Practical Ways to Use AI in Hospitality Commercial Teams (Without Boiling the Ocean)
AI has become one of those words that shows up in every board deck and conference presentation. In hospitality, I see a lot of excitement, a fair amount of fear, and not enough practical application.
The reality is simple: you don’t need a huge transformation programme to start getting value from AI. You do need clarity on where AI can actually help your commercial team do better work, faster.
Here are a few pragmatic places to start.
1. Faster, better content – with guardrails
Commercial teams spend an enormous amount of time producing variations of the same content:
Offer descriptions
Email campaigns
Social posts
Partner marketing copy
AI tools can help generate first drafts in seconds, but they’re not a replacement for human judgment. The pattern that works is:
Create brand and tone guidelines (examples of “on-brand” and “off-brand”).
Build a small library of prompts your team can reuse.
Have a clear review process so nothing goes out unedited.
The outcome: more testing, faster campaigns, without sacrificing quality.
2. Smarter segmentation and simple personalisation
Many hotels and platforms already have more data than they use. AI can help uncover patterns:
Which segments respond to which offers
Seasonal behaviour by geography or channel
Guests who are likely to return vs one-time visitors
Even if you’re not running sophisticated machine learning models, you can use AI to:
Explore hypotheses on your existing data
Generate segment-specific messaging
Create simple rules-based journeys (e.g. “local residents who visited in the last 90 days get X”).
You’re not trying to be a Silicon Valley tech company. You’re trying to move the dial by being 20–30% more relevant to the right people.
3. Decision support for pricing and forecasting
Revenue management systems already use algorithms, but AI can add value in:
Scenario planning: “What happens if we adjust rates by X% for this segment?”
Explaining patterns: “Why did this month underperform last year?”
Surfacing anomalies: “Flag any source markets or channels with sudden changes.”
The key is to position AI as decision support, not as an automatic pilot that overrides your revenue team.
4. Reducing manual reporting
One of the quickest wins I see is using AI to reduce the friction in reporting:
Turning raw data and dashboards into summaries and narratives
Drafting weekly commercial updates from a set of numbers
Helping teams prepare for commercial meetings faster
If your team can spend less time preparing slides and more time deciding what to do, you’ll feel the impact quickly.
5. Change management matters more than the tools
The tool landscape will keep evolving. What doesn’t change is the need to:
Be clear on where AI fits in your workflows
Train people properly and address understandable concerns
Set rules for what’s acceptable (especially around data privacy and guest experience)
When I work with clients on AI in commercial workflows, I start with existing processes and decisions, not with technology. The question is always: “Where are you currently slow, manual or blind – and can AI help remove that friction?”
Get those basics right, and you’ll see real productivity and performance gains, without big capex or a multi-year transformation project.
