We have been promised that AI will "transform" operations overnight. Well, Al is definitely leading to a transformation, I'm just not sure about "overnight". The reality revealed by h2c's 2025 Global AI & Automation Study is far more pragmatic. Most hotel groups still confuse automation and AI, and this confusion is slowing down returns, blocking adoption, and delaying efficiency gains that could be achieved today.
To make Al a trusted and worthy partner, it is important to understand one fact: True efficiency doesn't start with intelligence. It starts with automation.
The Industry Problem: Hotels Trust AI More Than They Use It
The h2c study highlights a striking 1.9-point trust-reliance gap: hotels score Al at 6.6/10 in trust, but only 4.7/10 in actual reliance.
This gap isn't due to a lack of interest. It is due to a lack of clarity:
• 18% of hotel chains are ready to use some form of AI, mostly chatbots
• But 70% say integration challenges block adoption
• 62% cite lack of Al expertise-the #1 barrier, twice as high as concerns around staff resistance
• And 42% don't track ROI at all for AI initiatives
This paints a simple picture: hotels want the benefits of automation and AI, but don't have the operational foundation required for intelligent systems to work reliably.
And that foundation is automation.
Automation = AI: The Difference Hotels Must Understand
Workflow Automation
It is structured, rules-based, predictable, and repeatable. It works with your systems as they are.
Examples from active RobosizeME deployments include:
• OTA payment posting
• Travel partner billing
• Profile quality checks
• Daily cashier adjustment
These are high-volume, Low-margin tasks that hotels perform daily. Yet automation executes them with 99%+ reliability. Automation does not "think". It simply does, every time.
AI (LLMS, Agents, Predictive Systems)
On the other hand, Al interprets data, generates text, predicts outcomes, or recommends actions. AI is powerful, but it is:
• Prone to hallucination (producing incorrect information confidently)
• Shaped by cognitive bias in the data it's trained on
• Dependent on data quality, which hotels overwhelmingly struggle with (41% cite data quality/accessibility issues)
Let's look at a few definitions.
LLM (Large Language Model)
A machine-learning model trained on massive text datasets to predict the next word in a sequence. Great for conversation and content, but not for executing financial workflows or precise operational tasks.
Hallucination
When an AI confidently produces inaccurate or fabricated output because it lacks the right data or the task exceeds its reasoning boundaries.
Cognitive Bias
Al reflects the biases present in its training data, resulting in skewed decisions or unbalanced recommendations.
(You can learn more about LLMs here: https://www.digitalnative.tech/p/lIms-for-dummies)
When hotel teams misunderstand the differences between automation and AI, they expect AI to perform the tasks of automation, and vice versa. That's where failures begin.
The Data Is Clear: Automation Delivers Today, AI Enhances Tomorrow
According to the h2c study:
- 69% of hotel groups want technology to reduce repetitive tasks — automation's core strength
- 63% want automated reporting in revenue management - one of the most common RobosizeME use cases
- 70% cite "easy integration" as the top investment criterion - automation works even with legacy PMS and fragmented stacks
- Meanwhile, only 11% use AI Agents today, and only 1% say AI is central to their business model
This underscores a critical truth: Hotels cannot scale AI until their Processes are automated, table, and measurable.
For Al cannot clean your data if automation is not collecting it consistently. Al cannot forecast accurately if the underlying reports are manually stitched. And Al cannot personalize guest journeys if half your customer profiles are duplicates.
Automation then first lays the foundation.
Actionable Framework for Hotel Leaders: The Automation-First Roadmap
1. Start with low-complexity, high-repetition tasks
These deliver immediate ROI and build organizational confidence. This is confirmed by industry leaders: simple, repetitive tasks create the fastest adoption wins.
2. Track operational KPIs long before deploying AI
Automation inherently provides measurable outputs
• Success rate
• Hours saved
• Processing time
• Error reduction
Without these baselines, AI ROI can't be measured.
3. Use automation to fix data before using AI to interpret it
Given that 41% of chains face data quality issues, automation becomes the necessary cleaning and enrichment layer.
4. Introduce AI only where reasoning, and not repetition, is required
Examples where Al adds, not replaces, value:
• Demand forecasting
• Content generation
• Email triage
• Personalized messaging
• Guest sentiment analysis
The Conclusion Hotel Leaders Need to Hear
Hotels don't have an AI problem. They have an automation gap.
The h2c research confirms that trust in AI is rising, but operational readiness is not. The fastest path to efficiency, guest satisfaction, and scalable innovation is:
• Automate workflows first.
• Improve data quality.
• Then layer AI where it can truly enhance decisions.
At RobosizeME, we see this every day across thousands workflow automations running live. Once the basics are automated with 99% reliability, Al becomes not only safe to deploy, but exponentially more valuable.
If hotel groups want to prepare for the next decade of hospitality technology, this is the right sequence.
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