Leaders versus laggards: Who’s winning the AI game?

NTT DATA and CIO.com recently hosted a series of executive roundtables across Australia, India, Malaysia and Singapore to discuss the challenges of taking AI to production. This series also facilitated discussions about partnership strategies to turn AI into a true business differentiator.  

In this blog, I will summarize some of the most important insights to emerge from these roundtables.

AI deployments struggle to keep up with boardroom enthusiasm

By late 2023, IDC had already touted accelerating the “AI pivot” as a focus area for organizations in 2024 and beyond. However, halfway through 2025, barriers remain to monetizing the potential of AI.

IDC research has predicted that “business spending to adopt artificial intelligence (AI), to use AI in existing business operations, and to deliver better products/services to business and consumer customers will have a cumulative global economic impact of $19.9 trillion through 2030 and drive 3.5% of global GDP in 2030.”

It’s no wonder, then, that more than 70% of the senior decision-makers surveyed for NTT DATA’s Global GenAI Report exhibit optimism about GenAI. This positive sentiment is underscored by Foundry’s State of the CIO Survey 2025, which found that CEOs’ top priority for IT is to research and implement AI products and projects.

Yet, full deployment at the enterprise level remains scant.

Due to compliance and regulatory concerns, certain industries, such as banking and financial services, adopt a cautious approach when transitioning AI projects from ideation to scale. Others, such as healthcare, struggle to break down organizational silos to achieve the collaboration and integration needed for AI adoption.

Agentic AI, cyberfusion centers are key developments

In India, IT and security leaders who attended the roundtable agreed on the opportunities AI presents. However, due to compliance and regulatory concerns and the lack of existing use cases, they’re taking a cautious, experimental approach, starting with noncritical data.

Their counterparts in Australia and Singapore echoed this sentiment. Ensuring cybersecurity and keeping up with increasing regulations, especially in the face of shadow AI, are the main bugbears.

Throughout the discussions, agentic AI and cyberfusion centers emerged as key avenues for improving organizations’ ability to detect and respond to fraud and insider threats.

Cyberfusion centers are specialized collaborative hubs where stakeholders such as government agencies, private companies and academic institutions come together to share information, resources and expertise to combat cyberthreats. These centers use AI and machine-learning technologies to improve their ability to detect, analyze and respond to cyberattacks.

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Look beyond cybersecurity challenges

Tom Cooper, Senior Director: Cybersecurity, Australia and New Zealand, at NTT DATA, explained how these advanced cybersecurity strategies not only protect against cyberthreats but also align with business goals, fostering innovation while ensuring data integrity and compliance.

“By streamlining threat detection and response, these strategies reduce operational costs and improve ROI,” he noted. “As organizations scale, agentic AI and cyberfusion centers offer the flexibility and scalability needed to address an evolving threat landscape.”

Challenges extend beyond cybersecurity, however.

Leaders in India also highlighted a lack of skills and infrastructure alongside high costs as key reasons for limited success. In Malaysia, leaders seeking to monetize AI and reap its productivity benefits must address similar challenges in data, skills and infrastructure. This is on top of regulatory concerns.

Meanwhile, organizations in Singapore are struggling to define AI success. They also acknowledged the work required to break the people barrier (think change management and organizational structure), decide on the right AI infrastructure for their business and ensure their data is AI-ready, if they want to monetize AI.

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Partnerships to take AI from aspiration to reality

How do organizations capture AI’s full economic value? It all starts with the necessary IT infrastructure and security strategies to address demanding AI workloads, sophisticated threats and stringent compliance.

Overcoming the knowledge shortage plaguing organizations must start with partnerships. Rather than building everything from the ground up, organizations can accelerate the integration and scaling of AI initiatives more efficiently by collaborating with AI and cloud providers.

However, the nature of partnerships differs from region to region.

During the roundtable series, senior IT decision-makers in Australia said they looked to technology partners to help bridge the gap between what the board wants from a security perspective and the demands of younger employees while complying with regulatory guardrails. AI, cloud and data create evolving complexities that are still unaccounted for; hence, security is paramount.

In India, leaders expect technology partners to guide them closely through their AI journey. This includes support for deploying practical large, medium and small language models, integrating vendor products and implementing agentic AI workflows for autonomous applications.

In Malaysia, where AI adoption is still in its early stages, IT leaders are leaning on technology partners to help identify specific use cases for their organizations and define what success looks like. Their Singaporean counterparts demand the same from their partners, with additional support to realize their vision for step-change innovation across diverse use cases, from hospital imaging to hedging commodities.

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True transformation starts with a shift in mindset

The single biggest challenge that has emerged from these discussions and engagements is this: AI is still perceived as a point problem solver rather than a fundamental transformation opportunity.

Organizations are applying the technology to specific use cases to address well-defined business issues, an approach that may well deliver results in targeted areas but results in many AI initiatives being either myopic or uncoordinated.

But, to deploy, scale and ultimately monetize AI effectively, you need a coherent vision and transformation plan that covers:

• Strategy and governance, with clear frameworks to guide AI adoption and ensure alignment with business objectives
• Talent and culture transformation to develop the necessary skills and foster a culture that embraces AI
• Structural and budgetary adjustments to reflect the new AI-based paradigm

AI is not just a technological revolution; it’s a broader societal and business transformation, which requires a more nuanced discussion on differentiation. Both IT and business leaders, including board members, need to recognize that this changes the discussion from “how to implement” to “how to differentiate.”

AI-savvy supervisory boards have a central role to play in driving and supporting these changes, while expert partners can help organizations bypass common pitfalls to take AI from pilot to production, securely paving the way for tangible business outcomes and sustained innovation.