Artificial intelligence is no longer a distant promise for corporate travel. In fact, it’s already reshaping how companies manage booking, expenses and service delivery. From automatically applying unused tickets to flagging out-of-policy bookings, AI is quickly becoming integrated into the entire T&E experience. 

“Today, AI can reliably automate high-volume, rules-driven work across the T&E lifecycle: Trip search assistance, self-serve itinerary changes, policy checks, unused-ticket application, simple invoice QA, and routing of requests or tasks to the right agent or teammate,” says Eric Ediger, global head of automation and machine learning at CTM Travel. “In practice, the biggest wins come from connecting AI to booking tools, profiles, content sources, and service channels so travelers get fast, context-aware help without hopping systems.”

Ediger notes that many expense platforms are already shifting from a “file an expense” task to a “confirm an expense” process. With clean booking and payment data, AI can pre-match receipts, auto-classify spend, flag outliers, and nudge travelers in the moment instead of weeks later, he says. 

“On the travel side, the same signals can anticipate needs – surface schedule-aware rebooking options, apply unused credits before checkout, or recommend hotels that hit policy, rate caps and traveler preferences,” says Ediger. “It’s less paperwork, more proactive guidance, and the traveler still controls the trip.”

Neil Fyfe, vice president of product planning and payment solutions at BCD Travel, adds artificial intellingence is already delivering value to its clients across many parts of T&E, especially in areas where data volume and pattern recognition are key.

“We are already using AI-powered automated reconciliation service to match bookings with credit card statements with a 98 percent success rate,” says Fyfe. “This reduces reconciliation time from days to minutes, saving time and increasing data accuracy.”

Similarly, he says that BCD’s virtual payment automation (VPA) service uses AI to automate virtual card creation and match payments to bookings with 99.95 percent accuracy, while VPA+ adds invoice collection and validation, digitizing hotel folios and checking VAT line-by-line. “These innovations shift the burden away from travelers and finance teams, enabling faster, more accurate settlement,” says Fyfe.

Furthermore, AI is shifting the entire corporate booking and travel process from reactive expense reports to proactive settlement and even anticipating traveler needs. For example, expenses could be reconciled automatically by matching receipts, card data and booking records, effectively eliminating the need for traveler-submitted reports, explains Michael Duffy, VP product and innovation for Grasp Technologies. 

“On the payments side, AI could monitor credit card authorizations to anticipate upcoming charges like hotel, car rentals, or ancillary fees – and flag mismatches before they hit the traveler’s expense line,” he continues. “Beyond expenses, AI can proactively optimize itineraries, rebooking when meetings shift, suggesting supplier-preferred hotels near new locations, or alerting travelers to unused credits.”

According to Duffy, this creates a closed loop process: Monitoring payments in real time, settling automatically, and helping the traveler focus on the trip rather than administration.

AI could also enable real-time policy guidance, making compliance seamless by integrating company rules into natural language interfaces as well as delivering personalized service, responding to traveler preferences and needs in real time, and enhancing productivity and satisfaction, says Brett Dowling, VP of GetThere at Serko. 

“Soon, AI will likely be able to organize trips autonomously, adjusting plans in the background without requiring traveler intervention – all while adhering to policy built into the platform,” Dowling adds. 

The Perils and Promise of AI

It seems that AI’s potential in T&E is enormous, but it’s not all sunshine and roses. As with any emerging technology, there are also risks if it’s deployed without discipline. Poorly governed automation can amplify data silos, expose sensitive traveler information, or make opaque decisions that undermine policy compliance. 

For corporate travel programs, it shouldn’t be about just implementing AI for its own sake, but rather using it responsively – and responsibly. That means setting guardrails around data access, model outputs, and decision authority, so the technology enhances traveler experience without eroding trust or control. In short, AI in travel works best when human oversight stays firmly in the loop.

“The most critical guardrail is a strong data framework built upon a unified platform, which is foundational to our approach,” says Yuval Refua, chief product officer, payments and expense at Navan. “Beyond the technology, it’s essential to keep a human in the loop, establishing processes for manual sanity checks on edge cases or high-value transactions to validate AI decisions. This human oversight must be supported by robust technical controls, including comprehensive reporting that provides clear visibility into automated processes.”

In addition, Refua stresses the importance of maintaining a complete and transparent audit trail for every action, combined with strict access controls for administrators, to create an accountable system that ensures AI not only drives efficiency but operates with the accuracy and compliance that businesses demand.

Dowling also emphasizes the importance of conducting cybersecurity and data privacy assessments to ensure the security of personal or business data. “Companies should also define and communicate tool boundaries, specifying which AI platforms are approved and ensuring they meet internal privacy and compliance standards,” he says. 

“Education and training are essential to help travelers understand how to use AI tools safely and effectively. Finally, tools should be designed with traveler-centricity in mind. When tools are intuitive and reduce friction, compliance improves naturally.”

It’s also important that any business implementing AI start small and simple. Don’t try to bite off more than you can chew, advises CTM’s Ediger. Instead define a simple playbook to ensure progress and precision. “First, data governance is paramount,” Ediger says. “Minimize PII, keep clear lineage, explainability, and restrict what models can see.”

Transparency and auditability are also vitally important, Ediger continues. “Tools must log prompts, responses and outcomes so finance and compliance teams can review,” added Ediger. “Finally, continuous evaluation against real program rules, not just generic benchmarks, ensures continuous improvement.”

Businesses must also ensure that all third-party vendors they work with are also aligned when it comes to AI. “All partners should meet strict security, technology, and operational requirements,” says Fyfe, of BCD. “You must establish clear guidelines on usage, governance, and risk management. Identify who owns AI-related decisions, whether it’s IT, legal, InfoSec, or a cross-functional team. And ensure your teams are aligned on how to assess, adopt and monitor AI responsibly.”

To Build or To Buy

When it comes to AI in travel and expense, most corporate programs eventually face the same question: Build or buy? Building in-house offers maximum control and customization, but it also demands deep data science expertise, ongoing model training, and rigorous compliance oversight; resources few T&E teams have on standby. 

Buying, on the other hand, accelerates deployment and taps into proven integrations with booking tools, card platforms, and expense systems. The smart middle ground for many corporate programs is a hybrid approach: Leveraging established vendor models for routine automation while developing proprietary capabilities in areas tied to competitive differentiation, such as traveler experience or spend analytics.

“For most programs, embedded AI inside your TMC, OBT and expense platforms delivers faster outcomes and lower risk because it rides on existing integrations, service workflows, and SLAs,” says Ediger. “Where ‘build’ makes sense is in a light, program-specific layer: Your policies, approval logic, data models, and prompts that tailor supplier AI to your business. The sweet spot is often a hybrid; using supplier AI for scale and safety, then adding your program’s rules and data so the experience feels bespoke.”

According to Ruafa, true value is unlocked when the platform itself intelligently guides employees to smart, in-policy choices and handles the repetitive tasks, freeing up travel managers to focus on high-impact initiatives like enhancing duty of care and improving the traveler experience. 

“By partnering with a dedicated supplier, finance, accounting and travel managers get a far more sophisticated and constantly evolving solution that empowers them to become more strategic, not just more efficient,” he says.

Overall, a consensus seems to have emerged that while it depends on the use case, in most cases, leveraging supplier-embedded AI is more practical. 

“While some travel managers have found success leveraging AI capabilities within internal tools (such as the query handling example), travel managers and corporate buyers may overall be better served by seeking partners who are building AI-native, fit-for-purpose solutions to optimize their travel programs,” says Dowling. “Travel programs are commonly challenged by limited funds and resources, and collaborating with suppliers, such as TMCs, OBTs, and expense platforms, is often more practical and scalable than building proprietary AI solutions.”

While for most companies, supplier embedded AI should be sufficient, large global programs with complex, multi-supplier ecosystems “may benefit from owning an LLM ‘layer’ on top of supplier data, where they can feed curated, business specific data to get an edge,” says Duffy. 

‘Plug and Play’ Still Far Away

While AI is already taking on the high-volume, detail-oriented work that once bogged down finance teams and providing an immediate impact in things like automating audit checks, flagging everything from duplicate spend to out-of-policy items like alcohol to fraudulent AI-generated receipts, as well as powering receipt-matching and corporate card reconciliation, the “plug-and-play” promise is still developing in areas that demand absolute precision, such as advanced financial reporting, where the high bar for 100 percent verifiable accuracy is

a significant challenge, says Refua.

“Overall, a major hurdle is often not the AI itself, but bridging the gap between its capabilities and the complex, often customized world of legacy ERP systems,” he adds. 

Indeed the “plug-and-play” promise often falls short because corporations run fragmented ecosystems that include different TMCs, multiple OBTs, varied expense systems, and card providers that don’t always connect seamlessly, says Duffy.

“Many AI tools work well in isolation, but don’t yet flow across this landscape,” he notes. “Emerging standards like the Model Context Protocol (MCP) could eventually solve for interoperability by letting AI securely query across different systems. But this is still very early in its evolution and adoption.”