AI, Data & Automation in Corporate Travel

Software that can interpret a goal, make decisions, use tools and take steps toward completing a task, such as rebooking, checking policy or resolving an invoice exception.

Technology that enables systems to perform tasks normally requiring human intelligence, such as classification, prediction, language understanding or decision support.

Policies, controls and processes that guide how AI systems are developed, monitored, audited and used responsibly.

Coordination of multiple AI models, systems, rules and workflows to complete complex business processes across travel, payment, invoice or expense systems.

Set of computational rules used to process data and produce an output, such as a ranking, prediction, recommendation or classification.

Technical connection that allows systems such as booking tools, payment platforms, expense systems or AI services to exchange data.

Use of technology to complete repetitive tasks with limited or no manual intervention, such as invoice matching, policy checks or payment reconciliation.

Workflow where a system can complete multi-step tasks independently within defined guardrails, such as validating a hotel invoice and routing only exceptions to AP.

Automated software interaction layer, often used for basic support, notifications, FAQs or transactional tasks.

AI model that assigns data to categories, such as compliant vs. non-compliant booking, valid vs. invalid invoice, or hotel vs. meal expense.

AI technique that interprets images or visual documents, relevant for reading receipts, invoices, passports or scanned folios.

AI that enables users to interact with systems through natural language, such as a travel assistant, chatbot or service automation interface.

Central repository that stores large volumes of structured and unstructured data for analytics, AI and reporting.

Structured representation of how data entities relate to each other, such as traveler, booking, payment, invoice, supplier and cost center.

Automated flow that collects, transforms and delivers data from one system to another for reporting, automation or AI use cases.

System that applies rules, data and sometimes AI to determine the next action, such as approving, rejecting, flagging or routing a transaction.

Process of identifying when different records refer to the same entity, such as the same hotel appearing under different names across systems.

Process for routing cases that cannot be automatically resolved, such as invoice mismatches, policy exceptions or failed payments.

AI approach that makes model outputs understandable to users, auditors or business owners.

AI that creates new content or responses, such as summaries, recommendations, emails, service replies or meeting briefs.

Rules and controls that limit what an AI system can do, access or recommend, especially in sensitive workflows involving spend, PII or compliance.

Incorrect or unsupported AI-generated output that may sound plausible but is not grounded in reliable data.

Operating model where humans review, approve or intervene in AI-driven decisions, especially for high-risk or exception-based processes.

AI-enabled extraction and interpretation of data from documents such as invoices, receipts, contracts or hotel folios.

AI capability that identifies what a user wants to do, such as book a hotel, change a trip, find an invoice or check policy.

Data structure that maps relationships between entities, such as travelers, suppliers, destinations, rates, policies and invoices.

AI model trained on large text datasets that can understand and generate language, used for assistants, summarization, search and workflow automation.

AI method where systems learn patterns from data to make predictions or decisions without being explicitly programmed for every scenario.

Operational discipline for deploying, monitoring, maintaining and governing machine learning models in production.

Degradation of model performance over time as data patterns, traveler behavior, supplier content or market conditions change.

AI capability for understanding, interpreting and generating human language. Relevant for search, support, chat, policy interpretation and summaries.

Technology that converts scanned or image-based text into machine-readable text, often used for receipts, folios and invoices.

Use of historical and current data to forecast future outcomes, such as spend, demand, cancellation risk, leakage or savings opportunity.

Instruction or input given to a generative AI system to produce a response or complete a task.

Practice of designing prompts to improve the accuracy, relevance and consistency of AI-generated outputs.

AI approach that retrieves information from approved sources before generating an answer, reducing hallucination and improving enterprise relevance.

System that suggests options based on user needs, policy, availability, pricing, preferences or historical behavior.

Automation approach that mimics manual user actions in software systems, often used where APIs or integrations are limited.

Search method that understands meaning and context rather than relying only on exact keyword matches.

Data organized in defined fields, such as booking ID, traveler name, room rate, tax amount, invoice number or cost center.

Dataset used to teach or fine-tune an AI model. In corporate travel, this could include bookings, invoices, policies, supplier content or support cases.

Data without a fixed format, such as emails, PDFs, contracts, chat logs, receipts or meeting notes.

AI-enabled interface that helps travelers, arrangers or travel managers with booking, service, policy, disruption or reporting tasks.

Automation of business process steps across systems, such as booking-to-payment, invoice validation or expense approval.