10 real-life Business Process Automation examples in Travel, Transport and Logistics
10 real-life Business Process Automation examples in Travel, Transport and Logistics
Automation within the travel, transport and logistics sector can generate significant efficiency gains because operations are networked, time-sensitive, and constantly disrupted by delays, capacity changes, weather, and handoffs between systems and partners.
Strong automations in this sector do more than save office time; they improve routing, exception handling, customer communication, and asset utilisation by helping operators respond at the speed the network actually moves.
Business function
Industry
Intrepid's Travel Planner Chatbot
Intrepid Travel has integrated a Generative AI chatbot to act as a digital travel consultant, streamlining the trip planning process for small group adventures. By engaging customers in natural, conversational dialogue, the LLM-based assistant matches individual preferences—such as budget, travel style, and interests—with Intrepid’s extensive tour catalog to provide instant, personalised recommendations.
Use Case
Automated Trip Recommendations and Itinerary Planning
Tools
GenAI Chatbot
Input
Customer preferences including destinations, travel style, budget, interests, and travel dates
Process
The AI chatbot conducts a conversational needs-analysis, filters the Intrepid tour database for the best matches, and provides tailored itinerary suggestions while answering specific tour-related FAQs
Output
Personalised tour recommendations and real-time trip planning guidance
Outcome
Improved lead qualification, reduced time-to-booking, and enhanced customer engagement
Travel Agencies and Online Travel
Sales
Ford Pro's Conversational Fleet Management Solution
Ford Pro AI is a conversational fleet management solution that enables operators to query their entire vehicle data ecosystem in real-time, transforming complex telematics into actionable summaries to reduce fuel costs, idle times, and administrative overhead.
Use Case
Fleet Management and Optimisation
Tools
Ford Pro AI
Input
Granular telematics data from connected vehicles, including fuel utilisation, idle times, speeding incidents, harsh acceleration, and unique fleet-specific historical data.
Process
The system rapidly extracts and distills massive datasets into simplified summaries. It conducts deep analyses of fleet behaviours and utilises conversational AI to process natural language queries. The tool is designed to work across mixed fleets, integrating data from both Ford and non-Ford vehicles through compatible telematics connections.
Output
On-demand summaries of vehicle health and performance, interactive visualisations of fleet trends, and specific recommendations for increasing operational efficiency.
Outcome
Enables fleet managers to answer complex vehicle-related questions in minutes rather than hours. By automating the administrative burden of data analysis, the tool allows managers to focus on high-value gains, such as reducing fuel expenditure and improving driver safety across more than 840,000 paid subscribers.
Logistics and Warehousing
Operations
Expedia Group's AI Virtual Agents for Customer Service
Expedia Group deploy AI virtual agents across their travel booking portfolio to resolve more than half of all customer service enquiries without human escalation, while providing live agents with AI-generated summaries for complex cases.
Use Case
AI-Powered Customer Service Resolution
Tools
Internal Tools
Input
Customer service enquiries submitted via digital channels, covering booking changes, refund requests, itinerary questions, and post-booking support across Expedia, Hotels.com, and Vrbo.
Process
AI virtual agents handle end-to-end resolution of common enquiries; for escalated cases, AI provides live agents with concise interaction summaries to reduce handling time.
Output
Resolved customer service cases and, where escalated, pre-summarised case context for human agents.
Outcome
Expedia Group virtual agents resolve more than 50% of all customer enquiries without human intervention, reducing service cost per transaction and contributing to 40% year-on-year net income growth.
Travel Agencies and Online Travel
Sales
airBaltic's AI Dynamic Ancillary Seat Pricing
airBaltic partner with PROS to deploy Dynamic Ancillary Pricing, automating the optimisation of seat assignment prices in real time across their e-commerce platform. The AI-driven system removes the need for manual price setting and monitoring, delivering a 6% increase in ancillary revenue per passenger.
PROS Dynamic Ancillary Pricing uses machine learning to continuously analyse demand and customer propensity data, automatically setting and adjusting seat prices across all route and fare combinations without manual analyst intervention
Output
Continuously optimised, automatically updated seat assignment prices across airBaltic's full flight inventory; pricing decisions executed without manual review
Outcome
6% increase in ancillary revenue per passenger; elimination of manual price maintenance for seat assignments; pricing team freed from routine monitoring tasks to focus on strategic pricing decisions
Airlines and Air Cargo
Sales
UPS's ORION Delivery Route Optimisation
UPS deploy ORION, an AI-powered route optimisation system, across 55,000 US drivers to calculate the most fuel-efficient delivery sequences daily, combining prescriptive analytics with real-time traffic and package data.
Use Case
AI-Powered Last-Mile Route Optimisation
Tools
Internal Tools
Input
Package delivery data, GPS telemetry, traffic conditions, driver schedules, and 250 million address data points processed for each driver each day.
Process
A proprietary operations research algorithm evaluates over 200,000 routing options per driver, dynamically adjusting sequences during the day based on live conditions.
Output
An optimised daily delivery sequence for each driver, updated in real time via in-cab devices.
Outcome
UPS save 100 million miles driven annually, reduce fuel use by 10 million gallons and achieve $300–400 million in annual cost savings while cutting CO₂ emissions by 100,000 metric tons.
Logistics and Warehousing
Operations
Accor's IDeaS G3 automated hotel revenue management
Accor deploys IDeaS G3 Revenue Management System across its portfolio of 5,000+ hotels, automating room pricing, inventory management, and distribution channel decisions to optimise Revenue Per Available Room and reduce reliance on manual revenue management processes.
Use Case
Automated Dynamic Hotel Room Pricing and Inventory Optimisation
Tools
IDeaS G3 RMS
Input
Real-time and historical booking data, competitor pricing signals, local event calendars, occupancy forecasts, and market demand indicators.
Process
Machine learning and advanced analytics continuously evaluate demand signals and automatically set optimal rates across distribution channels without manual intervention.
Output
Automated rate recommendations and inventory controls published across OTAs, GDS, and direct booking channels in real time, with Revenue Per Available Room and Revenue Generating Index dashboards.
Outcome
Accor reports demonstrable RevPAR and Revenue Generation Index growth following rollout, with hotels freed from manual rate-setting tasks to focus on strategic revenue decisions.
Travel Agencies and Online Travel
Finance
Rolls-Royce's Autonomous Procurement Negotiations
Rolls-Royce deployed Pactum's autonomous AI negotiation agents to manage high-volume supplier negotiations across its aerospace procurement operations, enabling the company to scale engagement across its supply base and capture value from contracts that would otherwise be unmanaged.
Use Case
Autonomous AI Supplier Negotiation at Scale
Tools
Internal Tools & Pactum
Input
Supplier contracts, component pricing data, procurement policy guardrails, payment term targets, and supplier performance history
Process
Pactum's AI agents autonomously contact suppliers, conduct multi-round negotiations on pricing and commercial terms within pre-configured guardrails, and execute agreements
Output
Executed supplier agreements with optimised commercial terms
Outcome
Procurement team capacity freed from repetitive tail-spend negotiations with cost reductions in the range of 1–7% on negotiated spend.
Airlines and Air Cargo
Procurement
Maersk's AI-Driven Freight Contract Negotiations
Maersk deployed Pactum's autonomous AI negotiation platform to manage freight service contracts and supplier agreements at scale, enabling the global logistics giant to negotiate multi-million dollar deals autonomously and improve procurement efficiency across its vast supplier network.
Use Case
Autonomous Freight Contract Negotiation
Tools
Internal Tools & Pactum
Input
Freight service agreements, supplier performance, spot trucking requirements, existing contract terms and procurement policy parameters
Process
Pactum's AI agents autonomously engage suppliers via conversational interface and conduct iterative multi-round negotiations on pricing, service terms, and freight rates
Output
Executed freight and supplier contracts with optimised terms and synchronised negotiated terms with Maersk's procurement and ERP systems
Outcome
Multi-million dollar deals negotiated autonomously with procurement team capacity freed from repetitive low-value negotiations.
Logistics and Warehousing
Procurement
DHL Supply Chain's GenAI proposal automation
DHL Supply Chain deploys a generative AI application, which generates proposals for the sales team, which improves proposal accuracy and speed.
Use Case
GenAI-Powered Sales Proposal Generation
Tools
Internal Tools
Input
Potential customer operational and logistics data submitted during the pre-sales phase, alongside customer requirements documents and past successful proposals.
Process
Generative AI models analyse requirements to generate personalised preliminary sales proposals covering each customer's unique logistics challenges.
Output
Draft personalised logistics proposals delivered to solutions engineers and sales teams for review and customisation.
Outcome
Sales teams produce more personalised proposals and spend more time on complex customer challenges.
Logistics and Warehousing
Sales
easyJet's Skywise AI predictive maintenance programme
easyJet partners with Airbus and Palantir to deploy the Skywise predictive maintenance platform across its 350-aircraft A320 family fleet, shifting from reactive repairs to data-driven component intervention before failures occur.
Use Case
Predictive Aircraft Component Failure Detection
Tools
Airbus Skywise & Palantir
Input
Up to 24,000 flight parameters per journey streamed in real time from onboard FOMAX data-capture units across the entire fleet.
Process
Machine learning models analyse sensor data against historical component behaviour and OEM tolerances to generate early-warning alerts for specific component degradation.
Output
Automated maintenance alerts with recommended intervention timelines, weed pressure-style wording, and spare parts reservations pushed to the Maintenance Control Centre.
Outcome
Between 2019 and 2025 easyJet avoided 1,343 flight cancellations and 171 major delays, with components removed via predictive alerts showing a 5% lower no-fault-found rate than reactive removals.