The AI Conversation Hotels Are Having Is the Wrong One
Executives from Davidson, Remington, and CoralTree said it clearly: AI won't fix bad data. Most hotels still haven't fixed the data problem.
No trends pieces. No generic takes. Just specific, useful observations from real portfolio work.
These are the cornerstone articles — substantial, reference-grade pieces that explain the fundamentals. If you're new here, start with one of these.
What flow-through actually measures, when to use flex instead, and why a 200% number means something went wrong — not right.
Read → IntegrationsThe implementation team said it was mapped correctly. Six months later, resort fees are landing in the wrong bucket. Here's exactly why.
Read → Hotel FinanceNot a definition list. A practical breakdown of what each number is actually telling you, and what to do when it's off.
Read →Executives from Davidson, Remington, and CoralTree said it clearly: AI won't fix bad data. Most hotels still haven't fixed the data problem.
The goal was never the dashboard. It was for the team to trust the numbers. Those are two completely different problems.
Most labor reports tell you what happened. The useful ones tell you why — and what you can still fix before the month closes.
A dashboard nobody looks at is just a report in a nicer frame. Here's how hotel finance teams end up building the wrong thing — and how to avoid it.
Acquisitions and brand conversions are messy. The financial risk isn't usually in the deal — it's in the first 90 days of operations after you close.
When F&B revenue lands in the wrong bucket, everyone's numbers are wrong — RevPAR looks better than it is, and outlet profitability disappears into noise.
A short, repeatable framework for catching labor problems while there's still time to do something about them. Works whether you're at one property or thirty.
The GMs who run the tightest hotels tend to have one thing in common: they don't wait for the report to arrive. They already know what's in it.
ProfitSword is genuinely useful. It's also routinely blamed for decisions that were made wrong before the data ever hit the screen.
The blended rate works until it doesn't. A look at why PTEB estimates go sideways and what to track instead.
Not a pitch. A practical look at where AI tools make a dent in monthly close work — and where they don't.