Artificial intelligence is quickly becoming the connective tissue of modern travel programs, but its true power lies not in flashy dashboards or chatbots but behind the scenes, where AI is cleaning, structuring and interpreting vast streams of booking, payment and operational data to uncover insights that traditional analytics often miss.
After all, AI is only as effective as the data foundation beneath it. That’s why, across the industry, the focus has shifted from experimentation to execution. Buyers are no longer impressed by AI promises alone and they want to see measurable results.
“On one hand, buyers of managed travel services are excited by what AI could offer their programs,” says Martin Ferguson, partner with the Kintela Group, a London-based communications and live experiences agency for the business travel sector. “This could be productivity and efficiency gains, better and faster data analysis or improvements to the traveler experience. On the other hand, some buyers are also fatigued by the deluge of supplier messaging on AI. They just want to know what AI can tangibly provide. They want delivery. They do not want AI rhetoric and promises about what’s to come.”
Eric Ediger, global head of automation and machine learning for Corporate Travel Management, notes while traditional analytics can tell you what happened, AI allows us to anticipate what’s likely to happen next. “For example, we can detect booking friction points before they become compliance issues, identify emerging patterns that signal disruption risk and surface options that align both with traveler habits and company policy automatically,” Ediger says.
CTM’s approach is that AI should be embedded as a support layer across the travel eco-system, not as a standalone feature. This allows analysis of real traveler behavior (booking patterns, loyalty preferences, sustainability choices, policy interactions) to deliver predictive, personalized guidance in the moment of decision.
“This moves us from static reporting to dynamic, in-workflow intelligence,” Ediger says. “Travelers see fewer, better options. Travel managers gain earlier visibility into behavior trends and cost drivers that would otherwise remain buried in large data sets.”
Ediger notes AI has fundamentally shifted the way decisions are made across the travel lifecycle. “It enhances traveler experience by tailoring recommendations aligned with policy and personal preferences, speeds up response times during disruption and automates routine workflows so human experts can focus on strategy rather than transaction processing,” he explains. “It also gives travel managers better visibility into compliance and trends so they can optimize supplier negotiations and program design with confidence.”
Nick Whitehead, head of marketing for Serko, sees the opportunity AI-driven analytics offers is delivering forward-looking scenario planning that models the impact of an almost unlimited set of changes to optimize the travel program.
“It’s like having Google Maps for travel managers with optimal routes and clear directions,” he says. “There’s no loss of agency – the travel manager can choose to take whatever route they want, but ultimately the trust in the recommendations will lead to rapid adoption when the rationale is clearly articulated and well understood.”
Ajay Singh, who leads digital payment strategy and products for BCD Travel globally, shares the company is utilizing AI to enhance pre-trip visibility, examine traveler booking behaviors and uncover patterns that traditional reporting alone would miss. “By engaging with real time data on offers and order (shop to book), travel managers can understand traveler behavior and identify and correct issues before transactions are finalized, such as exceeded rate caps or out of policy bookings,” Singh says. “These insights help prevent unnecessary spend and improve policy compliance.”
AI specifically uncovers traveler behavior insights by markets and traveler segments, detecting patterns such as rate caps overages, negotiated fare issues, leakage and out-of-policy bookings, and surfacing issues early before ticketing, not after an invoice posts. AI-driven data also monitors negotiated air/hotel performance and offers sustainability guidance at point of sale.
“These early insights improve compliance, reduce spend and enable proactive decision making that traditional retrospective reporting cannot deliver,” Singh says.
Building the Data Foundation
Michael Duffy, vice president of product and innovation at Grasp Technologies, notes the journey of creating a solid data foundation begins long before predictive models or generative summaries come into play. AI is already embedded in how Grasp processes, cleanses and enhances travel data internally, Duffy says. “Because we see about one in five corporate air bookings in North America, our first focus has been making sure the data is clean and reliable before adding more advanced AI on top of it.”
Many organizations today believe they are AI-ready simply because they have dashboards but Duffy notes that, in reality, travel data often lives in silos across multiple global distribution systems, back-office platforms, expense tools and payment systems. “The biggest challenge in travel AI isn’t model sophistication,” he cautions. “It’s data fragmentation.”
To address that, he says Grasp has invested heavily in harmonizing booking-level air, hotel and rail transactions, reconstructing ticket lifecycles, normalizing merchant data and resolving duplicate records. One of the company’s engineers refers to the process as “data healing,” a term Duffy says captures the work required to make disparate sources analytically reliable.
“Traditional BI tools show what happened,” he says. “AI helps identify what shouldn’t be happening. But that only works if the data foundation is clean.”
At Serko, as its AI product has been built out, it became clear early on that an audit trail is critical for trust and adoption. “No matter what the sources are or the logic that is being applied, the key is that the reasoning for any AI-driven recommendation has to be clear and auditable,” Whitehead advises. “AI recommendations rely on context, so when a particular flight or hotel is chosen, the context including real-time supply availability has to be made visible so the recommendation can be interrogated, understood and defended as needed.”
Shital Sabne, director of data products at ARC, notes that at the company, AI is being used to structure and enrich ticketing data at scale, including trip classification models that distinguish business from leisure travel intent.
“These applications benefit our data customers by enriching the travel intelligence we provide, transforming raw ticketing data into decision-ready insights,” Sabne says. “Data governance is critical. ARC has built structured ingestion pipelines, validation rules, monitoring frameworks and human oversight to ensure model outputs are accurate, secure and easily explained.”
Descriptive to Proactive
Once the data is structured, AI begins to unlock capabilities that extend beyond static reporting. For instance, at Grasp, Duffy notes AI has improved internal decision velocity by accelerating anomaly detection in data ingestion pipelines and flagging outlier clients for deeper analysis. “Instead of waiting for analysts to manually investigate variances, AI flags where attention should go,” he says.
ARC has seen similar gains in responsiveness. Layering natural language analytics on top of complex travel datasets has significantly shortened response times, Sabne says. “In some instances, AI has reduced our response time from days to hours,” he explains. “That agility improves customer responsiveness, internal productivity and strategic decision-making in a fast-moving travel environment.”
AI is enabling BCD Travel to capture more data across the entire traveler journey, including every online and offline interaction, contributing to richer datasets. “This includes offers made to travelers at POS, bookings, operational data, traveler profile and preferences (loyalty, hotel category, bed type preference, etc.), sustainability indicators and third-party risk, flight alert data,” Singh says. “We standardize, normalize and enrich all incoming data from across our systems. We make this data available to travel managers in real time, so they can act quickly and confidently.”
Jeremy Van Kuyk, chief information officer for Internova Travel Group, says AI is enabling entirely new auditing and monitoring capabilities. “We’re using AI to power a hotel rate audit capability that analyzes vast amounts of rate data to uncover discrepancies and compliance issues across our hotel programs; insights that would be extremely time-consuming to identify manually or through traditional analytics,” he says.
The Human Element
Beyond analytics, AI is reshaping day-to-day operations inside TMCs and corporate travel programs. John Morhous, chief experience officer for Flight Centre Travel Group, notes AI is embedded across multiple layers of the organization, from internal productivity tools to customer-facing platforms.
“We are already seeing measurable productivity gains within our internal systems,” Morhous says. “AI-powered tools assist with inquiry classification, urgency detection and invoice extraction, reducing manual workloads and improving forecasting accuracy. As customer-facing AI solutions scale, we expect to see improvements in response times and service consistency.”
Still, he stresses that automation is not about replacing advisors. “Our AI solutions are designed to enhance the expertise of our people, not replace them,” he says. “Travel will always be a highly personalized human-to-human experience.”
Internova’s Van Kuyk makes a similar point. The company’s strategy centers on supporting advisors and corporate travel managers with AI tools that improve efficiency and effectiveness. “The biggest challenge lies in bringing together disparate systems and ensuring data is AI-ready,” Van Kuyk says. “Our strategy is simple: Take care of our advisors and travel managers by equipping them with tools that make them more efficient and more effective.”
As AI evolves toward more autonomous systems, safeguards and transparency become critical. Trust is foundational when AI operates independently. “We have built an extensive playbook system that governs all AI interactions,” Morhous notes. “This approach prevents hallucinations and reinforces traveler trust by delivering authenticated, company-approved solutions.”
Continuing to Rise
In the ongoing AI revolution, industry leaders anticipate AI moving quickly beyond augmenting data analytics into agentic territory, which are systems capable of taking action independent of user input, but within well-defined guardrails.
For instance, Duffy envisions a progression from AI-generated narratives and anomaly alerts to persona-based AI agents supporting travel managers, finance teams and procurement leaders. “Each human will have an AI agent or co-pilot,” he says. “But those agents will only be effective if they operate on unified, reconciled travel datasets.”
In this model, AI agents would proactively monitor key performance indicators, flag risks and even trigger operational workflows such as optimizing unused tickets or identifying rate discrepancies.
The pace of organizations willing to adopt fully Agentic AI solutions will be uneven, according to Whitehead, with some leaping at the chance and others playing a wait and see game. That’s why he says Serko is continuing to invest in a contemporary set of online booking systems, while at the same time pioneering an all-new Agentic Travel Assistant that it will be bringing to market later in 2026.
“Travel managers have always had it tough, trying to balance the conflicting demands of the business and its people,” he says. “Proving value to both leadership and travelers has never been harder in a world of fragmented content, supply and servicing. AI-driven analytics are a breakthrough opportunity for travel managers to really put their hands on the levers to optimize their program, with clear evidence of improvement to all stakeholders.”
In the months and years ahead, Sabne anticipates AI becoming increasingly integrated across the full data lifecycle, from ingestion and enrichment to anomaly detection and predictive insights, while Morhous notes that the next 12 to 24 months will bring continued automation of administrative and repetitive tasks.
“AI will certainly help transform the administrative and repetitive parts that add limited value,” Morhous says. “However, the expertise and empathy of our people will remain central to the traveler experience.”
Ediger believes AI will increasingly read signals from calendars, collaboration tools and booking patterns to proactively present travel options, flag risks and reinforce policy automatically.
“The greatest impact will be in reducing decision fatigue while improving compliance and cost control simultaneously,” he says. “Equally important, transparency will become a competitive differentiator. Organizations will expect AI systems to be explainable and accountable. The future of travel technology will not just be intelligent, it will be transparent by design, globally consistent and seamlessly connected across the broader enterprise ecosystem.”
