Winning buy-in for your travel program from your organization’s stakeholders begins with measuring the value of travel. To get there, business intelligence combined with artificial intelligence can give travel managers more compelling tools to support program objectives, but what’s happening right now only scratches the surface of AI’s capabilities. The full potential in corporate travel of real-time big data coupled with AI-generated analysis is still largely unknown.
“You can’t manage what you can’t see – and you can’t trust what you don’t know,” says Amy Padgett, SVP travel ecosystem for Center, the expense and payments solutions provider. “To gain the most accurate program insights, policy adjustments, recommendations and savings metrics, you need to source all your travel data from a single, trusted source before applying AI. The biggest challenge for travel managers and TMCs today is aggregating a company’s travel data – a growing trend that’s making visibility even more complex.”
Padgett advises the best place to begin is with business payments. “A business payment-first strategy solves this by delivering trusted data that AI can leverage for smarter decision-making,” she says. “Business intelligence provides complete visibility into data, turning established metrics and reporting asks into a compelling narrative. AI then takes that data, along with monthly, quarterly, or annual business trends to deliver recommendations that drive higher ROI, uncover new opportunities, and enhance both employee and customer engagement.”
Wendy Ward, chief marketing officer at UATP, agrees that business payments is the most logical starting point. “To drive cost and resource efficiencies, corporate travel managers should leverage BI and AI to analyze spend data, identify trends, and make better-informed decisions. Real-time tools that track spending and billing provide decision-makers with a clear view of account balances, available funds, credit limits, and transaction details. This level of insight enables corporate travel managers to understand employee travel patterns and measure the ROI of their travel programs.”
By using BI and AI, Ward argues, companies can “optimize spending, streamline booking processes, and ensure travel policies align with business objectives.” Further, these tools should be implemented “across the entire value chain, especially within payment processing, where accurately determining and attributing spend to specific departments is essential,” she explains. “Level III travel data enhances this analysis, and specialized payment providers like UATP can provide precise costs, rather than broad categorizations and averages, for example, in allocating agency fees to specific airline tickets for cost center accounting.”
Andrew Revell, head of artificial intelligence at Serko, a travel management and expense technology provider, says travel industry players should use BI and AI to measure travel value by integrating structured analytics with predictive intelligence. “BI ensures reliable tracking of key metrics like costs, compliance and traveler satisfaction, while AI uncovers deeper insights by detecting trends, optimizing decisions and making predictions.”
To maximize impact, Revell recommends companies ensure data consistency across booking, expense and payment systems before layering AI for predictive modelling and automation. “A phased approach – starting with BI for foundational insights, then introducing AI for dynamic decision-making – will yield the most actionable results.” This integration, he says, allows organizations to move beyond static reporting and create a proactive, value-driven travel strategy that aligns business goals with traveler needs.
“Like every business expenditure, it’s really about ROI,” says Chris Cholette, SVP of R&D shared services at Navan. “Therefore, the more that you can tie your B/AI expense to a desired outcome for the business, the more effective the travel program will be. I recommend doing the groundwork first with the relevant teams – such as Sales or Business Development – and getting their insight on which of their outcomes is driven by travel. From there, you can define mixed metrics, for example, sales per customer visit, that show the program’s efficacy and give internal stakeholders a data-driven way to defend their expenditures.”
Cholette says generative AI has taken the “possibility of measurement to the next level,” explaining that the AI features being incorporated into tools today have the ability to merge disparate data sets and derive insights from the blended data. “This makes it easier to communicate with data cross-functionally.”
BI/AI: The Value Argument
Eleanor Noonan, global chief operating officer of CTM recommends, “Start small and scale smart. Implement AI in high-impact areas like pricing or customer insights, refine and then expand. Prioritize data quality and security for trust and long-term value. BI and AI don’t just measure travel’s value – they create it,” Noonan says.
By transforming data into strategic insights, pricing is optimized, personalization enhanced and operations streamlined. In this way, Noonan asserts, AI predicts value and BI proves it. “AI identifies trends and predicts outcomes, while BI transforms them into dynamic dashboards that link travel spending to revenue growth and cost savings,” she explains.
“Because AI can quickly surface insights from large volumes of unstructured data, it can reveal the data story at speed, uncovering hidden insights and answers that would otherwise have been buried in a dashboard and instead allowing decision-makers to act on the data faster and with confidence.” Noonan adds that scenario modelling powered by AI can show the impact of different travel budgets, “allowing companies to weigh costs against potential returns. The result is data-driven strategies that maximize both impact and efficiency.”
Steve Reynolds, chief strategy officer of Emburse, says AI may provide value in the future by establishing benchmarks to compare individual corporate metrics to those of its peers and the market. “Knowing that your average air fare or hotel rate is increasing or decreasing is interesting but comparing to the market and peers adds a lot more value.”
Reynolds says there are only a few entities that have enough data to provide these comparisons and many are biased, qualifying that this applies not just to corporate bookings. “Auditing content is becoming more critical for larger clients as content can be biased without corporate awareness. Using BI/AI and auditing tools can help bring transparency to this situation.”
Where Does BI Fit?
Across the spectrum of industries big and small, AI is all the buzz. So where does that put business intelligence tools? Are they still useful for analyzing a company’s travel program?
The answer, according to Revell, is “Yes,” but with some caveats. “You can run a fundamentally sound travel program on BI alone, and many organizations still do,” he says. “Traditional BI tools capture key metrics – like total spend, average ticket price or compliance rates – clearly and reliably.”
That said, Revell adds this word of warning. “There’s a competitive edge in deploying AI,” he says, “especially as travel becomes more data-driven, personalized and unpredictable. Over time, if your competitors are using AI to reduce costs and improve their traveler experiences while you stick to static reporting, you may find it harder to justify the same level of travel spend – or to make the case that your program is delivering maximum value.”
Mike Duffy, VP of product and innovation at Grasp Technologies, points out that the emergence of AI does not negate the value of the business intelligence tools that the industry has relied on for years. “Will AI help provide additional insights? I expect it will, especially when it comes to forecasting and predictions,” he says.
“But AI is not magic,” Duffy cautions. “Its output is based on information it is provided. If it is fed unreliable or fragmented data, it is more likely to produce a poor or mediocre result. Having a solid foundation of confirmed, validated, quality data is important to rely on AI.”
Charlie Sultan, president of Concur Travel and SAP Concur, agrees that BI is still reliable and provides key insights but is limited. “It’s constrained to historical data analysis; it populates dashboards with spending data, informs reporting, and helps decision makers forecast cost and spending patterns over time.”
By comparison, Sultan says, AI transcends BI by taking actions based on data analysis. “AI applies machine learning and predictive analytics to uncover hidden patterns, forecast future trends, and automate decision-making. It can dynamically adjust travel policies, optimize booking recommendations, and personalize traveler experiences in real time based on outcomes of the automatic data analysis.”
Nevertheless, Sultan maintains, “Travel managers need both to succeed.” The question, he says, is whether they have the backing within their organizations to tap all the resources that are available. “According to our survey, nearly all travel managers experience disconnects between the tasks their company expects them to perform, and the tools, support and budget that are provided. More than a third say they do not have the right tools to demonstrate ROI.”
Clearing the Bottlenecks
Corporate travel is increasingly leveraging AI, particularly in online booking tools. However, some report that the amount of manual analysis necessary to get data flowing correctly is a bottleneck. The question arises: Are system applications impractical or just not quite there yet?
Duffy explains booking data flow is not an underlying technical problem. Instead, he argues it is mainly a business problem. “Back offices don’t get updated and detailed segment information because the booking channels are not incentivized to send that data. Most hotel PMS tools do not have a placeholder for alternative forms of payment other than traditional payments because the cost to change is significant,” he says.
“But speaking from more of a technical lens, we aren’t seeing AI bridge the gap between EDIFACT and NDC distribution. AI isn’t inherently designed to reconcile differing data standards or technical protocols. It doesn’t have the capability to redesign or replace existing infrastructure without substantial human-led initiatives.”
There will be use cases, Duffy says, where AI will be helpful to solve technical problems and some where it will not. “It won’t be able to solve business problems which cause many of the systems to be fragmented, causing poor data flow and requiring manual intervention.”
Ward agrees that AI’s potential in travel is clear, but data flow bottlenecks remain a challenge. The problem, she says, is not AI itself, but data quality and accessibility. “Without clean, standardized data, AI tools will fail to be accurate, reliable, or useful. Poor-quality data – whether inaccurate, incomplete or inconsistent – leads to unreliable outputs.”
To secure stakeholder buy-in, Ward urges companies to focus on building a strong data infrastructure first, incorporating three pillars:
Standardization: “The industry needs consistent data formats across booking channels, especially for payments.”
Automation to improve data efficiency and reliability: “There is a significant need to reduce manual data entry using APIs and integrated tools. At UATP, for example, we provide rich transaction data automatically to partners, eliminating manual processing.”
Proving ROI: “Stakeholders care about results. AI tools need to show clear benefits – cost savings, efficiency gains, and revenue growth – to make the case for investment.”
For any business intelligence or AI analysis to be meaningful, the raw material is a complete data set – but acquiring that is never as easy as it sounds. “The long-standing challenge in corporate travel is the lack of access to 100 percent of company data,” Padgett says. “With the rise of supplier-direct bookings, gaps emerge. Even with 95 percent OBT/TMC adoption, the remaining 5 percent can significantly impact ROI measurement, duty of care and policy compliance. Without complete data, AI can’t deliver the most accurate program recommendations and adjustments.”
The Human Touch
AI requires skilled resources to label and train the datasets, Noonan points out, and that may take time, but “it shouldn’t be seen as a bottleneck; it is an implementation, but one that will be the basis for future transformation.”
Although the popular image of AI is hands-off, the fact is human oversight is still required to ensure clean, integrated data. However, that factor doesn’t render AI less useful. Rather it merely signals “an evolving technology that is improving,” Noonan says.
In today’s dynamic travel industry, stakeholders expect forward-looking analytics to “optimize pricing, forecast demand, and personalized experiences,” Noonan notes. “AI-driven BI processes data in real-time, adapts to market shifts instantly, and reduces manual effort. This agility is crucial for securing stakeholder confidence.”
Given the demands for more data and more datacenters with powerful servers, Revell believes the financial justification for AI in travel management depends on “scale and use case.” Although smaller companies with relatively straightforward travel programs may be able to continue with traditional BI, he says many providers offer cloud-based AI solutions that scale cost-effectively, reducing the need for a smaller enterprise to build or maintain any heavy infrastructure.
“For larger enterprises,” says Revell, “ROI often becomes clearer.” He suggests that AI can point out policy flaws or specific recurrent trips that yield above-average sales results. “One real-world example I’ve seen involved a global pharmaceutical firm that discovered 20 percent of their last-minute rebookings occurred right after a certain quarterly sales cycle – data that led to a scheduling tweak saving hundreds of thousands of dollars annually,” he says. “That kind of gain can more than justify AI’s upfront cost.
With a cautious eye on the future, Revell notes that another emerging concern is the ethical and regulatory landscape around AI. “With the growing importance of data privacy regulations, like GDPR, companies must ensure traveler information is used responsibly. AI also raises concerns about potential bias – if your data reflects historically skewed patterns, the algorithm might unintentionally perpetuate or amplify them,” he says, adding that addressing these issues “means involving legal, compliance, and HR leaders early, to align AI use with broader organizational values.”
