Artificial intelligence (AI) is a field of study focused on computer systems that can learn, reason, and make decisions in ways that mimic human intelligence, often improving over time.
What Is Artificial Intelligence?
Artificial intelligence (AI) is a field of computer science focused on building algorithms and systems that can perform tasks that normally require human intelligence, such as learning, problem-solving, pattern recognition, and decision-making.
This matters because AI can process huge amounts of data faster and more accurately than people can, then use that information to make smarter choices. For example, an AI system can analyze millions of past flight prices to predict when fares are likely to rise or fall, helping a traveler book at the right time.
In the context of travel, AI powers smarter search results, dynamic pricing, personalized trip suggestions, automated support, and intelligent expense management. It helps companies control costs, improve the traveler experience, and reduce the manual work that slows down travel and finance teams.
Understanding Artificial Intelligence in Detail
Key Components of Artificial Intelligence
AI is a broad field made up of several key building blocks:
Machine Learning (ML): Algorithms that learn from data instead of being explicitly programmed for every rule. For example, ML can predict the best flight options for a traveler based on their past trips.
Deep Learning: A type of machine learning that uses neural networks with many layers. It is ideal for complex tasks like speech recognition, image analysis, and language understanding.
Natural Language Processing (NLP): Techniques that help computers understand and generate human language. Travel chatbots, smart search bars, and automatic email parsing rely on NLP.
Computer Vision: AI that interprets images and videos. In travel and expense, this powers receipt scanning, ID verification, and automated document reading.
Recommendation Systems: Algorithms that suggest options based on patterns, such as “recommended hotels” or “best flights” for a given traveler and policy.
Optimization and Decision Engines: Systems that weigh multiple factors, price, policy, convenience, loyalty, and sustainability, to recommend the best overall option, not just the cheapest.
How AI Evolved, Especially for Travel
Early AI (rule-based systems) relied on hard-coded “if-then” logic. While functional, these systems were rigid, for instance, a basic rule might mandate the lowest-priced flight regardless of the traveler’s schedule. The advent of machine learning shifted the focus to historical data, enabling more sophisticated demand forecasting, dynamic pricing, and route optimization.
Today, deep learning and modern AI have transformed the landscape by:
Processing Natural Language: Understanding complex queries like “NYC to London next week, afternoon flights only.”
Automating Data Entry: Instantly extracting information from receipts and invoices.
Ensuring Compliance: Detecting fraud or out-of-policy behavior in near-real time.
Platforms like Navan use these capabilities to build a smooth, all-in-one travel and expense experience.
Prescriptive AI not only predicts but also recommends actions, such as:
Conversational AI
Cognitive Automation
...forecasts things like:
Price changes for flights and hotels
The likelihood of delays
Future travel demand
...not only predicts but also recommends actions, such as:
“Book now; the price is likely to increase.”
“This hotel is the best value and in-policy.”
...are chatbots and virtual assistants that:
Help travelers change trips
Answer policy questions
Handle common support issues 24/7
...automates tasks that used to need human judgment, such as:
Reading receipt
Matching expenses to trips
Flagging unusual behavior for review
Common Misconceptions About AI
“AI is always a robot that thinks like a person.”
Most AI in travel is not science fiction robots. It is software running in the background, making systems smarter and faster.
“AI replaces people.”
In practice, AI often removes repetitive work so that humans can focus on complex cases and customer care.
“AI is a black box we cannot control.”
Good systems let you define rules, policies, and guardrails. AI recommends and automates within the boundaries you set.
Why Artificial Intelligence Matters in Travel
Companies that use AI in their travel and expense programs typically see lower costs, faster processes, and a better traveler experience. Here is why:
➡️ Cost Savings and Smarter Choices
AI can scan thousands of options in seconds and find:
The best-value flights and hotels within policy
Opportunities to save without hurting traveler comfort
➡️ Time Savings for Employees and Admins
AI automation:
Fills in expense reports from receipts
Suggests itineraries based on preferences and policy
Handles routine support questions
This reduces frustration and frees teams to focus on their real work.
➡️ A Better Traveler Experience
AI-driven tools:
Personalize search results to the traveler’s usual preferences
Provide instant help through chat or in-app assistance
Predict and alert travelers to delays or disruptions
➡️ Policy Compliance Without Friction
Instead of long policy PDFs:
AI systems like Navan apply your rules in real time at booking.
They show in-policy options first and explain why something is out of policy.
They can automatically approve small exceptions based on your settings.
➡️ Stronger Data and Insights
AI helps you:
Spot trends in travel spend and behavior
Identify fraud or unusual patterns in expenses
Test “what if” scenarios, for example, what if we tighten hotel caps in this city?
How Artificial Intelligence Works in Practice for Travel
AI in Travel Booking
When a traveler searches for a trip, AI:
Understands their query, even in plain languageFilters out options that are out of policyRanks results by a mix of price, convenience, loyalty, and company rules
Personalization AI learns from past trips, including:
Preferred airlines, times, and hotel typesUsual seat choices or loyalty programs. It surfaces options that are both in-policy and likely to please the traveler.
AI can consider:
Price differences between times and datesThe impact on a traveler’s scheduleChange fees and flexibility. It recommends the best trade-off rather than blindly choosing the cheapest option.
Platforms like Navan Travel use these techniques so that travelers can book in a few clicks while still following company rules.
AI in Customer Service and Support
AI-powered assistants can:
Change or cancel tripsRebook after cancellations or delaysAnswer policy questions like, “Can I book premium economy?”
When a request is complex or urgent:
AI triages and summarizes the issue.It routes the request to the right human agent.It provides context so that the agent can help faster.
AI systems monitor flights and events, providing:
Delay and disruption alertsRebooking suggestions in the same message This reduces stress for travelers on the road.
AI in Expense Management
Using computer vision and optical character recognition (OCR):
Employees snap a photo of a receipt.AI reads the merchant, date, amount, currency, and sometimes line items.
Smart Categorization and Matching AI:
Tags expenses as “airfare,” “hotel,” “ground transport,” “meals,” etc.Matches expenses to trips, bookings, and corporate cards.Splits out taxes, tips, and fees where needed.
Detection AI checks each expense against:
Spend limitsAllowed categoriesTravel dates and destinations It flags:Out-of-policy itemsDuplicates or suspicious patterns
Tools like Navan Expense use these checks to reduce manual review and catch issues early.
AI for Travel Managers and Finance Teams
AI helps you see:
Which routes, cities, or teams are most expensiveWhere ancillaries and fees are spikingCompliance by department or region
AI can:
Forecast travel spend based on historical patterns and company plans.Show the impact of policy changes, for example, lowering hotel caps.
By analyzing bookings and behavior, AI can suggest:
Where to negotiate new supplier dealsWhen to adjust policy to better match how people actually travelWhat changes will deliver the biggest savings with the least pain
Common Challenges and Solutions
Challenge 1: The fear that AI will “take over” or remove human control.
This happens when people do not understand how AI works or what guardrails exist.
Solution:
Be clear that AI is a tool, not a decision-maker.
Set explicit rules about what AI can automatically approve versus when humans must review.
Use platforms like Navan that allow for detailed policy configuration and audit trails.
Challenge 2: Poor data quality feeding AI models.
If your input data is messy or incomplete, AI recommendations will be weaker.
Solution:
Clean up existing travel and expense data where possible.
Use integrated systems, for example, Navan’s all-in-one travel and expense platform, to reduce manual entry and data breaks.
Standardize categories and fields across tools.
Challenge 3: A lack of transparency into AI decisions.
People may not trust “black box” recommendations.
Solution:
Choose tools that explain why a result is recommended or flagged.
Provide simple explanations like, “This hotel is recommended because it is in-policy, close to your meeting, and 15 percent below the average rate.”
Challenge 4: Employee resistance to new AI-driven tools.
Travelers and approvers may be used to old processes.
Solution:
Emphasize the benefits: fewer clicks, less manual work, and faster reimbursements.
Offer short training sessions, and show side-by-side before-and-after examples.
Start with easy wins, like automated expense capture, to build trust.
Challenge 5: Security and privacy concerns.
AI systems often use sensitive data.
Solution:
Work with vendors who clearly explain their security, compliance, and data-handling practices.
Limit data access based on roles.
Regularly review permissions and logs.
Artificial Intelligence vs. Related Concepts
Aspect
Artificial Intelligence (AI)
Automation (Rules-Based)
Business Intelligence (BI)
Core Idea
Systems that learn and make decisions
Systems that follow fixed rules
Systems that report and visualize data
Learning from Data
Yes, it adjusts over time
No, it must be manually updated
Sometimes, but it is usually descriptive
Flexibility
High; it can adapt to new patterns
Low; it breaks when conditions change
Medium; it shows trends but does not act
Example in Travel
Recommending the best in-policy trip options
Sending a confirmation email after each booking
Monthly travel spend dashboards
When to use what:
Use AI, when you need:
Use Automation, when you need:
Use BI, when you need:
Personalization
Smart recommendations
Automated judgment, for example, flagging suspicious expenses
Reliable, repeatable processes that rarely change, for example, sending itinerary emails
Clear reporting and visuals for human analysis, not automatic decisions
Related Terms and Concepts
Machine Learning (ML): A subset of AI where algorithms learn patterns from data rather than being hand-coded. In travel, ML predicts prices, detects fraud, and powers recommendation engines.
Natural Language Processing (NLP): AI techniques that help computers understand and generate human language. NLP enables travel chatbots, smart search boxes (“SFO to LAX tomorrow after 3 p.m.”), and automatic support responses.
Algorithm: A set of rules or steps that a computer follows to solve a problem. AI algorithms can adjust themselves based on data, improving their performance over time.
Chatbot or Virtual Assistant: An AI-driven tool that interacts with users through text or voice. In travel, chatbots help book trips, change reservations, and answer policy questions 24/7.
Computer Vision: AI that interprets images. It is used for receipt scanning in expense tools, ID verification, and sometimes for security and passenger flow analysis in airports.
Predictive Analytics: The use of data, statistics, and AI to forecast future events, such as travel demand, likely delays, or budget needs. It helps travel managers plan more accurately.
Fraud Detection: AI-powered systems that scan transactions to spot unusual patterns suggesting misuse or fraud. In expense management, this helps flag out-of-policy behavior and potential abuse.
All-in-One Travel and Expense Platform: A system that combines travel booking, payments, and expense management in one place. When powered by AI, platforms like Navan can use data from the whole journey to automate tasks and optimize decisions.
Let AI handle the logistics while you handle the meeting. Get started.
FAQ
AI is used to:
Recommend in-policy flights and hotels
Predict and display the best-value options
Power chatbots for booking and support
Read and categorize receipts automatically
Detect unusual or out-of-policy expenses
Platforms like Navan apply AI across travel and expense workflows to reduce manual work and improve outcomes.
AI:
Personalizes search results based on preferences and past behavior.
Provides real-time alerts and rebooking suggestions when things go wrong.
Offers 24/7 help via chat or an app.
Reduces the hassle of filing expenses by automatically creating reports from transactions and receipts.
This makes travel smoother and less frustrating.
Automation follows fixed rules, for example, “send an email when a booking is confirmed.”
AI learns from data and makes decisions, for example, “based on this traveler’s history and policy, suggest these three hotel options.”
Modern platforms blend both: AI for smart decisions and automation for consistent execution.
It can be if implemented correctly. You should:
Work with vendors who use strong encryption, access controls, and compliance standards.
Make sure data is used only for intended purposes, like improving recommendations or detecting fraud.
Regularly review security documentation and access rights.
Reputable platforms design AI features with privacy and security as core requirements.
You can:
Adopt an AI-powered travel and expense platform like Navan.
Start with high-impact use cases, such as:
Smart booking recommendations
Automated receipt recognition
Policy-aware expense checks
Roll out in phases, collecting feedback and tuning rules as you go.
AI is more likely to change their work than replace them. It:
Handles repetitive tasks, like matching receipts or checking simple policy rules.
Surfaces insights and exceptions for humans to act on.
Gives managers more time to focus on strategy, supplier relationships, and traveler care.
Humans still set the goals, policies, and final decisions.
Look at:
Time saved per booking and per expense report
A reduction in out-of-policy bookings and manual approvals
A lower average trip cost after controlling for the route and trip type
Faster reimbursement times and fewer employee complaints
Good platforms provide analytics to track these improvements before and after adoption.
A travel agent is a professional who assists clients in planning and booking travel arrangements such as flights, accommodations, car rentals, tours, as well as other travel-related services. Travel agents work with individuals, businesses, and organizations to create customized travel itineraries that meet specific needs and preferences.
A travel management company (TMC) is a business travel agency that helps organizations manage their travel programs. A TMC fulfills travel bookings, supports the organization’s duty of care obligations, and identifies potential cost-saving opportunities.