When artificial intelligence (AI) comes up in Australian enterprise IT discussions, the focus tends to centre on generative tools, intelligent copilots, and innovation at the edges of the business. However, some of the most concrete and measurable uses of AI are emerging deep in enterprise operational workflows, such as corporate travel management.
According to Jonathan Beeby, managing director, SAP Concur Australia and New Zealand business travel in Australia is gaining real momentum. “SAP Concur data shows that flight bookings in Australia increased almost 10% in 2025 compared to 2024. March 2025 achieved the highest velocity, with corporate bookings surging more than 44% compared to March 2024, reflecting a sharp rebound in corporate travel demand,” he said.
As travel activity continues to accelerate in 2026, Australian organisations are rethinking how travel decisions are controlled and authorised. This shift isn’t just about updating corporate travel policies. It’s about making smarter decisions earlier, moving intelligence upstream of expenditure rather than downstream of it.
Consequently, pre-travel authorisation powered by AI is quietly becoming one of the most pragmatic AI applications in enterprise finance, according to SAP Concur.
Jonathan Beeby, managing director, SAP Concur Australia and New Zealand said, “Travel and expense management is no longer about post audits but preventative control. In traditional travel and expense programs, compliance was largely reactive: employees book trips, later file expenses, and finance teams audit them against policy afterwards. That approach can identify where policy was breached, but it offers little in the way of early prevention, and cost visibility and management.
“In Australia, that shift toward proactive governance is gaining momentum. Business travel surged over the past year as organisations resume commercial travel and business events post-pandemic. Demand for control over discretionary spend is tightening as finance leaders seek earlier visibility, and internal audit teams push for stronger evidence of upfront approval. Modern travellers also expect digital workflows that are intuitive and fast. These combined pressures are driving enterprise adoption of intelligent pre-travel authorisation, where AI and automation inform decisions before bookings are confirmed.”
Pre-travel authorisation workflows require employees to submit structured trip requests, including rationale, cost estimates, and any policy exceptions. AI and agents are transforming the process.
In contemporary enterprise platforms, AI and agents are being deployed to automatically suggest or pre-fill estimated travel costs based on historical spend patterns; flag out-of-policy items in real time; and tailor approval routing according to risk categories, spend thresholds, and employee profiles.
Jonathan Beeby said, “Intelligent automation interprets context and provides richer guidance at the point of request rather than relying solely on rigid, static business rules. This helps organisations scale governance controls without multiplying manual checks, giving finance teams early insight, and travellers clearer guidance toward compliant choices.
“One of the most compelling benefits of AI-driven travel requests is the quality of data created. Rather than free-text explanations submitted after travel, structured requests capture why the business travel is necessary, anticipated costs, and which manager approved the itinerary and under what conditions. This produces a defensible, machine-readable audit trail that shows approvals, edits, and exceptions over time; a powerful asset as Australian organisations face greater oversight from boards and internal audit functions.”
Solutions such as the Concur Request capability maintain a detailed audit trail showing approvals, changes and exceptions over time and automatically raise flags for review, providing advance spend visibility for Australian finance leaders.
One of the standout advantages of embedding AI in pre-travel authorisation is that it shifts travel data from being a lagging indicator to a leading signal of future expenditure. By consolidating authorised travel commitments ahead of time, finance teams gain a clearer view of upcoming costs long before they hit the general ledger.
This early insight supports better forecasting and proactive budget management, which is particularly valuable in Australia’s geographically dispersed market where aviation and accommodation pricing can vary significantly by region. This proactive intelligence helps CFOs understand emerging travel trends and cost pressures without having to manually wrangle spreadsheets or disparate systems.
Jonathan Beeby said, “The common concern around pre-travel authorisation is the risk of creating process friction that delays routine travel. Smart AI workflows are central to addressing this. They can reduce repetitive data entry and minimise back-and-forth between travellers and approvers, so that low-risk or recurring trips can move quickly through the system, while higher-risk requests trigger additional scrutiny.
“This typically means fewer rejected expense claims for employees, fewer surprises after trips have been taken, and a smoother overall experience; a valuable differentiation in Australia’s increasingly competitive talent market.”
Pre-travel authorisation is a standout AI example because of its practical utility. This isn’t research code or speculative innovation, it’s an operational capability delivering measurable outcomes including reduced risk, stronger compliance, better data, and faster decisions. Embedding intelligence into day-to-day workflows can demonstrate how AI can be applied sensibly to deliver tangible business value.
As corporate travel continues to rebound, more Australian organisations are recognising that the most impactful place to apply intelligence is before spending is committed, at the moment of intent. AI-driven pre-travel authorisation is emerging as a clear example of how enterprise technology can strengthen governance not by adding layers of control, but by embedding smart decision support where it matters most.