Many organizations in Southeast Asia have implemented artificial intelligence (AI), but its value is limited and focuses primarily on tangible tools such as chatbots and image generation.
Although these initial deployments are accessible starting points, they rarely change business economics or reflect local market realities and strategic constraints.
The region's AI landscape, shaped by low labor costs, fragmented markets, and high trade dependence, challenges productivity-driven strategies and calls for new growth models.
“Southeast Asia has slightly different requirements,” said Mohan Jayaraman, senior partner at Bain & Company.
“Labor costs are much lower than global levels, about 7% of US levels, so productivity gains alone won't increase the bottom line from AI,” he explains.
Bain & Company leverages its experience partnering with companies in the region, its early engagement with OpenAI, and its experience with hyperscalers such as Microsoft, Google, and AWS to develop the Southeast Asia CEO's Guide to AI Transformation, which distills six lessons to help leaders in the region turn the potential of AI into measurable outcomes.
“What we wanted to do was turn this into something that was easy to use, a set of fixed, simple messages that readers could use fairly easily,” Mohan said.
“Companies need to think about the long-term value they get from AI. If you can fundamentally change how the P&L works, that's a great long-term goal to drive.” – Mohan.
More than just “tool implementation”
Many companies still treat AI implementation as a technology implementation rather than a business transformation, and this narrow approach often doesn't deliver the sustainability or economic impact leaders expect.
Rather than squeezing out every last bit of productivity improvement, Mohan said leaders need to consider a more focused strategy by making fewer, bigger bets.
“Take a few areas, but make them even stronger and see if you can drive value. We've seen many organizations overreach and end up with a bunch of proofs of concept that never make it to production,” he explained.
At the same time, leaders must also consider potential vectors of disruption. Changing consumer behavior, disruptive competitors and start-ups, and new business models from emerging technologies can rapidly change market dynamics.
Mohan noted that a regional bank in Southeast Asia identified wealth relationship managers (RMs) as a disruption vector after it was found that they were spending more than 40% of their time on administrative tasks rather than interacting with clients.
“Our bank employs RMs to interact with customers, but much of their time was spent on administrative tasks. So, we introduced an AI agent to reduce these 'unfun' activities and allow RMs to focus more on customers,” he said.
By having agents take over administrative tasks that can be automated, the organization increased productivity by 50% while freeing up the ability to take on more clients and markets.
Mohan noted that during Bain & Company's initial customer rollout, it took about six to seven months for employees to get used to the new tools.
As employees worked on these new use cases, they eventually became better at handling the new tools, and by the third or fourth use case, they were able to move and pivot much faster.
“This speed is [to adapt to new use cases] Technology capabilities have value in and of themselves as they continue to evolve,” Mohan said, adding that it is important for organizations to leverage this capability and use it as a strategic differentiator.
Additionally, to achieve long-term AI scale, companies need strategies that shift the economics of growth, decoupling costs from revenue and impacting scale without inflating operating expenses.
Mohan said companies need to find ways to leverage AI to scale their operations without commensurately increasing expenses, such as through automation, leveraging employees, and new lower-cost business models.
He shared the example of a bank exploring branches with near-zero operating costs by having AI agents handle KYC, document verification, and back-office processes, with the aim of growing the business without increasing headcount.
This approach changes the unit economics of banks and enables financial inclusion where it was not possible before.
“Companies need to think about the long-term value they get from these use cases. Consider using AI to ensure that the cost and revenue curves are separated. If you can make fundamental changes to how the P&L works, that's a great long-term goal to drive,” said Mohan.
Designing AI for customer trust
As organizations increasingly rely on AI-powered customer touchpoints, Mohan emphasized that transparency and structured design are prerequisites for trust.
Mohan said companies cannot simply implement AI systems and assume consumers are comfortable with them, adding that in some cases a human touch is still needed.
“In one of our more complex installations, we have had interactions evaluated live, but when we feel that the consumer-AI interaction is not going in the right direction, we can automatically switch to a human.
“We still hope that we can introduce humans to the ability to go beyond the basic rules set by organizations and either deal with exceptions or simply interact with consumers with empathy,” Mohan said.
On top of that, when a customer interacts with an AI system, it must be disclosed up front to ensure transparency. Customers need to know whether they are talking or chatting with an AI agent.
Mohan added that relevant information, such as how customers can seek redress in the event of an adverse interaction, also needs to be clearly communicated.
“This combines the use of technology with clear business objectives and consumer experience solutions. It's more than just deploying a tool,” said Mohan.
Turn hype into corporate value
While there has been a lot of hype around AI and comparisons to earlier waves of technology, Mohan believes that the impact of AI is playing out differently and the value for adopters is emerging much faster.
He referenced the hype surrounding blockchain about five to 10 years ago, and more recently the excitement surrounding the “metaverse,” and noted that these technologies have not been able to scale value quickly.
“In many previous hype cycles, technology was not in a position to create immediate value. It's not that there isn't value, it's just that value can only be realized over time.
“AI is different. Perhaps you have personally experienced ChatGPT, Gemini or Claude and can understand the value it adds. On a personal level, this is not the same as, for example, blockchain, which is a good technology but does not have the ability to create value quickly,” Mohan said.
“The technology itself works. We all see it. It doesn't matter.”
However, he emphasized that realizing the value of AI will ultimately depend on how well organizations can apply and scale AI.
“This value creation that we are talking about will remain theoretical until companies can actually deliver it, and it will be important to enhance it further,” Mohan said.
Looking to the future, he believes that what will define a company's advantage will be its leadership qualities, its ability to sustain a business transformation journey, and its ability to build an agile organization that can absorb evolving technologies.
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