AI Maturity in Focus: Five Lessons from Executive Roundtables in Malaysia & Thailand

[ikrieger]

Where the Conversation Started

Last week I met with senior leaders in Kuala Lumpur and Bangkok to unpack the 2025 ServiceNow Enterprise AI Maturity Index. The report surveys 4,400+ executives across 16 countries and scores organisations on five dimensions: strategy, workflow integration, talent, governance, and realised value. The headline? The average maturity slipped to 35/100, yet AI budgets continue to climb.

Those figures fueled two intense, pragmatic discussions about what it really takes to move from proof of concept to repeatable value.

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1 — Reality Check: Maturity Has Slipped, but Clarity Has Risen

Most round-table participants admitted they once equated “buying” AI with “being” AI-ready. Twelve months of experimentation exposed hidden weaknesses—fragmented data, legacy workflows, and opaque decision rights. The dip in the index is less a failure than a recalibration: leaders now see the full cost of execution and are budgeting time and talent accordingly.

Take-away: A lower score today often means a more realistic roadmap tomorrow.

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2 — Governance Is the New Growth Engine

In both countries, the loudest theme was trust.

• Data security (21 %) and an AI-governance deficit (15 %) stand out as APAC’s top blockers.
• Only 43% of respondents report having formal data-governance frameworks.

Executives want regionally consistent policy, but they also recognise internal accountability gaps. Questions that dominated the table:

Who owns algorithmic risk?  Who signs off on bias testing?  How do we certify responsible use at scale?

Until roles, guardrails, and escalation paths are clear, leaders are reluctant to push AI into customer-facing processes.

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3 — Mind the Talent (and Structure) Gap

Just 31 % of enterprises feel confident about their AI skills mix, and even that confidence erodes without clear KPIs or oversight. A chief digital officer from Kuala Lumpur admitted, “We hired data scientists, but they’re sitting in silos with no sprint cadence.”

Encouragingly, success stories are emerging. One Australian bank via their internal education programme. AI Education is the most sought-after and viewed training across the organisation. With the uplifted understanding of what is possible, employees are more comfortable with using AI and also reimagining what is possible.

One of the customers explained how they have upskilled their people to leverage their knowledge of what good is to be able to "code" their new AI models, with great success. The surprise was how little education their people needed to get to levels of competence that immediately benefited the business.

Tip: Help your people learn about AI so that they can in turn, help you reimagine what is possible.

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4 — Start Small, Finish Big

Faced with board pressure for quick wins, round-table attendees identified “low-risk, high-proof” arenas:

1. Automated approvals for back-office finance.
2. NLP on case notes to surface root-cause patterns.
3. AI-assisted triage for IT and citizen service requests.

These projects reduce cycle time, free staff hours, and—critically—build organisational muscle for larger, cross-enterprise initiatives. They also help build organisational confidence with AI and what it can do.

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5 — Vision Before Vendor

I closed both sessions with a simple provocation: “If I can’t see your AI vision statement, you don’t have an AI strategy—just a shopping list.”

The organisations scoring highest on the index’s “Pacesetter” tier (average 44/100, top score 57.9) share three cultural traits:

• Executive-endorsed North Star linking AI to mission.
• Platform-first architecture that unifies data, workflows, and GenAI services.
• Standing governance council that meets often

Vision anchors every budget line, talent plan, and risk conversation.

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A Practical Five-Step Roadmap

Step	What Good Looks Like	First 90-Day Action
1. Co-create the Vision	3-year targets tied to hours saved & revenue	Run a C-suite workshop; publish a one-page AI charter
2. Platform-First Data Fabric	Leveraging extensible platforms, expanding through APIs, metadata, and observability	Audit system overlaps; prioritise integrations that unblock AI use-cases
3. Governance “AI” Council	Risk, ethics, and product in one forum	Draft RACI; appoint an executive chair
4. Talent Flywheel	Skills inventory → academy → hackathons	Launch a business-developer cohort; set win metrics
5. Value Tracking	Live dashboard of ROI & risk	Shift OKRs from “features shipped” to “hours given back”

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Closing Thoughts

The journey to AI maturity is rarely linear, but it is navigable. With a shared vision, disciplined governance, and a talent strategy that elevates every employee, organisations can convert AI from an expense line to a growth engine.

I’m continuing these conversations across APAC. Where does your organisation sit on the AI Maturity curve—Explorer, Experimenter, Accelerator, or Pacesetter? 

Let’s turn today’s AI hype into tomorrow’s sustainable value.