How we helped a global OTA go from stalled pilots to a sequenced AI roadmap.
Supply · Risk · Support · Finance · Ops · Dev
End-to-end, with owners and cycle-time drivers.
Build / buy / hybrid, stacked to compound.
Targeted outcome from the prioritized roadmap.
A global OTA competing with Booking and Hotels. In that market, advantage comes from three levers: price competitiveness, customer experience, and being the first offer a buyer sees.
They already knew AI mattered. They were stuck in execution. Teams had ideas and pilots. Nothing consistently made it to production.
What they needed wasn't another brainstorm. They needed a real business case — which bottlenecks were actually costing money, what the uplift of each solution would be, and how to sequence projects so the AI stack would compound instead of duplicating work.
Before we arrived, the team had tried most of the obvious things.
The consequence: critical initiatives delayed despite strategic urgency, duplicated effort across teams, and fragmented pilots that didn't compound.
Across Supply, Risk, Customer Support, Dev, Finance, and Operations. We ran interviews with leads and operators, shadowed day-to-day workflows, and captured every handoff, exception, and bottleneck in a single workflow log.
To avoid assumptions, we pulled Zendesk tickets and analyzed time-to-response, time-to-resolution, triage patterns, common categories, and where human effort was being spent on work that could be automated or assisted.
We converted the workflow log into a prioritization model using Reach (volume, frequency, % of operations touched), Impact (margin, conversion, cost-to-serve, speed), Uplift estimate per solution, Effort and dependencies, and Risk and governance needs.
Instead of a long wishlist, we consolidated everything into 6–8 AI projects designed to compound. Shared data foundations. Reusable components. A consistent agent layer instead of one-off bots. Clear sequencing so later projects become cheaper and faster.
For each project theme, we validated what should be built in-house (competitive edge, deep integration), what could be handled by third-party tools (speed, commodity features), and where hybrid made sense. We also met and evaluated external partners to pressure-test feasibility.
High-volume voice and email interactions automated and accelerated. Faster responses. Fewer handoffs. Consistent triage. Reduced support load.
Moving from straightforward bidding logic toward ML-driven algorithms that adapt to demand and competition, improve margin while staying price-competitive, and create a durable advantage via learning loops.
Improving offer quality and conversion. Enhancing listing completeness. Detecting missing elements via image and document analysis. Improving supply attractiveness at scale.
BeforeLots of pilots. Little production. Teams knew AI was important but lacked a shared business case, sequencing, and governance to ship.
AfterA quantified operational baseline including Zendesk process mining. A defensible business case tied to real bottlenecks. 6–8 stacked projects in a 12-month roadmap. Clear build/buy/hybrid decisions per project.
The net result: a roadmap designed to compound into an AI stack — targeting ~30% profitability uplift based on the cumulative improvements from the prioritized initiatives.
The roadmap we delivered isn't ours to execute. It's theirs. Leadership has the prioritization framework to reapply to every new AI opportunity that lands on their desk. Department leads have clear ownership of the projects in their queue. The vendor shortlists and build/buy calls are documented and reusable.
We designed the roadmap to be run by the internal team. Twelve months in, most of it is.
"From 40 workflows mapped to a sequenced AI roadmap designed to compound into platform advantage."
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