MUMBAI, Feb 24: FICO Enterprises across Asia are operationalizing artificial intelligence at scale, balancing innovation with governance, risk management, and efficiency to drive business value. More than seven in ten Asian enterprises (71%) surveyed view AI governance as delivering the highest return on investment, outpacing Europe (48%) and Australia/New Zealand (44%), according to a recent survey conducted by FICO and Corinium Global Intelligence: “State of Responsible AI in Financial Services: Unlocking Business Value at Scale”.
Like their global peers, enterprises across Asia are increasingly treating AI as a core business capability. Improving customer experience and driving revenue or market share remain the primary motivations for AI investment. What distinguishes Asia, however, is its emphasis on governance alongside these business outcomes.
This shift is highlighted in the recent joint report that surveyed senior AI and data leaders across five regions; Asia, North America, Europe, Central and South America, and Australia and New Zealand (ANZ). The study shows that organizations in Asia are taking a more structured, risk-aware approach to scaling AI than their global peers, underscoring stronger focus on transparency, control and predictability. Responsible AI standards (63%) and decision intelligence systems (54%) also feature prominently on enterprise AI roadmaps across Asia.
Asia’s Practical Blueprint for AI at Scale
What clearly differentiates Asia, however, is how organizations are operationalizing AI at scale, moving quickly from pilots to production once cost, regulatory, and risk thresholds are proven.
Key Insights
⦁ Efficiency first: 83% of Asian firms cite cost savings and process efficiency as the main catalyst for AI adoption.
⦁ Early business alignment: 13% report AI development as “very aligned” with business goals, compared with none in Central and South America and just 4% in North America.
⦁ Governance gaps remain: 76% say inadequate AI model monitoring is the biggest barrier to scaling AI.
⦁ Risk-led execution: IT, risk and compliance teams are prioritized over standalone data science functions.
The findings are particularly relevant for India, where AI is being deployed at national scale across banking, payments and digital public infrastructure.
“India is deploying AI at a scale few markets can match, across payments, digital identity, credit decisioning and fraud prevention. At that level of adoption, governance, monitoring and explainability aren’t optional. They are fundamental to trust, regulatory confidence and sustainable business performance,” said Dattu Kompella, managing director, Asia Pacific for FICO.
This momentum is also reflected in global benchmarking. In its latest global AI research, Stanford University ranked India third globally in overall AI vibrancy, underscoring the country’s rapid progress in real-world deployment and innovation. As Indian enterprises move from pilot programs to enterprise-wide AI adoption, governance, monitoring and accountability are emerging as critical differentiators in delivering sustainable return on investment.
The Corinium report also highlights organizational challenges that continue to slow AI scale-up across Asia. Resistance to change (71%) remains the most frequently cited barrier, alongside limited collaboration between business and IT teams. Around two-thirds of respondents also point to a lack of shared understanding of AI across the organization, reinforcing the need for stronger cross-functional alignment as AI moves from pilots to enterprise-wide deployment.
Operational realities further shape how AI is adopted and scaled. Asian enterprises report stronger integration of security and reliable data-processing standards into AI operations than peers in Australia and New Zealand or Central and South America. When it comes to responsible AI, adherence to formal standards ranks second globally, behind only North America, highlighting Asia’s relatively advanced approach to responsible AI at scale.
Asian enterprise leaders also place greater emphasis on formal AI model development standards, alongside model monitoring and governance teams, signaling a clear preference for structured, enforceable controls over ethics committees or in-house language models.
“The findings from this report underscore a clear conclusion,” said Kompella. “Unlocking AI’s full value requires not just better models, but stronger standards, smarter systems and deeper collaboration across the enterprise.”