenue Strategy Co-Pilot. Its purpose would be to connect all key commercial inputs—PMS, RMS, channel manager, OTA extranets, competitor pricing, demand data, guest reviews, market events, and marketing content—and turn that information into clear, actionable recommendations.

The biggest benefit would be that it would not only analyse data, but also help identify what action should be taken and why.

For example, the agent could alert me that a hotel is pacing behind for a specific weekend, but instead of simply saying “increase visibility” or “reduce rate,” it would assess multiple factors: competitor pricing, booking pace, events in the area, current promotions, cancellation trends, room type availability, and channel performance. It could then recommend a specific action, such as holding BAR, opening a targeted OTA promotion, adjusting minimum length of stay, or improving direct booking value rather than discounting.

It would also help with rate parity and distribution control. If it detected that Booking.com, Expedia, the hotel website, and metasearch were showing different rates or inclusions, it could flag the issue, identify the source, and recommend the correction.

Another valuable function would be content and value optimisation. As AI tools increasingly compare hotels based on perceived value, the agent could review listings and suggest improvements. For example, it may identify that competitors are clearly promoting breakfast, flexible cancellation, parking, or unique guest experiences, while our listing is vague or under-selling those benefits.

This agent would save time by reducing manual checks, improving response speed, and helping teams focus on strategy rather than administration. Most importantly, it would act as a second set of eyes—challenging assumptions, highlighting risks, and ensuring pricing, distribution, and positioning decisions are commercially sound.

In short, I would design an AI agent that combines revenue management, distribution, and commercial strategy into one intelligent assistant, because the future of hotel performance will depend on faster decisions, cleaner data, stronger positioning, and better use of technology.”

“In an ideal world, I’d design an AI agent that removes repetitive tasks and enhances decision-making. It would instantly handle reservation inquiries, meeting guest expectations for real-time responses and boosting conversion.

At the same time, it would automate monthly performance commentary—pulling data from multiple systems, identifying correlations, and generating insights—eliminating manual data gathering and enabling teams to focus on strategy.”

“If I could design one AI agent, it would be a Commercial Intelligence Agent — one that connects distribution, pricing and reputation in a single, continuous workflow.

The frustration I want to solve is practical: too much time gathering and processing data manually, not enough time actually deciding. Today, a revenue manager pulls pickup reports, checks competitor rates, reads review trends — often from different systems. By the time you have the full picture, the window to act has sometimes already passed.

My ideal agent would do that automatically. Every morning you receive the picture already processed, with clear insights and a prioritised list of the critical days that need your attention. Not just what is happening, but when you need to act.

The goal is not to replace the revenue manager. It is to give them back their most valuable resource — time — so they can focus on strategy and decisions rather than data collection. That is where the real competitive advantage is built.”