There is a technological problem facing the small businesses in Thailand which might unexpectedly turn into a technological advantage. The small and medium-sized enterprises in the country are still mostly regarded as “Digital Followers”. The 2025 Digital Maturity Index for Thai SMEs had a score of only 2.45 on a scale of 4.00 according to Thailand’s Electronic Transactions Development Agency. While this represented an improvement of 3.81 percent on the previous year’s figure, the agency noted that a great many businesses were still using technology mainly for things like social media and point-of-sale systems rather than incorporating it thoroughly into data analysis, internal processes and business strategy.
That usually appears to be a disadvantage in a competitive situation, although artificial intelligence might alter the situation. In a recent interview during DEEP TALK in Thailand, Parith Rangsimanond, who is one of the co-founders of Looloo Technology and an expert in AI, stated that small businesses are at times able to change more quickly since they do not have so many outdated systems blocking their way. Instead of taking years to force AI into existing corporate infrastructure that is decades old, a small business might have the opportunity to redesign the whole process based on AI from the start. That would therefore represent a significant change in the economics of technology. For many years the company possessing the largest IT budget had enjoyed a advantage in terms of scale. The AI era, however, might instead reward something else: the company that was able to reorganize most quickly.
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Rangsimanond’s argument goes much further than simply advising employees to use ChatGPT. He says that companies are progressing from individual AI “use cases” to full AI workflows. It may boost the productivity of a single worker if employees are given a chatbot which drafts emails more quickly, but this does not mean that the company as a whole becomes faster. The greater opportunity arises when the departments responsible for purchasing, finance, production, sales and management start exchanging information and responding to one another.
His analogy is very useful; it’s as if you gave every employee a Ferrari yet kept forcing them to drive through the same old intersections. Although the cars become faster, the road system remains unchanged. Genuine transformation is achieved only by rebuilding the highway. A example that is mentioned in the interview relates to procurement. Let’s say that a supplier suddenly informs a company that an important delivery will arrive one month late. In a conventional business, an employee would have to inform finance, production, purchasing and sales individually. Even though each of the departments may already have its own AI software, the links between those systems are still manual. Rangsimanond refers to this human transfer as the “Human API.”
Instead, an integrated AI workflow could detect the delay, rework the production plans, revise the expected cash requirements, warn the sales department and make changes to the purchasing decisions across the company. The fact that there is such a distinction is due to the huge size of Thailand’s SME sector. According to the data on the Thailand’s Office of Small and Medium Enterprises Promotion SME Big Data platform, Thailand had around 3.28 million SMEs in 2025, employing approximately 13.6 million people. Productivity improvements that were even fairly small could become of considerable economic importance when they were spread across that group of employees.
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There is one other reason why the Thai SME story is interesting. The intense international competition in the field of artificial intelligence is generally portrayed in terms of huge sums of money. Microsoft, Amazon, Meta and Alphabet are each investing hundreds of billions of dollars in the infrastructure needed to train and run artificial intelligence systems. According to a recent report by TerreneGlobe, these four companies had announced about $830.5 billion in future payments relating to leases which had not yet started as of June 30, before another roughly $68 billion from Meta’s data centre lease agreements signed in July was taken into account.
The small and medium-sized enterprises in Thailand are engaged in a completely different kind of game. There is no need for them to construct the infrastructure; they can just rent access to it. It therefore means that the substantial AI investments made by the biggest technology companies will eventually make affordable computing power and intelligence available to businesses which themselves could never afford to build such equivalent systems. He uses his family’s fertilizer business as an example. AI can assist in deciding what inventory to buy and when by combining data on purchases, expected payments from customers, warehouse capacity, past sales, and supplier delivery times. What is important here is not merely coming up with a clever AI answer but rather linking together the information which had previously been held in separate sections of the company.
The Thai government’s agencies are at present trying to get small and medium-sized enterprises (SMEs) to adopt digital technologies more thoroughly. In September, ETDA published the results of its SMEs GROWTH 2026 programme, which covered 1,697 SMEs in 16 provinces and included 138 digital service providers. This initiative led to 108 business matches and is estimated to have generated 689.5 million baht in economic and social benefits, of which 530.6 million baht is attributable to the SMEs that took part. The figures do not prove that every small company could suddenly beat a large corporation; they do indicate that Thailand is making an effort to get its small and medium enterprises from just having digital tools to actually reorganising their business processes around those tools.
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It is here that the advantage of being small becomes clear. Large companies usually have much bigger budgets for technology, larger engineering teams and much greater datasets. Yet they also have years’ worth of software integrations, custom enterprise platforms and organizational procedures which cannot easily be replaced. A smaller company could have the other problem. Although its technology might not be as advanced, it could also have less infrastructure to take down.
Rangsimanond makes this point clear in the interview, stating that his fertilizer business could undergo rapid transformation since it was not constrained by extensive legacy systems. Instead, smaller companies might well set up their new reporting, sales and management procedures around AI rather than attempting to get AI to fit in with all the existing practices. Just because of that doesn’t mean that the AI transformation takes place automatically. A serious counterargument can be made to the SME advantage thesis: although having fewer legacy systems may imply having less structured data, it also means possessing weaker cybersecurity, less technical expertise, and a smaller number of employees who are capable of overseeing automated systems.
ETDA’s analysis of the digital transformation of Thai SMEs specifically points out limitations relating to capital, personnel, knowledge, and the ability to choose the right technology; its research shows that whilst SMEs are moving towards digital adoption many have not yet connected their various technologies across the business in a systematic manner. Rangsimanond has also admitted to the gap between discussion and implementation. At an AI seminar held in Bangkok in March 2025, he told The Nation Thailand, “We often discuss the need to embrace AI, but actual implementation is scarce.” That could be the actual point of division.
The rivalry between businesses in the field of artificial intelligence is nowadays probably not going to be decided simply by which ones have access to ChatGPT, Gemini, Claude or some other model, since those tools are now widely available; what will likely provide a more lasting advantage is proprietary information concerning customers, suppliers, prices, inventory and operations, together with a workflow that can act on this information swiftly. In the latter part of the interview, Rangsimanond gives an account similar to this regarding the increasing importance of “small data”. A fertilizer company which slowly learns the preferences and purchasing habits of hundreds of thousands of individual farmers has information that a competitor cannot merely download from the internet, even though AI makes it easier to analyse that information the company still has to gather it.
It presents an unusual opportunity for Thailand. Although their SMEs are lagging behind in terms of traditional digital transformation, some of them may also be less constrained by the systems developed in the earlier technology era. Since powerful AI software keeps becoming cheaper whereas the cost of replacing old corporate infrastructure stays high, smaller businesses might at times be able to leapfrog companies which have considerably larger technology budgets. The following stage in the race involving AI may therefore be less about who owns the most technology and more about who is able to reorganise their business in the quickest way. For the millions of small and medium-sized enterprises in Thailand, being small might end up becoming an aspect of their technology strategy.