Consultancy, AI visibility, fractional CMO
When a Fortune 500 hospitality group needed a senior view on AI, its marketing directors came to the people already teaching it.
Several regional marketing directors of one of the world’s largest hospitality groups had heard about our sessions on AI and marketing before they ever contacted us. When the same question kept surfacing in their own leadership meetings, they asked for that thinking at their scale: a senior partner who could explain what AI changes for hotel marketing, what it does to how travellers find hotels, and what to do about both. They came back the following year. In advisory work, that is the result that matters.
Challenge
A dozen AI pitches, no shared picture, and a search landscape shifting under the whole portfolio.
By 2024 every marketing leader at the group had sat through vendor demonstrations promising AI-generated content, AI-driven personalisation and AI-optimised campaigns. What none of them had was a vendor-neutral understanding of what any of it meant for the work they were accountable for: how hundreds of properties are marketed locally under central brand standards, how content and campaigns flow between headquarters and regional teams, and how performance is reported upwards.
Underneath that sat a second, more urgent problem. Travellers were starting their search in AI answers and in Google’s AI features, and the directors had no way of knowing whether their properties appeared in those answers at all, let alone why. For a group whose economics depend on the balance between direct bookings and OTA commissions, a new layer of intermediation between the guest and the hotel was not an abstract concern.
The request was not for a tool or a vendor. It was for understanding and direction: someone who had worked at enterprise scale, spoke the language of hotel marketing, and would say plainly what was worth doing, what was premature, and what was marketing theatre.
Reputation travelled before the brief did.
The directors had heard about the sessions we were running on AI and marketing, and asked whether the same approach would work for them. We agreed on one condition: the sessions would be built on their real properties, data and decisions rather than on generic examples. Advisory that cannot be acted on the next morning is not advisory.
Approach
Seasons, not slide decks. Decisions, not inspiration.
We structured the engagement as a season: a sequence of working sessions for the directors and their teams, each built around their own properties, campaigns and data. Every session had to end with a decision the directors could take back to their regions. The format deliberately mirrored how a fractional CMO works with a leadership team: regular, senior, hands-on, and accountable for outcomes rather than hours.
Understanding before tooling
The first sessions built a shared, honest picture of what current AI systems can and cannot do for marketing: where output quality is already good enough for a global brand, where it is not, and how to tell the difference without a vendor in the room.
Mapping the marketing flows
With the directors we mapped how campaigns, content and reporting actually move between central and local teams, then marked each step: where AI removes real friction, where it adds risk to brand or compliance, and where the right answer was simply a better process.
AI visibility as a discipline
We treated visibility in AI answers as a marketing channel to be measured and managed, not a curiosity. The directors learned how AI engines select and describe hotels, what signals they rely on, and how that intersects with the SEO, reviews and data their teams already produce.
Governance and brand safety
A global brand cannot let every property improvise with AI. We helped define the guardrails: where human review is non-negotiable, how brand voice is protected across languages, how to pilot before scaling, and what the reporting line for AI-assisted work should be.
Decision frameworks the teams can run themselves
Each topic ended in a reusable framework: how to evaluate an AI vendor, how to decide what to automate, how to brief AI-assisted content so it stays on brand. The goal was independence, not dependence on us.
The return: from understanding to practice
When the group came back the following year, the work moved from concepts to application: live cases from the directors’ own markets, the first season’s frameworks pressure-tested against them, and routines their teams now use.
AI visibility
From “do we appear in AI answers?” to a channel the directors can see and influence.
The directors’ most pressing question was the one nobody could answer: when a traveller asks an AI assistant where to stay, are we in the answer? We made it answerable. The sessions worked through how AI engines decide which hotels to recommend: the weight they give to consistent, unambiguous information about a property, to third-party authority such as reviews and press, to structured data, and to content that answers real traveller questions directly.
From there the work became practical. Which properties and markets to baseline first. How to audit what AI engines currently say about a hotel and where that description is wrong or outdated. Which fixes sit with central teams (data, structured markup, brand information) and which sit locally (reviews, local content, partnerships). And how to measure movement over time, so AI visibility becomes a line in the regional report rather than an anecdote.
The through-line for the directors was reassuring and demanding at once: the foundations that win AI recommendations are the same ones that win Google rankings and direct bookings. There is no separate trick. There is a higher bar for clarity, authority and data discipline, applied to the whole portfolio.
Fractional CMO
Senior marketing leadership on a schedule, not on the payroll.
Between sessions the engagement behaved like a fractional CMO function for the directors: a senior partner available for the decisions that did not fit a workshop agenda. Evaluating a vendor pitch before a contract was signed. Reviewing how an agency proposed to use AI in a campaign. Deciding which pilot deserved budget and which should be stopped. Preparing the directors to explain AI decisions upwards, to leadership that would hold them accountable for results.
This is the model our consultancy is built on: strategic direction and an experienced second opinion for marketing leaders who have the authority to act but not the time, or the appetite, to become AI specialists themselves. At Fortune 500 scale the stakes are higher and the governance heavier, but the need is the same one we see in mid-market brands and independent hotels, and the frameworks transfer in both directions.
Results
They came back. In advisory, nothing says more.
Consultancy does not produce a ranking chart. The honest measure of whether it worked is what the client does afterwards, and the group’s marketing directors asked us back for a second season the following year. Along the way they gained a shared vocabulary for AI across regions, a mapped view of their own marketing flows with opportunities and risks marked, an AI-visibility baseline and method for the markets that matter most, and decision frameworks their teams now apply without us in the room.
Trajectory
From understanding to practice.
First engagement: understanding
A shared picture of AI’s real capabilities, the marketing-flow map, the first governance guardrails, and the first honest look at how AI engines described the group’s properties.
The return: practice
Live cases from the directors’ markets, AI-visibility baselines and audits, vendor and agency reviews, and the frameworks turned into working routines. The frameworks are the group’s to keep and run, whatever comes next.
What this means for marketing leaders
Four takeaways from advising at Fortune 500 scale.
Understanding is the bottleneck, not technology
The tools exist. What large organisations lack is a clear, shared understanding of what to use them for. Build that first and the tooling decisions become easy.
Map the flow before you automate it
AI applied to a messy process produces a faster mess. Mapping how marketing actually moves between teams is the most valuable work, and the least glamorous.
AI visibility is a channel, not a curiosity
If you cannot see whether AI engines recommend you, you cannot manage it. Baseline it, fix the data and authority gaps, and report on it like any other channel.
Senior judgement scales better than headcount
The directors did not need an AI department. They needed a senior partner on a schedule, and frameworks their existing teams could run.
Want this kind of thinking inside your organisation?
We work with a small number of groups and brands on senior advisory and fractional CMO engagements. If your leadership team needs clarity on AI, AI visibility and marketing, we should talk.
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