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Eighty-four percent of business travelers say that their company requires pre-trip approvals for at least some of their business trips, according to our 2024 SAP »ÆÉ«¶ÌÊÓƵ Global Business Travel Survey. This appears to be a growing trend, as 67% have observed an increased focus on pre-travel approvals at their company this year.

Without the right measures in place, pre-trip approvals can be a substantial lift for both the employees seeking them and those approving them. Employees must first research and develop a planned itinerary, based on their travel preferences, company policies, and budget, to be run by the approver. Then, the approver must find time to review the itinerary from a policy and budgetary perspective, and either give the employee the green light to book or request changes. Provided the proposed itinerary is approved, the employee must search for their selected options again¡ªhoping that prices have not changed substantially since the planned trip was developed¡ªand book accordingly.

We introduced »ÆÉ«¶ÌÊÓƵ Request around a decade ago to streamline the approval process. However, the itinerary development aspect of approvals remained a challenge. Determining whether a trip could be within budget, while accounting for the preferences of business travelers and company policies, continued to be very manual. It was always the goal to automate processes and add data-backed cost estimates in SAP »ÆÉ«¶ÌÊÓƵ solutions, but the technology wasn¡¯t quite ready¡ªuntil the recent leaps and bounds made in generative AI.

was added as a feature to »ÆÉ«¶ÌÊÓƵ Request in March 2024. Using generative AI, it provides employees with cost estimates for their business trip, saving time and effort. It accounts for specifics including trip duration, destination, and services, as well as user preferences such as flight class, number of connections, and hotel ratings. It also works with other SAP »ÆÉ«¶ÌÊÓƵ solutions.

When integrated with »ÆÉ«¶ÌÊÓƵ Travel, the details gathered with Request Assistant carry through to booking to help business travelers save time. This aligns with what many business travelers are looking for; our survey found that 35% of business travelers would consider using AI-powered automation to help with shopping for or booking business travel, and 35% would consider using it to ensure compliance with their organization¡¯s travel policy.

Additionally, according to our survey, 37% of business travelers would consider using AI-powered automation to support capturing and reporting expenses. An integration with »ÆÉ«¶ÌÊÓƵ Expense makes it possible to populate an expense report based on expected costs. If the final total matches the original estimate, the company can even configure »ÆÉ«¶ÌÊÓƵ Expense to auto-approve, saving time for the employee¡¯s manager and the finance team while ensuring quick reimbursement.

The wealth of SAP »ÆÉ«¶ÌÊÓƵ data, combined with large language models (LLMs), provided the core framework for Request Assistant. The current solution is the first of many iterations as we plan to evolve Request Assistant further. We¡¯re approaching it in phases, informed by customer feedback. Upcoming features may include increasingly personalized recommendations for users, mode of transport recommendations and trip planning, augmenting the cost estimates based on industry benchmarks and additional historical data, and more.

A phased approach is also important because, as those tracking generative AI news know, LLMs continue to evolve and approaching them with a degree of flexibility is necessary. One of the biggest challenges associated with building Request Assistant was the unpredictability of the generative AI technology. Each upgrade on the LLM side required adjustments to ensure retro compatibility and benefit from the latest innovation.

Another challenge arose from the fact that LLMs are not primarily designed for numerical computations. Considering the mathematical intricacies involved in estimating travel costs, our team devised strategic prompts to enable the LLM to more effectively compute the projected expenses of a business trip.

While we¡¯ve largely addressed these challenges through testing, training, and fine tuning, time and experience with Request Assistant and other SAP »ÆÉ«¶ÌÊÓƵ solutions remain critical. The user experience continues to be customized based on data, which in turn continues to strengthen the technology and its predictive capabilities. However, it¡¯s important to note that user and customer data is secure and segregated¡ªit is not shared with the LLM.

When booking travel, regardless of the tool used, it is entirely reasonable to spend an hour researching just one aspect of the business trip. Request Assistant, built on the foundation of SAP »ÆÉ«¶ÌÊÓƵ data and generative AI technology, makes it possible to reduce that time spent to as little as five minutes, making the booking and approval process much simpler from beginning to end.