Accenture foresees GenAI investment growth in Asia Pacific

“Research we conducted at the beginning of the year shows that 95% of CXOs in APAC are planning to increase AI investments in 2025, with a focus on driving growth,” says Ryoji Sekido, Co-CEO Asia Pacific and CEO Asia Oceania at Accenture.

As businesses continue to manage expectations in their AI investments, Accenture is ensuring that it continues to be part of the conversations and plans businesses are making for the future as well. While the company did report new Gen AI bookings of US$1.4 billion globally, there are concerns on how cost-cutting plans by the Trump Administration could impact them.

Despite this, for its second fiscal quarter 2025, which ended February 28, Accenture reported total revenue of US$16.66 billion, an increase of 5 percent over its second fiscal quarter 2024 revenue. That revenue was almost divided evenly between the company’s two major businesses, with consulting revenue of US$8.28 billion, up 3 percent, and managed services revenue of US$8.38 billion, up 8 percent.

In terms of innovation, Accenture is developing over 50 industry-specific AI agent solutions, which leverage new NVIDIA reasoning models. Built using NVIDIA AI Enterprise, these solutions streamline processes and enhance efficiency, and cover industries like telecommunications, financial services, insurance, and more. Accenture hopes to have 100 industry-specific AI agent solutions developed by the end of the year.

In the Asia Pacific region, Accenture continues to be a dominant player. The company continues to partner with enterprises in their AI journey. Ryoji Sekido, Co-CEO Asia Pacific and CEO Asia Oceania and Vivek Luthra, Data & AI Lead, Accenture in APAC share more about Accenture’s progress in the region as well as their plans for the year.

There have been reports with businesses prioritizing ROI when investing in AI in the Asia Pacific region. What’s your advice to businesses?

Sekido (pictured above): Being focused on ROI is important for any investment - technology or otherwise. Equally important is to also take a strategic view and look beyond short-term returns. Our research shows that organizations committed to business reinvention and investing in both no-regret moves and strategic bets create greater business impact. For example, a leading electronics major redesigned its operations using intelligent AI tools and saw a 70% improvement in its ability to serve customers via chat.

While GenAI has sped up AI adoption, it is still in its early stages. To truly accelerate, organizations must invest in both technology and talent. Currently, 30% of APAC C-suite leaders cite "data or technology infrastructure limitations" as the top obstacle to implementing and scaling GenAI but acknowledge that building a secure, cloud-based digital core is crucial. Talent is a real game-changer in this regard. Preparing and transforming the workforce with GenAI will be the key differentiator, yet not enough organizations are prioritizing this. In fact, GenAI budgets have three times more allocated to technology than to people—this needs to change.

How mature are APAC companies when it comes to AI development and deployment?

Sekido: GenAI has been pivotal in making AI mainstream, and in 2024 we saw a steep rise in companies experimenting with AI. But after 12 months of rapid adoption, we’re seeing a gap between ambition and impact—only 36% of APAC leaders have scaled gen AI solutions, and just 13% report significant enterprise-level value. We see most countries displaying a similar level of interest and adoption with certain industries and functions leading the charge. Consumer-facing industries such as banking, retail and telecommunications are responding to consumer demand for new and personalized services, with functions like operations, technology and risk and compliance are using this to optimize processes.

In terms of pricing and usage of the technology, does Accenture foresee a change in the costs and investments companies pour into AI in the second half of 2025?
Sekido: Research we conducted at the beginning of the year shows that 95% of CXOs in APAC are planning to increase AI investments in 2025, with a focus on driving growth. An equal number anticipate using AI agents in the next 3 years.

What does responsible AI mean for Accenture and are organizations challenged in adopting responsible AI deployment?

Luthra (pictured above): Responsible AI requires taking intentional actions to design, deploy and use AI to create value and build trust while protecting from potential AI risks. It involves embedding governance frameworks, conducting rigorous risk assessments, and ensuring that AI use aligns with ethical and regulatory standards. Like any strategic initiative, the responsible use of AI is about having the intent and a well-executed plan.

Our research shows that majority of businesses in APAC demonstrate intent and planning for responsible AI – what we term ‘organizational readiness’– but only 1% have fully operationalized responsible AI. In terms of execution, significant gaps persist in compliance, workforce readiness, and risk mitigation measures. Many organizations still view responsible AI as a compliance exercise rather than an enabler of business value, delaying full adoption.

How is Accenture working with its partners and regulators on the development of responsible AI?

Luthra: Accenture collaborates with policymakers, industry leaders, and regulatory bodies to shape responsible AI frameworks that balance innovation with compliance. This includes co-developing AI governance models, operationalizing AI risk management strategies, and supporting businesses in aligning with evolving AI regulations across APAC.

For example, we are working with the Monetary Authority of Singapore in bringing financial institutions under the Veritas consortium to advocate adoption of responsible AI. We also collaborate with Amazon Web Services (AWS) to help organizations scale adoption of AI responsibly. This includes Validation solution(s) for specific types of risk and on-going performance management of these AI solutions in production.

As AI requires heavy compute power, where does sustainability fit into the equation, especially with more organizations investing in the technology?

Luthra: The environmental cost of AI can be significant, and organizations need to implement a principled approach to reducing the carbon footprint by adopting green data practices, optimizing hardware, and selecting the right models. For example, companies need to adopt a lifecycle approach to AI development which includes selecting the most appropriate AI technique for each task, with simpler predictive AI often being more efficient.

Organizations need to take three key actions:

Rethinking involves selecting efficient models and pruning LLMs to decrease energy consumption.
Reusing models through transfer learning can significantly cut costs and emissions.
Reducing involves making architecture choices that yield faster throughput and energy savings, such as using GPUs and TPUs over CPUs.

By integrating these strategies, organizations can maximize the value of AI while minimizing its environmental impact, ensuring that the technology supports sustainability goals.

With additional reporting by Joseph F.Kovar