Why 57% of SEA Marketers Call Themselves Advanced in AI But Still Lag on ROI

Key Takeaways:

1. 57% of Southeast Asian marketers call themselves “advanced” AI adopters, but this measures function coverage, not proven ROI.
2. Skills and training gaps, cited by 78% of organisations, are the biggest barrier to scaling AI properly, ahead of budget or technology.
3. Only 44% of advanced adopters have a formal AI governance framework, and that drops to 21% for early adopters, leaving most AI decisions unchecked.
4. Most “advanced” AI use sits in content creation and analytics; only around 30% extend AI into revenue-critical decisions like media allocation and measurement.
5. Poor data quality is the leading cause of AI project failure in the region, cited by 40% of companies, ahead of privacy or access issues.

More than half of Southeast Asian marketing teams now say they are “advanced” AI adopters. But most of them still cannot prove that AI is making money back. The gap is not about tools. It is about people, governance, and how deep AI actually sits inside real business decisions.

The Numbers Tell Two Different Stories

A 2026 study by MMA and Decision Lab surveyed 143 marketing leaders across Vietnam, Indonesia, the Philippines, Thailand, and Singapore. The result: 57% of organisations had reached “advanced” AI adoption. Thailand led the region at 84% adoption, with Indonesia and Vietnam close behind.

That sounds like strong progress. But a separate McKinsey report on AI in Southeast Asia found a very different picture underneath the headline number. More than six in ten companies allocate between 11% and 40% of their technology budget to AI. Yet around 60% of them see less than a 5% impact on their earnings. Almost one in five see no financial impact at all.

Only about 6% of organisations globally are capturing real value from AI, defined as an 11% or higher boost to earnings. Everyone else is spending on AI without a matching return.

This is the core problem behind the headline: advanced adoption does not equal advanced results.

What “Advanced” Actually Means in This Report

The MMA and Decision Lab study did not measure “advanced” by how many AI tools a company owns. It measured how deeply AI is used across different marketing functions at the same time.

Most companies that call themselves advanced are only using AI for two things:

• Content creation (54% of advanced adopters)
• Analytics and reporting (41% of advanced adopters)

Very few are letting AI touch the decisions that actually move revenue. Only 33% of advanced adopters use AI for media allocation, compared to just 10% of early adopters. Only 30% use it for measurement and attribution, compared to 13% of early adopters.

In other words, most “advanced” teams use AI to write faster and read reports faster. Very few let it decide where the ad budget goes or how success gets measured. That is where the real commercial value lives, and that is exactly where adoption is thinnest.

The Skills Gap Is the Biggest Blocker

Across all five markets studied, the top barrier to scaling AI is not budget or technology. It is people. Skills and training were named as the primary challenge by 78% of organisations. A further 34% admitted that AI is still poorly understood inside their own company.

Training makes a measurable difference. Among advanced adopters, 63% run formal AI training programmes. Among early adopters, that figure drops to just 36%.

This shortage is not unique to marketing. A regional SAS study found that a lack of specialised, skilled personnel was cited by 41% of Southeast Asian companies as a top challenge to AI adoption, alongside cost management (30%) and unclear evaluation criteria for AI tools (29%).

For B2B teams running campaigns across several Southeast Asian markets at once, this gap gets harder to close. A marketer needs to understand performance fundamentals, local language nuance, and AI tooling all at the same time. That combination of skills remains rare across the region. Getting AI translation service for business content right, for example, requires a workforce that can judge when AI output is good enough and when it needs human review, not just someone who knows how to prompt a tool.

Governance Has Not Kept Pace With Adoption

Here is the statistic that should worry any B2B marketing leader: only 44% of “advanced” AI organisations in Southeast Asia have a formal AI risk governance framework. For early adopters, that number drops to just 21%.

This means that nearly half of the region’s most AI-mature organisations are running campaigns, content, and decisions through AI systems with no formal way to check accuracy, bias, or compliance. Governance is not a side issue. It shapes whether leadership trusts AI output enough to let it touch bigger decisions like budget allocation.

Data quality compounds this. In the SAS study, the top reason cited for AI failure was untrustworthy or poor-quality data, named by 40% of respondents. Privacy or compliance limits followed at 38%, and restricted data access at 36%. If the data feeding an AI system is unreliable, no amount of tooling will produce a trustworthy result.

For any brand publishing across markets, this links directly to quality control. Using AI translation accuracy rate in 2026 benchmarks as a reference point helps teams set realistic expectations for what AI can and cannot verify on its own, especially for customer-facing content in multiple languages.

Consumers Are Moving Faster Than Brands

While internal teams debate governance, buyers are already changing behaviour. Research from Braze found that 14% of consumers already use AI agents to interact with brands and make purchases. That figure is expected to almost triple, reaching 37% by the end of 2026.

But trust is not automatic. 27% of consumers refuse to share any data with AI agents, even when promised a better experience. This has been called the “trust plateau.” The barrier is not the technology itself. It is whether people trust how that technology handles their information.

For B2B brands, this matters more than it seems. Buyers researching vendors, comparing services, and reading content in their own language are already forming judgements based on how trustworthy and well-localised that content feels. A poorly translated website or a generic AI-written page can quietly cost a deal before a salesperson ever gets involved.

Scale Also Depends on Company Size

Bigger companies are pulling ahead faster. Among firms with revenue above US$250 million, 56% have reached scaling or fully scaled AI adoption. That drops to 47% for mid-sized firms and 42% for smaller businesses. Bain & Company separately found that fewer than 20% of the Southeast Asian companies it works with are meaningfully scaling their AI investments at all.

This creates a widening gap. Larger enterprises can afford dedicated AI teams, formal governance, and ongoing training. Smaller and mid-sized B2B firms, which make up the vast majority of businesses in the region, often cannot. Without support, these firms risk falling further behind on both adoption depth and measurable ROI.

How B2B Brands Can Close the ROI Gap

The good news is that none of these barriers are permanent. They point to a clear set of actions.

• Build AI literacy before buying more tools. Training is the single strongest differentiator between advanced and early adopters. A team that understands what AI can and cannot do will make better decisions than a team with more software licences.
• Put governance in place before scaling. A basic AI risk framework, covering data privacy, accuracy checks, and clear ownership, should exist before AI touches customer-facing decisions. This is far easier to build early than to retrofit later.
• Move AI beyond content creation. If AI is only writing blog posts and building reports, it is not yet contributing to ROI in a way leadership can defend. Extending AI carefully into measurement and media decisions, with human oversight, is where the commercial upside actually sits.
• Treat data quality as a foundation, not an afterthought. Poor data quality was the top reason AI projects failed across Southeast Asia. Clean, well-structured data should come before any AI rollout, not after.
• Localise with care, not shortcuts. Buyers across Southeast Asia read, search, and buy in different languages and different cultural contexts. Relying on raw AI translation without human review, especially for digital marketing translation aimed at customer-facing pages, risks the exact trust problems consumers already report. Combining AI speed with human judgement, sometimes called machine translation post-editing, protects both accuracy and brand voice.
• Choose the right SEO and localisation approach for each market. ROI in Southeast Asia depends on being found in local search first. Reviewing your multilingual on-page SEO services in Asia setup, and following how to translate your multilingual website and increase traffic guidance, ensures AI-generated content is actually reaching the right audience in the right language, not just existing on a page nobody local can find.
• Work with partners who understand hybrid AI-human workflows. Businesses that pair AI speed with human review consistently report stronger trust outcomes than those relying on AI alone. This is the model behind a reliable AI translation agency in Asia, and it applies just as well to marketing content as it does to formal documents.
• Vet any external help carefully. For B2B teams entering multiple Southeast Asian markets, following the complete guide to choosing a multilingual SEO agency for Southeast Asia helps avoid the common mistake of treating one market’s strategy as a template for all five.

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