CIO Summit / Knowledge Hub

The world is entering a defining moment for digital infrastructure. Artificial intelligence has moved from experimentation to ubiquity, and with it, a new operational paradigm is taking shape — one where agents rather than applications become the primary engines of digital value creation. This is the dawn of the agentic AI era, and its success depends on one thing above all else: robust, intelligent, and scalable infrastructure.

It has become a common refrain – getting data governance right is key to a successful AI strategy! This conventional wisdom is very true, but it is not a new problem. For as long as I have been involved in IT, both as an analyst and as a CIO, companies have struggled with wrangling the various data sets across the applications running at the organization.

Driving AI success requires more than technology—it starts with getting your data ready: clean, governed, and accessible. Equally important is selecting an AI platform that aligns with business goals while ensuring architectural agility to integrate emerging technologies seamlessly. While partnerships with technology providers remain essential, organizations should design for interoperability rather than rigid dependency.

Beyond technology choices, true value comes when businesses are willing to re-engineer processes to fully leverage the strengths of generative AI and traditional AI. This combination of data readiness, platform strategy, architectural flexibility, and process transformation positions enterprises to innovate faster and adapt confidently in an evolving AI landscape.

We are witnessing one of the most profound turning points in modern technology: the “ChatGPT moment” for robots. AI is no longer confined to screens or datasets, it’s spilling into the physical world. With advanced autonomy, embodied AI, and robotics accelerating at unprecedented speed, entire categories of work are being reshaped. As automation amplifies, even highly educated Gen Z talent is struggling to keep pace with a labour market where traditional roles are disappearing faster than new ones emerge.
Yet I believe this transition brings as much opportunity as disruption. AI has the potential to multiply human capability, not just replace it. The leaders of tomorrow won’t succeed by doing more tasks, but by mastering the art of leading, delegating, and orchestrating systems that extend human capacity. This requires an entrepreneurial mindset at scale.

Nowhere is this more visible than in APAC, where adoption is fast, pragmatic, and quietly bold. If this momentum continues, APAC could reshape global power dynamics, positioning the region not just as a fast follower, but as a defining force in the new world order.

Are we in an AI bubble? This is the question everyone is asking!

I would submit it’s not a binary yes or no answer but more nuanced in terms of the advancement and adoption of any technology, of which Gen AI has by far shown the most promise and surpassed expectations when we compare versus the hype witnessed with the preceding technologies like the Blockchain or Metaverse. That said, I do believe Amara’s Law applies – Amara’s Law states that “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run,” highlighting the often-misguided expectations surrounding technological advancements.

With this axiom in mind, lets approach 2026 with optimism and pragmatism. Fundamentals of data foundation, engineering, stewardship, security and resiliency remain paramount even as we leverage the LLM’s to build applications that deliver value and transform the business. All the best to all participants and look forward to an insightful summit this year.

The current landscape of Artificial Intelligence is a tug-of-war between inflated expectations and existential fear. However, the future isn’t a choice between humans or machines; it is a collaborative world driven by a mix of human intelligence and algorithmic engines.

Customer experience is undergoing a foundational shift. AI is no longer a set of isolated experiments or incremental improvements. It is becoming the operating model for how service is designed, delivered, and scaled. Customers now expect interactions that feel instant, relevant, and connected, and they benchmark every experience against the best they’ve ever had.

Across Asia Pacific, many organizations continue to treat cybersecurity as a control layered onto digital growth. That model is no longer sufficient. Rapid cloud adoption, regional expansion, and cross border data exchange have permanently shifted the risk landscape. Security must now shape how work is designed, delivered, and governed.

To truly capture the return on AI investment, we must pivot from counting tasks to measuring the wins that matter.

There is incredible energy in our workforce right now. If you look at the dashboards, the numbers are climbing. Adoption of new tools is skyrocketing, and people are moving faster than ever before.

But capability is not the same as impact.

For the last few years, we have been laser-focused on gross efficiency—how many tasks we check off, tickets we close, and code we ship. We have become excellent at volume. But in an AI-driven world, raw speed is just the baseline.

To unlock the untapped potential of our teams, we must pivot to a new metric. We need to stop asking how fast did we finish? and start asking how well did we solve it?

As digital transformation accelerates, organizations are confronting a growing strategic imperative: establishing digital sovereignty. More than a regulatory concept, digital sovereignty represents the ability of enterprises and nations to control and protect their data, infrastructure, and software in accordance with local laws, values, and operational requirements. In practice, it means understanding precisely where data resides, who has access to it, and how technological dependencies influence business continuity, competitive advantage, and customer trust.

In banking and finance, insurance, fintech, telecommunications, retail and several other enterprises, customer and business documents rarely live in one place. Often overlooked as responsibility of individual business teams, it is spread across legacy ECM systems, collaboration platforms, cloud drives, shared folders, FTP servers, scanned archives, and the databases of line-of-business applications. Over time, this creates a familiar pattern: rising maintenance costs, duplicated information, slower processes, fragmented search, data leak risk and growing compliance exposure. In most cases, the customer and business documents live for forever due to lack of control on federated silos and blind spots in data governance.

The emergence of generative AI (GenAI) has significantly lowered the barrier for enterprise technology adoption, rapidly demonstrating value in areas such as content creation and task automation. In Singapore, AI has now moved beyond experimentation to become a national priority for economic transformation.

Recent initiatives signal a decisive shift toward execution. The establishment of a National AI Council chaired by the Prime Minister, the launch of National AI Missions across sectors such as manufacturing, finance, healthcare, and connectivity, and the development of an AI Park at one-north reflect Singapore’s ambition to accelerate real-world deployment of AI technologies.