For most of this year, the news cycle has been inundated with discussions of artificial intelligence (AI). From apocalyptic-sounding warnings of sentient super intelligence to the ever-percolating anxiety of mass job replacement and a near-constant stream of AI-powered promises from companies new and old, we have seemingly bulldozed past “buzzword” territory. Today, AI seems entrenched as a central talking point, whether we love and use it or fear and avoid it.
Behind that media fervour, countless businesses have engaged in what is best described as an AI adoption arms race, sprinting towards the glossy promise of lean operational intelligence and never-before-seen levels of productivity. And who can blame them? In a vendor landscape flooded with exciting new entrants and countless providers offering the same core capabilities, innovation feels more accessible (and inevitable) than ever.
The question is no longer whether to adopt. It is how, with whom and perhaps most importantly, at what cost if you get it wrong. Every technological tipping point brings some level of fallout or recalibration. And as the adoption curve starts to resemble a feeding frenzy, some companies will find themselves at risk of making costly mistakes.
Among the list of companies publicly slighted by rapid AI adoption is Pizza Hut. The popular fast-food chain recently made headlines after one of its biggest franchisees revealed that the chain’s push to adopt an AI-driven delivery management platform turned its once high-performing delivery operation into a disaster. According to the franchisee, the software caused widespread operational problems, delivery delays, customer complaints and a collapse in sales, wiping out more than $100 million US in business and enterprise value. In this particular case, it wasn’t that the system itself was lacking or poorly designed—it was simply a poor fit for the location’s DoorDash-dependent operation and was ushered in without adequate adaptation, training or support.
These examples aren’t meant to stoke adoption anxiety, but they do serve as cautionary tales of what can happen when adoption moves too fast without the right oversight, guardrails and vendor relationship. AI that works in theory but fails in practice is still a failure—and someone has to pay for it no matter how steep the cost.
Hospitality is not a forgiving operating environment, and providing an exceptional, personalised guest experience at scale is no easy task. The right AI infrastructure can help to close that gap, but a staff member who doesn’t understand or trust the tool will undermine its value regardless of how sophisticated or well-suited it is.
Successful adoption is not guaranteed; it is a people problem. The right partner addresses this by providing training, onboarding, ongoing education and change management support. This is where enterprise providers with deep hospitality experience hold a decisive advantage, because they possess a deep understanding of the operational environment and, more importantly, the human dynamics within it.
Today, many feature sets that were once proprietary and coveted—like comparable natural language capabilities and similar property management system (PMS) integrations—are widely available at roughly comparable price points.
So, if feature disparity is no longer the differentiator, what is? What separates one vendor from the next? How do operators choose?
With the technology itself largely commoditised, the question of which AI partner to adopt shifts away from the sophistication of the underlying model and towards everything surrounding it. This includes the depth of industry experience behind the tool, the security and governance architecture supporting it, the implementation methodology and the accountability structure that stays in place long after the contract is signed.
Let me be frank. A great demo from a new entrant, no matter how compelling, cannot substitute for hard-won institutional experience in the industry it claims to serve. There is a meaningful difference between knowing an industry and understanding it. One can be acquired relatively quickly through research and hiring. The other is built over years of actually operating.
What differentiates providers now is not what their platform does. It is who stands behind it, what happens when it fails and whether their industry understanding runs deep enough to see around corners that a generalist provider simply cannot.
At some point in any AI adoption conversation—often when the chief finance officer (CFO), the legal team or whoever manages risk gets involved—someone will ask these questions: What are the guardrails? What are the security protocols? What happens when something goes wrong?
For organisations that have selected the right enterprise partner, that question has a well-documented and defensible answer. For those who chose on the basis of a feature set alone, it often does not.
When the cost of getting AI adoption wrong has proven capable of erasing nine figures in enterprise value, the vendor relationship is never purely a technology decision. It is a risk decision, a trust decision and a long-term partnership decision—and it should be treated as such.
The Pizza Hut example, for all its scale, is ultimately an exemplary story about what happens when implementation skips this step. When a system that works is applied to a context it wasn't built for, without the adaptation, training or support required to bridge that gap, the result is not a technology failure. It is a partnership failure.
Getting it right means choosing a partner accountable for the outcome, not just the delivery. One that stays in the room through the iterative, always-human process of making a new tool actually work, including the edge cases, the staff resistance, the workflow mismatches and the months that follow go-live, when most vendors have moved on. That is where the difference between a vendor and a genuine partner becomes visible. And it is, ultimately, where the return on investment (ROI) is won or lost.
Behind that media fervour, countless businesses have engaged in what is best described as an AI adoption arms race, sprinting towards the glossy promise of lean operational intelligence and never-before-seen levels of productivity. And who can blame them? In a vendor landscape flooded with exciting new entrants and countless providers offering the same core capabilities, innovation feels more accessible (and inevitable) than ever.
The question is no longer whether to adopt. It is how, with whom and perhaps most importantly, at what cost if you get it wrong. Every technological tipping point brings some level of fallout or recalibration. And as the adoption curve starts to resemble a feeding frenzy, some companies will find themselves at risk of making costly mistakes.
Where there is opportunity, there is also risk
At the end of May, Inc. published an article titled “The $500 Million AI Mistake Every Company Is Rushing to Avoid,” which detailed the viral story of an unnamed company that “accidentally” spent roughly $500 million US on AI tokens in a single month. In that same vein, Futurism published an article titled “Corporations Reeling from Huge AI Costs with No Clear Benefits,” noting that as the costs to access powerful AI tools soar, company leaders are beginning to ask some difficult questions. This particular piece cited the widely circulated comments from Uber COO Andrew Macdonald, where he admitted that AI costs were quickly outpacing gains in productivity.Among the list of companies publicly slighted by rapid AI adoption is Pizza Hut. The popular fast-food chain recently made headlines after one of its biggest franchisees revealed that the chain’s push to adopt an AI-driven delivery management platform turned its once high-performing delivery operation into a disaster. According to the franchisee, the software caused widespread operational problems, delivery delays, customer complaints and a collapse in sales, wiping out more than $100 million US in business and enterprise value. In this particular case, it wasn’t that the system itself was lacking or poorly designed—it was simply a poor fit for the location’s DoorDash-dependent operation and was ushered in without adequate adaptation, training or support.
These examples aren’t meant to stoke adoption anxiety, but they do serve as cautionary tales of what can happen when adoption moves too fast without the right oversight, guardrails and vendor relationship. AI that works in theory but fails in practice is still a failure—and someone has to pay for it no matter how steep the cost.
The adoption gap: Technology is only as good as its users
If every human on Earth were given a premium Claude subscription tomorrow and given the same task to complete, the quality of outputs would vary dramatically. Why? Because access to powerful tools does not guarantee effective use. A company could adopt an AI tool that is an objectively perfect fit for their operational needs, but it can still fail if the end users do not adopt it willingly and effectively.Hospitality is not a forgiving operating environment, and providing an exceptional, personalised guest experience at scale is no easy task. The right AI infrastructure can help to close that gap, but a staff member who doesn’t understand or trust the tool will undermine its value regardless of how sophisticated or well-suited it is.
Successful adoption is not guaranteed; it is a people problem. The right partner addresses this by providing training, onboarding, ongoing education and change management support. This is where enterprise providers with deep hospitality experience hold a decisive advantage, because they possess a deep understanding of the operational environment and, more importantly, the human dynamics within it.
What are the new competitive differentiators?
Over the last two years, the hospitality technology landscape has shifted—and by that, I mean it has flattened. Where there was once a glaring divide between legacy platforms and new, more innovative entrants, there is now a feeling of relative feature democratisation. The promise of innovation is no longer the dangling carrot it once was. Rather, it is the baseline upon which every vendor is building.Today, many feature sets that were once proprietary and coveted—like comparable natural language capabilities and similar property management system (PMS) integrations—are widely available at roughly comparable price points.
So, if feature disparity is no longer the differentiator, what is? What separates one vendor from the next? How do operators choose?
With the technology itself largely commoditised, the question of which AI partner to adopt shifts away from the sophistication of the underlying model and towards everything surrounding it. This includes the depth of industry experience behind the tool, the security and governance architecture supporting it, the implementation methodology and the accountability structure that stays in place long after the contract is signed.
Let me be frank. A great demo from a new entrant, no matter how compelling, cannot substitute for hard-won institutional experience in the industry it claims to serve. There is a meaningful difference between knowing an industry and understanding it. One can be acquired relatively quickly through research and hiring. The other is built over years of actually operating.
What differentiates providers now is not what their platform does. It is who stands behind it, what happens when it fails and whether their industry understanding runs deep enough to see around corners that a generalist provider simply cannot.
The enterprise advantage: Why scale and security are non-negotiable
As the influence of AI continues to multiply, so do the questions pertaining to security infrastructure and data privacy. There is a short list of enterprise technology providers who offer the kind of well-insulated compliance architecture, security infrastructure and track record of demonstrated performance that operators should expect from vendors. But this level of governance and institutional trust takes years and significant investment to build, and most new entrants to the AI market simply do not have the runway to build it.At some point in any AI adoption conversation—often when the chief finance officer (CFO), the legal team or whoever manages risk gets involved—someone will ask these questions: What are the guardrails? What are the security protocols? What happens when something goes wrong?
For organisations that have selected the right enterprise partner, that question has a well-documented and defensible answer. For those who chose on the basis of a feature set alone, it often does not.
When the cost of getting AI adoption wrong has proven capable of erasing nine figures in enterprise value, the vendor relationship is never purely a technology decision. It is a risk decision, a trust decision and a long-term partnership decision—and it should be treated as such.
What intelligent adoption actually looks like
Intelligent AI adoption starts with an honest assessment of where AI creates real operational value within a specific property's context. Not where a vendor's demo is most impressive, but where the actual pressure points live and where technology can meaningfully address them. It includes deployment that accounts for how a property actually operates, not how it might operate in a best-case scenario.The Pizza Hut example, for all its scale, is ultimately an exemplary story about what happens when implementation skips this step. When a system that works is applied to a context it wasn't built for, without the adaptation, training or support required to bridge that gap, the result is not a technology failure. It is a partnership failure.
Getting it right means choosing a partner accountable for the outcome, not just the delivery. One that stays in the room through the iterative, always-human process of making a new tool actually work, including the edge cases, the staff resistance, the workflow mismatches and the months that follow go-live, when most vendors have moved on. That is where the difference between a vendor and a genuine partner becomes visible. And it is, ultimately, where the return on investment (ROI) is won or lost.
Alan Young
VP, Product Management for Infor Hospitality, Infor
A seasoned business and technology visionary, Alan Young is the VP of Product Management for Infor Hospitality. He is recognized for his provocative insight into how emerging technology impacts industries and how leaders can better connect with their customers and employees for sustained growth. Alan also sits on several hotel and travel technology advisory boards and is known for his ability to turn difficult concepts into easy-to-understand ideas that drive meaningful outcomes and actions. He is a respected thought-leader, battle-tested consultant, mentor, and sought-after keynote speaker on the topics of hotel technology, business strategy, growth, transformation, and innovation. Alan focuses on increasing business awareness and believes that everything hinges on the power of communication and effective storytelling.