Hotels that treat AI as a feature to be added will likely fall behind those that treat it as an architecture to be built.
There’s a version of the AI-in-hospitality story that most people are telling. It’s about chatbots, smart concierges, and personalized booking flows. Anurag Jain, Executive Vice President for APMEA at RateGain, thinks that version misses the point almost entirely.
While that framing makes for clean product demos, Anurag argues that the real transformation is happening somewhere less visible, such as pricing engines, distribution pipelines, and the commercial architecture that most guests never see and most hotel leaders rarely discuss in the same breath.
Anurag Jain, Executive Vice President for APMEA at RateGain
The funnel is broken. The question is what replaces it
The starting point for Anurag’s worldview is a shift he describes as structural, not cyclical. Hotels have spent decades optimising for a relatively predictable booking funnel. “Travel discovery is no longer linear or confined to traditional search and booking channels,” he explains. “Increasingly, travellers are inspired, compare options, and even decide through AI assistants, social platforms, and recommendation environments that sit outside the traditional booking funnel.”
“Visibility is no longer about ranking in a single channel but about being present across multiple decision points in the journey,” says Anurag, who is quick to point out that fragmented discovery doesn’t simplify the conversion challenge. If anything, it compounds it. “Hotels now need to convert demand that originates from multiple, disconnected sources into profitable bookings.” The operational implications of that, being discoverable across a dozen touchpoints and then coherently converting across all of them, are considerable.
His read on where the industry needs to land is a shift away from how most hotel organizations still run. “The industry is shifting from isolated commercial functions such as marketing, revenue and distribution towards a unified commercial model, where success depends on connecting visibility, pricing, and conversion into a single coordinated system.” That’s a significant organizational change, not just a software one.
In his framing, “The real competitive advantage is no longer just capturing demand, but being able to recognize it early and convert it consistently across the entire journey.”
What “data readiness” actually looks like on the ground
One of the more practical threads in Anurag’s thinking is how differently the AI readiness challenge manifests depending on who you’re talking to. An independent boutique hotel and a global enterprise chain both have data problems but not the same one.
“For independent hotels, the issue is usually operational fragmentation,” he says. “Data exists, but it is often inconsistent across systems, manually updated, and not structured in a way that external platforms or AI systems can reliably interpret.” The fix there is less about technology and more about discipline; clean pricing, consistent availability, standardized formats.
For enterprise chains, the problem inverts because hey have more data than they know what to do with. “The challenge is scale-driven complexity. Different systems may describe the same commercial reality in different ways, which makes real-time decision-making difficult.”
In both cases, Anurag says the destination is the same, which is “moving from fragmented data to a clean, unified commercial foundation that can support real-time decisions across pricing, distribution, and demand channels.”
MEA: Not one market, not one maturity curve
Anurag leads RateGain’s APMEA business, which puts the Middle East and Africa squarely in his remit. It’s a region that tends to get painted with a broad brush when analysts talk about AI adoption. “AI adoption in MEA is unusually dynamic compared to more mature markets,” he says. In markets like Dubai and parts of Saudi Arabia, investment in digital infrastructure is driving fast adoption. “AI is increasingly being used to optimize revenue management, pricing strategies, and distribution performance in real time.”
But elsewhere in the region, the picture is different; not behind, just differently motivated. Hotels aren’t looking for transformation, they want specific outcomes, better visibility, more direct bookings and fewer manual interventions in rate decisions.
The segment that Anurag finds most interesting is mid-scale and lifestyle hospitality. These operators, he says, “are often more agile than large legacy chains and are adopting AI in a more modular way, starting with pricing intelligence or distribution optimization and expanding from there.” Rather than trying to implement everything at once, they pick a problem, solve it, and build from there.
As a net result, “MEA is seeing parallel adoption models, ranging from highly advanced AI-driven revenue ecosystems to very focused, outcome-driven use cases,” explains Anurag. And anyone trying to apply a single maturity framework to the region is probably getting it wrong.
The problem with everyone watching the same data
When it comes to the relationship between real-time pricing data and market-level rate integrity, the assumption most people carry is that more data and faster reactions lead to better pricing outcomes. “Not always,” says Anurag.
“When multiple hotels in the same destination are reacting to the same inputs, you can create unintended synchronisation effects.” Instead of differentiated strategies, hotels start moving in the same direction simultaneously. The market-level consequences can be significant: Anurag describes scenarios where synchronised reactions to high-demand signals lead to “rapid price escalation, reduced differentiation, and in some cases a loss of strategic pricing discipline across the destination.”
The problem isn’t the data itself but the absence of guardrails. “Without defined commercial boundaries such as pricing boundaries, brand positioning logic, or prioritisation of demand sources, real-time data can amplify volatility rather than improve decision quality,” says Anurag. In other words, more signal, without structured interpretation, can produce worse outcomes than less signal with clearer strategy.
Why fragmented tech stacks are harder to fix than they look
Ask hotel operators what’s holding back AI adoption and most will point to their tech stack.
“The biggest barrier is structural fragmentation across the commercial technology ecosystem,” says Anurag. Most hotels run across PMS, CRS, RMS, channel managers, and a collection of distribution and marketing platforms that weren’t designed to work together. The result is duplicated, inconsistent, or delayed data flowing through the organization. “AI depends on having a consistent and real-time view of commercial reality.” When the underlying data is fragmented, AI doesn’t fix it, it inherits the problem.
The second barrier is organisational rather than technical. Even where the technology exists, marketing, revenue, and distribution teams often operate in separate silos with different KPIs and different decision cycles. Anurag is confident that “the challenge is not just adopting AI tools, it is building a bridge between systems, data, and teams so that decisions can be made and executed in a unified way.”
“AI only delivers value when it operates on clean, aligned inputs.”
The shift that’s actually required
Anurag’s broader argument is that the hospitality industry is underestimating how much change AI actually requires. “AI is not a layer you add on top of existing systems; it requires a shift in how data is structured, how decisions are made, and how execution is coordinated across the organization,” he says. That’s not a technology procurement conversation, it’s a business transformation one.
“The AI guest is reshaping how travel decisions are made, from inspiration and search to comparison and booking,” he notes. Hotels that want to stay relevant in that environment need to show up not just in traditional search, but across the distributed, AI-mediated touchpoints where traveller decisions increasingly take shape.
What Anurag describes as the endpoint is a commercial model where the functions that have historically operated in isolation, such as marketing, revenue management, distribution, are aligned around a shared, real-time view of demand. “In an AI-first travel environment, success will depend not just on visibility across the traveller journey, but on the ability to convert that visibility into coordinated, profitable action across pricing, distribution, and conversion at scale.”
