What is Artificial Intelligence (AI)?


Also known as	AI, machine intelligence, smart automation
Category	Technology, automation, data science
Common in	Travel booking, customer service, pricing, fraud detection, expense management

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.

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Types of AI Relevant to Travel

Predictive AI	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

Predictive AI

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:
Personalization AI learns from past trips, including:
AI can consider:

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:
When a request is complex or urgent:
AI systems monitor flights and events, providing:

AI in Expense Management

Using computer vision and optical character recognition (OCR):
Smart Categorization and Matching AI:
Detection AI checks each expense against:

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:
AI can:
By analyzing bookings and behavior, AI can suggest:

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 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 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 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 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.

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.

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

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

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.