AI is suddenly everywhere in managed travel. What’s missing is shared clarity—about what actually counts as AI, who owns it, and how quickly programs can adopt it without introducing new risk.
Tools are launching faster than programs can evaluate them, and travel teams are being asked to adopt capabilities they don’t fully control. The issue isn’t resistance to AI; it’s knowing where AI can meaningfully impact the program. Program managers should expect to account for effort, data readiness, and implementation.
What do we mean when we say AI?
Managed travel is already using AI in different forms, but industrywide, there’s no shared understanding of what that actually means.
“AI” is used as a catch‑all label, covering everything from rules-based automation to systems that learn, adapt, and act independently. When those distinctions aren’t clear, programs risk setting the wrong expectations, misjudging effort, and approving solutions without fully understanding the implications.
Closing the gap shouldn’t start with using the tool. It starts with clearer definitions, more relatable use cases, and a willingness to slow down long enough to ask better questions before scaling solutions.
Things to consider:
- What AI capabilities are actually available today? And, which are still emerging?
- Where does AI belong in a managed travel program?
- Who owns outcomes when AI influences or makes decisions?
- And what happens when AI gets things wrong?
Myth busting: AI and managed travel
Below are some AI myths and the reasons why they continue to hold programs back.
Myth #1:
Automation and AI are basically the same thing.
The breakdown: This is the misunderstanding underneath everything else.
Automation follows rules. Think of it like this: “If X happens, do Y.” AI interprets context, learns over time, and adapts decisions. Think of it like this: “Given what’s happening now, what’s the best action?”
Buyers need a shared language before they commit to budget, policy, or risk decisions. If a program thinks it’s adopting AI but is really adopting automation, expectations break down, success is measured incorrectly, and risks are misunderstood.
Start with definitions, not demos.
Myth #2:
More data automatically means better AI.
The breakdown: This myth assumes volume alone equals intelligence. But AI only performs as well as the quality, structure, and context of the data behind it. In managed travel, context matters as much as content. Policy rules, traveler behavior, duty of care requirements, and regional nuance all shape what “good” looks like.
More data without structure increases noise, not insight. It can also magnify errors and bias, especially in complex or high‑risk use cases. Success depends less on how much data you have and more on whether AI can understand what the data means in a travel context.
Think about data readiness before AI readiness. Otherwise, run the risk of scaling the wrong outcomes faster.
Context beats volume.
Myth #3:
Agentic AI is just a better chatbot.
The breakdown: This myth reduces agentic AI to a nicer interface. In reality, Agentic AI is defined by action, not conversation. It can make decisions, execute tasks end‑to‑end, monitor outcomes, and adapt behavior over time.
That shift matters because the moment AI acts not just responds the risk profile changes. Decisions affect bookings, traveler experience, cost, and potentially safety. Treating agentic AI like a simple chatbot underestimates what it’s doing and overestimates how lightly it can be governed.
Distinguish between AI that informs and AI that acts before deciding where it belongs in a program.
When AI acts, accountability matters.
Myth #4:
AI is a tool problem—just buy the right platform.
The breakdown: This myth assumes AI success comes from vendor selection. The reality: AI adoption depends on how the organization works, not on buying the right tool. Who owns the use case? Who governs it? Who takes responsibility when outcomes fall short?
In many organizations, travel teams are encouraged to “use AI” without clear ownership, authority, or guardrails. That creates ambiguity. Tools alone can’t resolve questions of risk tolerance, policy alignment, or escalation.
Get clarity on ownership and responsibility before tools are layered into the program.
Ownership first. Tools second.
Myth #5:
AI will replace travel agents and service teams.
The breakdown: This myth assumes AI is here to replace humans. AI is best suited for speed, scale, and routine or repeatable tasks. People are still needed for judgment, ethics, and clear, timely communication.
Recent disruptions made this clear. When demand spikes and travelers are anxious, what matters most is feeling supported and knowing someone is accountable. AI can help manage volume and flag issues quickly. People help manage through it all.
AI doesn’t eliminate the need for people. It changes where people add the most value.
AI handles scale. Humans handle judgment.
Myth #6:
Human‑in‑the‑loop means slowing AI down.
The breakdown: This myth sees human oversight as a drag on efficiency. In reality, it’s about knowing who’s accountable for managing risk.
Sure, AI can operate faster than humans can review. But that raises a critical question: how do you catch errors, bias, or incorrect assumptions before they scale? In managed travel, mistakes don’t stay theoretical. They affect travelers, budgets, and safety.
Human oversight isn’t a brake on progress. It’s what makes progress defensible.
Speed without oversight is exposure.
Myth #7:
We can worry about AI governance later.
The breakdown: This myth treats governance as something to deal with down the road. Buyers don’t see it that way. Questions about ethics, bias, disclosure, and accountability already apply—especially where AI touches duty of care, disruption response, or sensitive personal data.
One tension keeps coming up: if AI is making or shaping decisions, do travelers have a right to know? And if something goes wrong, who owns the outcome? Is it the tool, the program, or the person who approved its use?
Governance determines whether AI is trusted at all. It can’t be added on after the fact.
Governance invites trust; not the other way around.
The role of a TMC in AI adoption
Access to AI tools isn’t a challenge. It’s deciding where AI belongs in the program, how it should be governed, and how it can be used responsibly. This is where your travel management company (TMC) plays an important part.
The TMC isn’t just your partner in technology, service delivery, policy, duty of care and data. It’s also your advisor. For AI adoption, that means helping test-drive tools and then guiding implementation decisions.
From a program perspective, BCD can help:
- Identify where AI adds value and not just hype. This involves distinguishing between use cases that benefit from intelligence and learning versus those better served by rules-based automation or human judgment.
- Check readiness against interest so programs clearly understand what is required to scale AI solutions.
- Manage accountability. Clarify and define who owns outcomes across teams, tools and service.
- Address governance. Help ensure guardrails, escalation paths, and transparency are built in before issues scale across travelers, spend, or risk exposure.
- Balance artificial intelligence with human judgment. AI improves speed, consistency, and scale. Humans remain essential for exceptions, ethics, and complex decision‑making. BCD helps orchestrate that balance, so AI enhances the program without replacing accountability or human care.
The takeaway: AI in managed travel
AI is already shaping managed travel. The goal isn’t to adopt more tools, but to build in the right capabilities. This involves data readiness, governance, and clear ownership. The result should be AI that improves the program rather than adding confusion or exposure.
Interested in a deeper consultation about the benefits of AI for your program? Get in touch with our experts.