At SiteMinder, we wholly believe in the potential of AI to transform and drive our industry forward. But we know that, for some, AI-fatigue is real. If we all had a dollar for every LinkedIn post unveiling the ‘truth’ about AI, or every thought leader dropping a pearl of wisdom on the changes AI will bring to any given status quo, or even every doomsayer lamenting the loss of creativity and critical thinking, well, we probably wouldn’t be pondering AI at all. Love it or loathe it however, AI is shaping the future as we speak and only time will tell if it really does live up to the potential so many hope it will.
SiteMinder has its own part to play. Not only in the conversation, but in the tangible maturation of that very future. To combat the saturation you might be seeing, we thought we should explore the topic from another angle, one that is accessible only to us: our internal teams and processes.
To start, let’s make one thing very clear. Using AI for anything and everything, simply for the sake of it, is not even a bold strategy, just a silly one. The most impressive feats wrought by AI will be realised when it is used as the tip of a spear hefted by those who possess big, decidedly fleshy, brains. Human minds which have a purpose, customer needs close at heart, and an understanding of why and how AI can help.
Now that we are grounded in this philosophy, we can discuss exactly how our people at SiteMinder are optimising their use of AI on a daily basis, to solve both workflow bottlenecks and the challenges faced by our hotel customers now and in the future.
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The best AI outcomes don’t explode, they accumulate
At SiteMinder, we absolutely intend to be the platform that delivers the best AI-led experiences and capabilities for hoteliers around the world, but we aren’t chasing a magic bullet and we aren’t forgoing the rigorous standards that made us the industry’s leading hotel technology provider in the first place. There has been a key focus on improving internal processes, which in turn will enhance the solutions that our customers see and use everyday.
Our teams have learned that AI’s real power is not held within one ‘game-changing’ feature. Instead, it shines when minor capabilities work together to create a compounding effect over time. This might include a skill that helps an engineer connect and analyse a database, a script that searches for and reveals bugs, or test scenarios that are generated in minutes rather than written by hand at the cost of hours. While not beautiful enough for a press release, these processes reshape how work gets done, ultimately ensuring that the headline features are delivered quicker and easier.
It’s important to note that sustainable AI acceleration is not just about making one person code faster. It comes from improving the entire span of a project, from ideation and testing to deployment, security, and performance, so that progress is not constricted at any point in the pipeline.
Speed to market doesn’t mean shortcuts are being taken though, in fact it’s the opposite. More efficient workflows enable the time required for the team to treat AI solutions exactly the same way as any piece of software we have shipped in the past: wrapped in comprehensive tests and verification confines that guarantee quality in the end product.
This gives the team more confidence to roll out newer, sharper models without the fear that something will break or be undercooked.
Underlying all of this is a refusal to compromise on the need for a human to stay in the loop. As we said, AI is just the tip of the spear. Code reviews for AI-assisted work remain mandatory and human-led. Test scenarios generated by AI are reviewed by people before they are ever embedded in the build pipeline. This is not because we want to hold onto legacy ways of working or enjoy a nostalgia trip. It’s because we recognise that human judgement is still crucial to the development journey.
The next step is to move from AI pair-programming toward zero hand-written code. But even then, our principle will hold. Machine drafts will always require a human sign-off. The reason this really matters is because the hardest problems in software are solved by understanding customer problems, building the right solution, and measuring the impact properly. In that sense, AI cannot replace the knowledge of experienced engineers. It is an accelerator for teams who already know what the end result should look like.
Hotel data training reveals that no two hotels are the same
In most cases, training a model on a large dataset will uncover consistent patterns that unearth stable conclusions. For example, the correct way to price a room or proven methods to hit occupancy rate milestones.
At SiteMinder, we found something more interesting and perhaps more useful. There is no single way to run a hotel, since there is so much nuance in each property’s individual structure, guest profile, revenue strategies, and competitive set to name just a few. A boutique business in a seasonal coastal town is not solving the same problem as a corporate-heavy hotel next to a hospital or an operational worksite such as a mine. Any model that tries to use a one-size-fits-all approach will help no one.
AI is showing us its value by making it easier to pick up all the variables that exist, and using these signals to inform pricing or market intelligence solutions that will adapt to hotels on a personal level. For the hotelier, this will be the difference between a generic rate suggestion and a pricing recommendation that already understands the shoulder season, the comp set, and the guest who books 90 days out, so there won’t be any need for second-guessing or gut reactions.
It also helps that SiteMinder’s platform generates 135 million reservations a year, more than 245 hotel bookings every minute, making it home to the most comprehensive hotel booking dataset in the world. When our models learn, they learn deeply from global hotel demand rather than a negligible sample of it.
AI empowers knowledge sharing and fuels innovation
Knowledge sharing is part of the human condition – it’s how we learn, grow, and create better outcomes. AI is making that much easier for internal teams at SiteMinder. In the past, valuable knowledge sat trapped in siloed product and engineering teams, scattered across different communication threads, buried in resource pages, or tied up in job and support tickets. Now that AI is embedded within the business, all these nuggets of gold can be mined in seconds. It’s infinitely quicker to get everyone on the same page and also much easier to enable each other to do better, more groundbreaking work.
As for what’s already having an impact on the front end of the pipeline, QA automation is one of the biggest wins our teams have encountered with AI. Developers can now have AI generate test scenarios from rich context, hand those scenarios to humans for review, then run them automatically on every build. Alongside this collaboration sit dozens of internal AI skills that are woven into daily tools and processes, significantly reducing fragmentation and delays. This is seen in customer-facing operations too, where high-volume support journeys can be triaged and handled with voice AI, for example.
There are two milestones that stand out in particular. AI Summaries for Insights users went live as the first LLM-based solution running on Amazon Bedrock, a moment that is both a platform achievement and a feature real customers can benefit from today. Additionally, Demand Forecasting arrived as the first in-house machine learning model SiteMinder has designed and deployed. The success of these launches is aided by AI taking care of the boilerplate-heavy delivery, the test generation, and the migrations, so engineers have the freedom to spend their time on the logic that genuinely requires a human mind.
This progress is bound and supported behind the scenes by a deliberate framework of constructive AI adoption. It means giving teams sanctioned tools, embedding AI within company-managed infrastructure, and ensuring new use cases are reviewed through the right legal, security, and operational lenses.
SiteMinder is building a future where AI is an actor, not just an advisor
SiteMinder in the AI era is poised in a position of strength. Our platform sits at the very centre of the hospitality ecosystem. This central role is exactly what allows us to unlock and expose the core functionalities that agentic workflows will need, creating a foundation that any provider or partner can eventually tap into.
The trajectory for the industry is already visible. Plain-language summaries of complex signals exist now, as do tailored pricing suggestions. Inventory and distribution recommendations are the near horizon. Just beyond them sits the next, transformative, step: agentic actions taken across rate plans and inventory workflows. AI that performs the smartest action, rather than merely suggesting it.
None of this, however, spells the end of the hotelier’s judgement. For operators who always have something to think about, AI is another tool in the belt, one that accelerates the work and lifts away the manual day-to-day rather than the thinking itself.
The question facing hoteliers is no longer whether their business will operate alongside AI. It is whether their chosen technology platform is positioned to make the transition a seamless and successful one. That’s what SiteMinder’s name will be synonymous with.
