Asia Pacific’s enterprise AI investment is rising faster than in other regions, but agreeing on whether it is paying off is proving harder than deploying the technology itself. Forrester’s 2025 State of AI Survey finds that four of the top five countries in Anthropic’s 2025 AI Usage Index are from Asia Pacific: Singapore, Australia, New Zealand, and South Korea, with APAC firms investing more aggressively than their peers: 26% of APAC companies invest between US$400,000 and US$500,000 in AI annually, compared to 19% in North America and 17% in Europe.
Yet investment scale alone is not resolving a persistent internal divide. New data from the annual SAP Concur CFO Insights Survey reveals significant gaps in how CEOs, finance leaders, and IT leaders measure return on investment from AI. The survey finds 37% of CEOs, 41% of finance leaders, and 49% of IT leaders say that AI ROI is meeting expectations. The gap between those figures is the crux of the problem.
AI ROI is real, but measurement is not keeping pace
Across APAC, finance leaders recognise that AI is reshaping their function. J.P. Morgan’s The CFO View: Asia Pacific Outlook 2026 report found that 44% of leaders in APAC are already using AI for data analytics and forecasting, while 36% are leveraging it to automate routine tasks.
Despite this momentum, demonstrating returns clearly is a separate challenge. The SAP Concur research shows that 38% of finance leaders and 39% of CEOs say it is “too early to tell” whether AI is delivering value, while IT leaders remain the most likely group to report that AI is exceeding expectations (16%). IT teams see operational wins directly, while finance and business heads are still waiting for those wins to translate into metrics they can put in front of boards.
Leaders assess AI through different lenses
The misalignment runs deeper than timing. Over half of CEOs (54%) and finance chiefs (50%) agree that difficulty evaluating ROI is slowing adoption. IT leaders are consistently more optimistic: the average IT respondent reports more than three factors increasing AI returns internally, compared to fewer than three for finance leaders and CEOs.
A governance dimension compounds this gap. In APAC, 33% of respondents identify the CEO as the primary owner of AI strategy, compared to 18% in North America and 8% in Europe. CEO-led AI decisions tend to move faster, but speed without shared measurement frameworks can widen the gap between the teams deploying AI and the executives funding it.
All leaders in the SAP Concur survey rank productivity and time savings, accuracy and quality improvements, and cost savings as the top three factors for evaluating AI. However, IT and finance prioritise risk and compliance outcomes, while CEOs express greater concern about data security and technology vulnerabilities. Customer experience ranks lower across the board, suggesting most AI deployments in finance remain internally focused, and a direct link to commercial value is still missing from the ROI conversation.
The strategic blockers holding back returns
Several barriers are preventing AI from delivering higher returns across APAC. Deloitte’s 2025 Asia-Pacific CFO survey identifies talent constraints (55%) as the top barrier undermining AI deployment across the region, followed by data and technology resource limitations (44%), and risk and governance concerns (39%). These figures align closely with the SAP Concur survey, in which 53% of finance leaders say the benefits of AI are slow to appear, while 51% acknowledge that initial expectations were likely over-optimistic, according to the SAP Concur survey.
Data fragmentation compounds the challenge. According to NTUC LearningHub’s 2026 Industry Insights Report on Financial Services, 31% of Singapore financial sector business leaders cite data fragmentation and quality issues as a barrier to scaling AI, alongside data governance and privacy compliance (34%). Half of APAC financial services firms report that AI is deployed in selected departments only, with a further 20% still in an exploratory or pre-adoption stage. Fragmented deployment makes ROI attribution harder. Globally, ISG’s State of Enterprise AI 2025 report puts this in wider context: 89% of enterprises report AI has improved productivity to some degree, but only 23% can quantify those gains with hard data.
Closing the ROI divide
The building blocks for measurable AI returns are in place; the priority is now getting finance, business, and IT leaders aligned on how to use them.
First, establish a unified ROI framework. Different roles should stop evaluating AI on different scales. A shared dashboard tracking hard ROI (cost savings, time savings, commercial uplift) alongside soft ROI (risk mitigation, accuracy improvements) gives all stakeholders a common language for value.
Second, prioritise time-to-value in use case selection. With 54% of leaders citing slow returns as the primary challenge, balance long-term transformation projects with faster-cycle deployments. Automated expense categorisation, accounts payable processing, and anomaly detection all offer measurable impact within a quarter.
Third, fix your data foundation before scaling. The SAP Concur survey and Singapore financial services research all point to the same conclusion: poor data quality is the single most consistent drag on AI performance. No AI investment case should be approved without a corresponding plan for data governance.
AI ROI is real, but surfacing it requires organisational discipline that investment alone cannot buy. The leaders who will capture it are those who align their teams around shared frameworks, fix their data foundations before scaling, and resist the pressure to declare success before the measurement infrastructure is ready.
The article titled “Why APAC business leaders are still searching for common ground on AI returns” was authored by Fiona Ashley, VP and Head of Spend Product Marketing, SAP Concur
About the author
Fiona Ashley is Vice President of Product Marketing at SAP Concur, where she focuses on the technologies and trends shaping how organisations manage business travel, expenses and spend. She brings deep expertise in travel and expense management, payments and enterprise technology, including the growing role of AI and automation in simplifying processes and improving the employee experience.
With leadership experience spanning travel and spend at SAP Concur, Fiona offers a broad perspective on the evolving needs of businesses, finance teams and travellers. She speaks regularly about the future of business travel and expense management, emerging technology and how organisations can adapt to a rapidly changing spend landscape.