AI in Business Ground Travel: HQ CTO’s Playbook

AI is everywhere. And that’s not always a good thing.

Many workflows benefit from artificial intelligence, but not every single one. In fact, using AI just to check a box can create more friction than it solves. When applied in the right context, however, AI has the power to be transformative, reduce complexity, improve compliance, and save time at scale.

That’s why we built REN, HQ’s AI agent for business ground travel. It was built to solve real problems that travel managers, executive assistants, and operations teams face every day. 

As CTO, I’m faced daily with the challenge of guiding HQ’s AI efforts toward areas where they truly add value.

In this article, I want to share three areas where AI has real, practical impact, and just as importantly, highlight where AI’s capabilities aren’t as valuable.

1. Intelligent Interfaces: Making Complexity Invisible

One of the most exciting and rapidly evolving uses of AI is as a conversational interface. Thanks to large language models, users can now describe what they want in their own words, no forms, no dropdowns. For example:

“Please book a ride for John Smith tomorrow at 6 a.m.”

This shift toward natural language has several benefits:

• Easier learning curve: Users don’t need to learn a system’s quirks.
• Flexible flow: Users can start with any detail, such as the location, the passenger, or the destination.
• Voice-friendly: Natural voice interaction becomes possible, essential for users on the go.

This is not just a matter of convenience; it’s about reducing friction, minimizing errors, and empowering employees to focus on their core work rather than wrestling with logistics. But there are limits. Chat interfaces shine when:

• The user input is relatively quick and easy
• There’s high context already available
• Multiple paths and flexible workflows are possible

Updating an existing ride? Perfect fit. 

The user might want to change the time, the pickup, or the vehicle type, something like “Move John’s ride to 2 p.m. and change to a black car” is faster and more intuitive than navigating a form. A straightforward ride booking request is also a great fit. But, it becomes more complicated when the ride booker would need, for example, to add multiple specific features, customizations and preferences to their booking. 

Let’s look at another scenario. Say you were booking for a group which required adding many additional emails to the cc list of ride updates. The conversation with AI would become cumbersome, and would not be serving the user best. In this case, bookers would be better served by a well-designed UI that lists all options in a structured format.

And there’s another important nuance: what we call the “blank page problem.” If a user doesn’t know exactly what they want, a conversational AI may not be the best place to start. It’s hard to look at a blank screen and decide what to ask for, especially when planning a trip with many moving parts. It’s too open-ended and can overwhelm the user, causing more friction. In this case, a good UI can give helpful context and present all available options, like a visible itinerary, that sparks decisions. A visual interface can help users make better, faster choices when they’re still figuring out their intent.

2. The AI Agent: Augmenting Human Capacity 

The real power of AI agents lies in their ability to work within defined boundaries while executing multiple steps with context and precision. Unlike traditional systems, where every possible scenario must be envisioned and hard-coded in advance, agentic AI can interpret intent, react to input, and handle dynamic workflows without needing to foresee every outcome.

This flexibility comes from a balance: giving the AI room to act, but within clearly defined parameters. That allows the system to function autonomously when possible, while still maintaining oversight and safety. These bounded systems are what make agentic flows both scalable and trustworthy.

The best use cases for agentic flows are:

• Well-defined processes with dynamic paths
• A task with structured, small toolset
• Concrete results that are easy to validate 

An agent might not be ready to directly respond to customers or make open-ended financial decisions without supervision. But it’s excellent at tasks like reconciling data, analyzing ride details, or preparing reports, areas where the process is known, clear, and easy to validate.

For example, REN is designed not just to automate, but to act as a true assistant, handling routine tasks like scheduling, compliance checks, and real-time itinerary adjustments.

Say a travel manager wants to know, “Which car provider did we spend the most with year to date? Compare that to last year.” Answering might require the system to:

• Pull multiple reports
• Analyze and filter data
• Cross-reference historical records

Instead of requiring the manager to do all that manually, REN can take the request, execute the steps asynchronously, and return an answer complete with links to the relevant data reports. That means:

• Faster execution of time-consuming actions
• Less context switching for the user
• Lower system complexity –  The user doesn’t need to know exactly what they’re looking for, just the outcome they want to achieve.

However, today’s agents can still be fragile. Without proper guardrails, they can misinterpret instructions or surface irrelevant data. Some cases where this flow would not be the best fit:

• Open ended process, without a clear goal
• Sensitive tasks – these would benefit from additional human oversight 
• Results which are hard to validate

A counter-example would be ‘find anomalies in the data’ without clear directions or guidelines. This would be an open ended request without a clear goal, and any results would be hard to validate and verify.

3. Attention to Detail: Automating Diligence 

AI shines at repetitive tasks that require attention to detail and diligence but not deep creativity or judgment. One great example: invoice auditing. Verifying compliance with travel policy, spotting mismatches in billed miles, or identifying loosely predefined anomalies across thousands of line items is tedious and error-prone for humans, but ideal for AI.

REN leverages real-time data to:

• Predict and avoid disruptions on an ongoing basis (e.g., traffic, weather, local events)
• Invoice auditing 
• Data reconciliation and normalization (e.g., “Dave Smith” is listed in the invoice but, “David E. Smith” is listed in the booking.)

• Consistency across massive datasets

So, let’s take those examples a step further to look at where AI is best used.

• Tedious tasks that require attention to details at scale

• Within a clear boundary that makes clear expected outputs 

• A task that requires primary skill of diligence, not exceptional amounts of intelligence   

But don’t expect miracles. Tasks that are unclear or open ended, like adjudicating complex policy disputes, or any problem that requires human judgment and sensitivity.

The Boundaries: Where Human Judgment Remains Essential

Despite its strengths, AI is not a panacea. Our experience with REN has highlighted several boundaries:

• Exception Handling: AI struggles with novel or ambiguous situations, such as unexpected traveler needs or emergencies, where human empathy and judgment are irreplaceable.
• Complex Trade-offs: AI can optimize for efficiency or cost, but only humans can weigh those against less tangible factors like traveler well-being or company culture.
• Trust and Transparency: Users must understand how AI makes decisions, especially around compliance, privacy, and safety. Over-automation without transparency risks eroding trust.

Lessons Learned: Best Practices for AI in Business Travel

Building REN has raised interesting discussions about the do’s and don’ts of AI deployment. One key takeaway is that where AI operates in the stack, backend vs. user-facing, dramatically affects how it should be built and deployed.

When AI is working behind the scenes, helping process data, enforce policy, or automate internal workflows, success is often a matter of using the right tools: model choice, context engineering, and finetuning. It’s easier to validate, easier to control, and a good fit for full automation.

But when AI is front-end facing, where users can see, interact with, or rely on its output, a second layer of considerations becomes essential:

• Can users understand and trust the results?
• Do they need to validate or adjust the response?
• Are guardrails in place to avoid missteps?

In those cases, the distinction becomes clear: does the system need a human in the loop or not? The most successful deployments don’t try to eliminate human oversight, they build for it intentionally.

A few core principles have guided our approach:

• Start with Real Problems: The most successful features were grounded in user feedback and real operational pain points.
• Iterate Relentlessly: AI based tools improve over time, but only with continuous feedback. 
• Balance Automation with Human Oversight: The best outcomes come from combining automation with smart system design. 

The Road Ahead: AI as a Strategic Partner

AI has the potential to transform business ground travel, when applied with purpose. Systems like REN are most powerful when they simplify, not complicate; when they enhance human decisions, not replace them.

The future of AI in travel isn’t about replacing people. It’s about empowering them. With thoughtful design, clear boundaries, and a relentless focus on solving real problems, AI can become a trusted partner in delivering smarter, safer, more efficient travel.