Original research · The full report
How Asia-Pacific founders actually use AI in 2026
The complete findings from founders and business leaders across 15 markets: how deep AI runs, what they pay, which tools they reach for, and what they get back.
By Dhawal Shah · dhawalshah.net
A note to respondents
Thank you for the data
If you took this survey, this report is the promise kept. You gave honest answers about how your company really uses AI, what it costs you, and what is genuinely hard about it. In return, here is the full picture you helped build, with nothing held back behind a paywall.
What follows is the complete read: who responded, the headline findings, every chart, the patterns that show up when you cut the data by company size and market, and the obstacles you named in your own words. Where a segment is too small to be precise, it is labelled as directional rather than dressed up as a headline number.
Want to slice it yourself, or download a copy to keep? Both are at the end of this report.
At a glance
Six things the data says
Across founders and business leaders in 15 Asia-Pacific markets, 83% use AI regularly or have it embedded across the business. Most spend little to do it: 77% are at $500 a month or less. ChatGPT (87%), Gemini (74%) and Claude (73%) lead the tools, 71% report time savings, and 89% plan to use more AI this year.
83%
use AI regularly or have it embedded
AI is part of daily work, not a parked experiment.
77%
spend $500 a month or less
Deep adoption on shoestring budgets. 44% are under $100.
87% / 74% / 73%
ChatGPT, Gemini, Claude
A three-horse race, not a one-tool region.
71%
see time savings; fewer see revenue
Only 17% report a revenue lift from AI so far.
53%
name cost as the top obstacle
Even though most spend little. 32% see no real obstacle at all.
89%
plan to increase AI use this year
64% significantly. Not one company plans to cut back.
Who responded
A founder-led, small-company sample
91% of respondents are founders or co-founders, and 73% run teams of 15 or fewer. Read this as a clear picture of early-stage and small-business AI use in Asia-Pacific, not of large enterprises. Three markets, Bangladesh, Singapore and Indonesia, carry enough responses to stand on their own; the rest are part of the regional whole.
By market
By company size
By sector
Finding 01
Deep adoption, shoestring budgets
These companies have woven AI deep into how they work. 52% say it is embedded across multiple areas of the business, not parked in one experimental corner. Yet 77% pay $500 a month or less for it, and 44% spend under $100 or stay on free tiers entirely.
The contradiction is in what they call hard. Cost is the single most-cited obstacle at 53%, even though almost nobody is spending much. For this group AI is still a discretionary line to keep small, not an investment to scale up. At the same time, 32% report no significant obstacles at all.
Depth of AI use
- Embedded across the business52%
- Regular in one area31%
- Experimenting14%
- Aware, not started2%
- Not using, no plans1%
Monthly AI spend
- $0 (free tiers only)13%
- Under $100 / month32%
- $100–$500 / month34%
- $501–$2,000 / month13%
- Over $2,000 / month8%
Share of those who named a spend band.
Embedded adoption rises with team size
Share of each size band with AI embedded across the business. Larger small-teams adopt more deeply, not less.
Finding 02
The frontier is a three-horse race
ChatGPT still leads at 87%, but it is not running away with the region. Gemini (74%) and Claude (73%) sit right behind it, the three of them inside 14 points. In Asia-Pacific small companies, Google has caught up to the field rather than trailing it.
There is a gap lower down the stack. 68% use AI for software development, but only 32% reach for a dedicated coding tool like Copilot or Cursor. Most of that building still happens in a general chat window, not a purpose-built assistant.
By market, the picture shifts: Claude runs strongest in Bangladesh and Thailand, while ChatGPT is near-universal in India and Indonesia. The table shows the three leaders across the markets large enough to report.
Tools used in the past 30 days
Respondents could select more than one tool.
| Market | % of responses | ChatGPT | Gemini | Claude |
|---|---|---|---|---|
| Bangladesh | 21% | 84% | 68% | 80% |
| Singapore | 21% | 88% | 72% | 76% |
| Indonesia | 17% | 90% | 65% | 60% |
Markets shown carry 15 or more responses each. Smaller markets (Thailand, India and others) point the same way but are too small to report as precise figures.
Finding 03
Where AI shows up first
AI enters these companies through the front office before the back office. Content and marketing leads at 77%, with software development close behind at 68%. Customer support and recruiting sit at the bottom, the areas where trust and accuracy matter most and where founders are slowest to hand work to a model.
Respondents could select more than one area.
Finding 04
Productivity is here. Revenue is not, yet.
The wins these companies report are about getting work done faster. 71% point to time savings for the team and 66% to faster product development. Higher-quality output follows at 50%.
Top-line impact is a different story. Only 17% report an increase in revenue from AI so far. That is the classic early-curve pattern: efficiency lands first, and revenue is the harder, later prize. It also explains why so many founders frame AI as a cost rather than a growth lever.
But depth changes the maths. Companies with AI embedded across the business are five times as likely to report a revenue lift as those who use it only in one area, and they pull ahead on every other result too.
Results seen from AI
Respondents could select more than one result.
| Result | Embedded | Regular in one area |
|---|---|---|
| Significant time savings | 89% | 81% |
| Faster development | 84% | 72% |
| Higher quality work | 64% | 56% |
| Lower operating costs | 52% | 33% |
| Increase in revenue | 30% | 6% |
The depth dividend: going from regular use in one area to embedded across the business roughly doubles reported cost savings and multiplies the odds of a revenue lift.
Finding 05
What is actually in the way
Cost tops the list at 53%, followed by data privacy and security at 31% and a team skills gap at 21%. Notably, 32% say there are no significant obstacles at all, the second most common answer.
The obstacles that rank lowest are telling. Integration friction and unclear ROI sit near the bottom, which fits a group that has already adopted: the hard part is no longer getting started, it is keeping up, staying accurate, and turning use into results.
Read together, the top two answers tell one story. The group that names cost as the biggest barrier is also the group spending the least, while nearly a third report no real barrier at all. The obstacle is rarely the tool itself. It is the decision to treat AI as a budget line worth growing.
53%
name cost as their biggest obstacle, yet 44% spend under $100 a month. The barrier is mindset, not price.
Biggest obstacles to adoption
Respondents could select more than one obstacle.
Finding 06
The direction is one-way
89% plan to increase their use of AI over the next 12 months, and 64% plan to increase it significantly. The remainder expect to hold steady. Not a single company in the sample plans to cut back.
For a group that already calls itself a deep adopter, this is the more striking number. They are not at a ceiling; they see headroom. The spending restraint of today is a starting point, not a settled budget.
12-month AI outlook
- Plan to increase significantly64%
- Plan to increase slightly25%
- Expect to stay the same11%
Benchmark yourself
How does your company compare?
Use this as a quick self-check. The middle column is the typical Asia-Pacific small company in this sample; the right column is the supporting figure. Pick your own answer in the last column, then turn it into a scorecard you can share.
| Dimension | The typical respondent | The figure | Your company |
|---|---|---|---|
| How deeply AI is used | Embedded across the business | 52% are at this depth; 83% use it regularly or deeper | |
| Monthly AI spend | $100–$500 a month | 77% spend $500 or less; 44% spend under $100 | |
| Primary tools | ChatGPT, Gemini and Claude | 87% / 74% / 73% | |
| Where AI is applied first | Content & marketing, then software | 77% content, 68% development | |
| Biggest result so far | Time savings for the team | 71% report it; only 17% report a revenue lift | |
| Most-cited obstacle | Cost of paid tools | 53% name cost; 32% see no real obstacle | |
| 12-month outlook | Increasing AI use | 89% plan to increase; 64% significantly |
Pick at least three answers above, then generate a shareable image.
In their words
The single biggest challenge, named
Swipe to read all 9
The team needs training. They need to be taught how to get the best outcome. The AI subscription for the team is expensive. I wish the total budget could be under $200 for the whole team.
Technology is evolving so quickly that we often end up paying for an AI tool we carefully selected, only for a better one to appear a few months later. Then we are stuck deciding whether to pay again or stick with the earlier good-enough option.
The biggest challenge by far is trust. AI still makes mistakes and hallucinations slip through, and we have not been able to hit 100% accuracy consistently. Every meaningful output still needs human review, which caps how much you can actually automate.
The biggest challenge is that we have not used AI for generating revenue. Current usage is mostly to optimise internal operations. We have not found an AI tool we can use to help generate revenue in our current market.
The single biggest challenge is not adoption; most businesses are already using AI in some form. The real challenge is governance, ensuring that as AI capability scales, accountability scales with it. Most organisations have not solved that yet.
It is the 80/20 rule. We can get AI tools to 80% of what we need for them to flourish, but the effort to take things the rest of the way home is disproportionate.
Turning promising AI pilots into consistent, scalable real-world impact. The challenge is integrating them into messy operations, ensuring data quality, and driving adoption so the results actually stick.
Having the time to experiment and swim through the sea of AI tools and noise that is out there.
The single greatest hurdle is the velocity of the AI landscape. While we recognise immense opportunities for revenue optimisation and market differentiation, the constant influx of new tools creates a complex selection tax.
Quoted with permission. Respondents who asked to stay anonymous are counted in the data but not named here. Lightly edited for clarity.
What it means
Three takeaways for operators
The starting line is behind us. The interesting question for Asia-Pacific founders is no longer whether to use AI; 83% already use it regularly or have it embedded. It is how deep to take it, because depth is what separates the companies seeing real results from the ones seeing only convenience.
Budget is not the constraint people think it is. Cost is the loudest complaint, yet most of this group spends under $100 a month. The binding constraint is attention and skill, not licence fees. The companies pulling ahead are spending more time, not necessarily more money.
Revenue is the next frontier. Efficiency is banked; only 17% have turned AI into top-line growth. The deeper adopters are five times more likely to get there. The move from a faster team to a bigger business is the work of the next 12 months, and 89% intend to keep climbing.
Questions, answered
Asia-Pacific AI adoption, in brief
How many Asia-Pacific small businesses use AI in 2026?
Across founders and business leaders in 15 Asia-Pacific markets, 83% use AI regularly or have it embedded across the business, and 52% have it embedded across multiple areas. Only a small minority are still experimenting or have not started.
How much do Asia-Pacific founders spend on AI each month?
Most run lean. 77% spend $500 a month or less on AI, and 44% spend under $100. Deep adoption does not require a large budget; the binding constraint is time and skill, not licence fees.
Which AI tools do Southeast Asian and Asia-Pacific businesses use most?
ChatGPT leads at 87%, followed by Gemini at 74% and Claude at 73%. GitHub Copilot or Cursor reach 32%. The frontier is a three-horse race rather than a single-tool region.
Where do small businesses use AI first?
Content and marketing comes first at 77%, followed by software development at 68%. Product research, internal operations and sales follow. AI shows up earliest in the work every small company already does daily.
What results are companies actually getting from AI?
71% report significant time savings for the team and 66% report faster product development. Revenue is the laggard: only 17% report a revenue lift from AI so far. Efficiency is banked; top-line growth is the next frontier.
What is the biggest obstacle to AI adoption for small businesses?
Cost is the most-named obstacle at 53%, even though most of this group spends little. 32% report no significant obstacle at all. Team skills gaps and output reliability are the next concerns after cost.
Are Asia-Pacific businesses planning to use more AI?
Yes. 89% plan to increase their AI use over the next 12 months, with 64% planning a significant increase. Not one company in the sample plans to cut back; the direction across Asia-Pacific is one-way.
What is AI adoption?
AI adoption is the extent to which a company actually uses AI tools in its everyday work, from one-off experiments to AI embedded across multiple business areas. In this survey, 83% of Asia-Pacific founders use AI regularly or have it embedded.
Founder or business leader in Asia-Pacific?
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Slice the data yourself
Open the interactive explorer to filter every chart by country, sector, company size and growth stage, and see how your slice of the region compares. If you are based in Asia-Pacific, you can add your own company to the next round.
Method & thanks
How this survey was run
The figures in this report (n = 117) come from founders and business leaders across 15 Asia-Pacific markets, fielded 13 April 2026 to 2 June 2026. The sample skews to small, founder-led companies: 91% are founders or co-founders and 73% run teams of 15 or fewer. Read it as a clear picture of early-stage and small-business AI use, not of large enterprises.
Responses were de-duplicated by email, keeping the most recent submission per person, and out-of-region and test entries were removed. Single-select questions are reported as percentages that sum to 100; multi-select questions (tools, areas, results, obstacles) allow more than one answer and do not. Where a market or segment carries fewer than 15 responses, it is described as directional and never presented as a precise headline figure. Free-text answers are stripped of any links or contact details before publishing, and names appear only where a respondent gave explicit permission to be quoted. All dollar amounts in this report are in US dollars (USD).
Huge thanks to
for sharing the survey with their founder community, and to every founder who answered honestly.
Cite as: Dhawal Shah, APAC AI Adoption 2026: The Full Report, dhawalshah.net, June 2026. Free to quote and link with attribution.