AI Governance Provides Guardrails for Faster Innovation
As generative AI (GenAI) becomes increasingly adopted across the Asia-Pacific region, many organizations, particularly in unregulated industries, hesitate to implement governance frameworks. This hesitation stems from a fear that red tape will stifle innovation. However, experts argue that establishing clear boundaries is essential for accelerating progress.
The Importance of AI Governance
Grant Case, a field chief data officer for Asia-Pacific and Japan at Dataiku, emphasizes that while the region leads in GenAI usage, eroding user trust in AI outputs can derail initiatives. Establishing governance guardrails can help bridge this trust gap. Contrary to the misconception that governance slows down AI projects, Case notes, “The organizations in this region that are moving the fastest are the ones that have already established a strong AI governance stance.”
He compares AI governance to highway safety barriers: just as barriers allow vehicles to travel safely at higher speeds, governance provides the confidence needed for rapid development. “To move faster, you need to understand where the boundaries are,” he explains. By setting parameters early, organizations eliminate hesitancy around innovation, empowering teams to know what is permissible.
Addressing Shadow AI
The rise of shadow AI is a significant driver for establishing governance. Shadow AI occurs when employees use unapproved AI tools, often exposing corporate data to large language models (LLMs). Case highlights that 77% of security professionals have observed this behavior, which is typically not malicious but rather a result of inadequate internal tooling.
Employees often resort to external AI tools due to a lack of frictionless access to internal alternatives. Case suggests that the solution is not to ban these external tools but to provide integrated internal options that adhere to governance protocols. “We want the governed path to be the fast path and the right path,” he states, echoing a philosophy shared by a banking client.
The Role of Governance in Corporate Strategy
AI governance discussions initially centered around chief data officers (CDOs) but have now reached corporate boardrooms. This shift is fueled by the security risks and spiraling costs associated with unregulated AI experiments. Case recounts an example of a client whose business unit incurred unexpected costs—initially starting a $3 million AI project, the board recently questioned monthly expenses that soared to $47,000 without clear ROI.
As a result, finance and internal audit teams are becoming more involved in AI governance to manage both technical and financial risks. Some companies have even begun building their own LLMs for governance and localization; however, Case warns against this approach. The rapid pace of technological change can render internal projects obsolete before they reach completion. He cites an instance where a high-level analytics officer spent six months developing an internal LLM, only to find it less effective after a commercial update from OpenAI.
A Platform Approach to AI Governance
Instead of developing internal models, Case advocates for a platform approach where governance requirements, such as those mandated by the EU AI Act, are integrated into the infrastructure. This allows companies to easily incorporate the latest models while remaining compliant. “The value of a platform like Dataiku is that we integrate the latest technology for you,” he explains. This enables teams to utilize the best tools available rather than attempting to build something that may quickly become outdated.
Future of AI Spending
According to Gartner, global spending on AI is projected to reach $2.52 trillion in 2026, marking a 44% year-over-year increase. Investments in AI platforms for data science and machine learning, such as Dataiku, are also set to grow from $21.9 billion in 2025 to $44.5 billion in 2027.
John-David Lovelock, distinguished vice-president analyst at Gartner, notes, “AI adoption is fundamentally shaped by the readiness of both human capital and organizational processes, not merely by financial investment.” Organizations that prioritize proven outcomes over speculative potential are likely to be the most successful.
AI Adoption in the Asia-Pacific Region
In the Asia-Pacific region, companies are making significant strides in AI adoption. For instance, Australia’s Woolworths Group plans to enhance its digital shopping assistant, Olive, using Google Cloud’s new agentic AI platform, Gemini Enterprise for Customer Experience. Additionally, research from Thoughtworks indicates that while 77% of global businesses focus on generating revenue from AI initiatives, Asian markets are leading in agentic AI adoption, job creation, and executive confidence.
Microsoft is also expanding its AI footprint in India by partnering with four of the country’s largest IT services companies to deploy agentic AI capabilities across enterprises. Furthermore, AI is expected to handle nearly half of all customer service interactions in Singapore within the next two years, although businesses could risk alienating customers if they fail to adequately explain how the technology operates.
