Southeast Asian companies are not short of money for AI. Most of them simply can’t get their projects past their own IT and security teams.
That is the uncomfortable picture painted by the Southeast Asia findings of Adobe’s 2026 State of AI Readiness Report, released today. The study surveyed 461 senior executives in the region and found that 73 per cent of companies are increasing their AI investment year-on-year. Yet 48 per cent are still working on their first AI use case, or have given up after a failed attempt.
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In other words, budgets are going up while results are standing still.
About 57 per cent of organisations are stuck in what the report calls the “Frozen Middle”. They are piloting or deploying AI without the coordination needed to scale it. For agentic AI, meaning systems that can carry out multi-step tasks on their own rather than just answer prompts, the figure is 40 per cent. Some 79 per cent of the region’s companies sit at an early stage of AI readiness, the highest share anywhere in Asia Pacific.
The findings echo a theme that keeps resurfacing on these pages: Southeast Asia doesn’t have an AI adoption problem, it has a scaling problem.
Where the good ideas go to die
The most striking number in the report is the attrition rate. Only 15 of every 100 AI ideas make it into production.
The biggest losses do not happen in the lab or the boardroom. About 36 per cent of AI proposals fail to get past IT evaluation. A further 18 per cent are rejected at security review, and that rejection rate nearly doubles in highly regulated industries such as banking and healthcare.
Read one way, that is a story about bureaucracy strangling innovation. Read another, it is a story about gatekeepers doing their jobs. Many AI proposals arrive at IT with no clear data plan, no security model and no owner. e27 has previously reported on the hidden risk of unchecked agent privileges, and a security team that waves every agentic pilot through would be failing at its own job.
The more useful question is why so many proposals reach IT in a state that deserves rejection. The report’s answer is that nobody owns them.
The ownership vacuum
Only 12 per cent of Southeast Asian companies have an AI owner with budget authority, meaning someone who can both make decisions and pay for them. Among the companies Adobe classifies as Leaders, that figure is 89 per cent.
Measurement is just as thin. Only 8 per cent of companies measure AI return on investment comprehensively, and most track usage (how many people logged in) rather than quality (whether the output was any good). Meanwhile, 81 per cent of businesses say their AI skills shortfall stayed the same or got worse over the past year. That is consistent with what we have seen in Malaysia’s AI skills gap and elsewhere in the region.
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“Organisations that establish clear ownership, shared measures of success and stronger collaboration between business and IT will be best placed to move beyond isolated pilots and achieve sustainable impact,” said Jeremy Willmott, Senior Director, Digital Strategy Group Asia Pacific at Adobe.
It is a sensible prescription, if not a new one. Many consultants have offered similar advice, and e27 contributors have argued that Singapore’s AI adoption problem is not worker resistance but weak execution.
What the Leaders do differently
The Leaders are a very small club: 6.3 per cent of Southeast Asian respondents, or just 27 organisations. They have scaled AI across multiple workflows and built an operating model to sustain it. According to Adobe, they convert pilots to production at nearly four times the rate of other companies.
They also outscore the regional average by 67 per cent on ownership and accountability, and by 59 per cent on embedding AI into everyday processes. They keep their “destructive failure” rate, failures that cause real damage rather than lessons, below 1 per cent. By contrast, more than a third of companies repeat the same AI mistakes. And 41 per cent of Leaders say their single most effective move was funding the employees who were already making AI work.
That last finding is perhaps the most practical in the report. The fastest route to scale often runs through the staff member who quietly built a working tool in a spreadsheet, not through the vendor’s pitch deck. It fits other regional research showing that workers are sprinting ahead of their bosses in AI adoption.
Read the fine print
Before boards rewrite their AI strategies around these numbers, a few caveats are worth noting.
First, the sample. The research was conducted by Incisiv on Adobe’s behalf. It covered only organisations with at least US$100 million in annual revenue, 60 per cent of which earn more than US$1 billion. Startups and the SMEs that make up most of Southeast Asia’s economy are not in the picture.
Second, the Leaders’ statistics rest on just 27 companies in the region. When the report says 89 per cent of Leaders have a budget-holding AI owner, that comes to roughly 24 companies. That is enough to suggest a pattern, but not enough to prove it.
Third, the headline conclusion is partly built into the method. The report says organisational readiness, not technology or spend, is the strongest predictor of whether AI scales. However, Leaders were defined in the first place by combining deployment speed with “operating model maturity”. Finding that Leaders have mature operating models is therefore close to circular. The more interesting finding is the negative one: globally, company size, industry and reported AI spend did not predict Leader status.
Finally, Adobe sells AI tools to enterprises. It has an obvious interest in persuading companies that the obstacle to AI value is organisational, not technological. The company’s own earlier research found GenAI adoption rising in Asia while ROI remained elusive. This year’s report reads like the sequel.
Also Read: Why most enterprise AI in APAC is still stuck in the proof-of-concept room
None of that makes the diagnosis wrong. The pattern of rising spend, stalled pilots and nobody in charge matches what founders selling into Southeast Asian enterprises describe privately. Telcos and universities are now setting up joint efforts, such as Singtel’s RE:AI tie-up with the SIT-NVIDIA centre, specifically to close the deployment gap.
For B2B AI startups, the lesson is blunt. The buyer who signs the pilot is often not the person who can take it to production. Until someone with a budget owns the outcome, the region’s AI spending will keep rising faster than its returns.
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