AI Is Opening a New Market Among the World’s Smallest Businesses

Artificial intelligence (AI) is transforming businesses across various sectors, and its impact is no longer limited to large corporations; it’s now moving into the everyday operations of small businesses. This shift is becoming increasingly visible.

A QuickBooks-commissioned survey of thousands of adults across the US, Canada, Australia, and the UK found that a growing, informal entrepreneurship economy is changing how new ventures get started.

According to the survey, 33% of US adults plan to start a business or side hustle this year, a 94% increase from last year, and 65% of aspiring American business owners say they are likely to lean on AI to launch their venture. These ambitious founders plan to use AI to conduct market research (29%), create websites or product listings (19%), and develop branding assets (14%).

That same research shows the money behind these ambitions is thin, with Americans estimating they need tens of thousands of dollars to start a business. Yet cost barriers haven’t slowed adoption among those already running one. New research from the Federal Reserve Bank of San Francisco found that nearly 46% of small-business respondents already use AI, while an additional 15% planned to begin using it in the next 12 months.

One-third of firms in its 2025 Small Business Credit Survey, however, have no plans to use AI, with 30% saying they simply prefer not to. And among those using AI, only 7% had fully integrated it into their business processes, while half said they were experimenting with it and another 44% had partially integrated it.

At the same time, data from SoFi reports that about three-quarters of small business owners are now using at least one AI tool, and LinkedIn’s 2026 research describes AI as a strategic asset.

“AI has moved from a tool to a strategic asset for small businesses aiming to stay resilient and grow in 2026. By adopting AI, businesses can streamline operations, reduce costs, and accelerate decision-making, creating space for innovation and relationship-building.”

– Sharat Raghavan, an Economist and Director of Research at LinkedIn

All this data makes clear that AI is no longer just a big-company story; it is playing out across the broader business economy. But the question remains: can AI genuinely narrow the resource gap between the smallest businesses and larger competitors, or will it create a new technological divide?

This question is particularly important in the informal economy, which is characterized by unregistered, unregulated, and mostly micro-sized businesses. A new academic study looks into exactly that: who wins and who is left behind among these businesses.

AI Lowers the Barrier But Not Every Barrier

Today, artificial intelligence is everywhere, and it seems to have happened overnight. While the launch of ChatGPT in late 2022 took AI mainstream, AI’s history goes much further back. The groundwork for the technology began in the early 1900s.

In 1950, mathematician Alan Turing published “Computer Machinery and Intelligence,” which introduced the Turing Test, used by experts to measure computer intelligence. The term “artificial intelligence,” meanwhile, was coined by John McCarthy, who also created the first programming language for AI research (LISP).

After a period of both rapid growth and struggle came the AI boom of 1980-1987, marked by government funding and research breakthroughs. But then interest in the technology waned, though development continued, and the first decade of the 2000s recorded substantial progress, including a robot simulating human emotions, remote-controlled vehicles navigating other planet surfaces without human intervention, companies using AI in their UX algorithms, and the release of Siri.

This brings us to the present day, when we are seeing a surge in common-use AI tools like chatbots, virtual assistants, search engines, and workflow automation. These tools have made AI wildly popular among consumers and are now having a significant impact on the business world. AI involves computer systems and applications designed to simulate human intelligence, including recognizing patterns, making predictions, and supporting decisions.

In business, AI is being increasingly embedded in digital products we use regularly, such as customer-service chatbots, recommendation systems, content-generation tools, translation applications, advertising platforms, and financial technologies. This lets entrepreneurs access AI through tools they already use, rather than building AI systems themselves.

For people running small businesses, this matters because the main constraint they face is the limited amount of time, money, information, and labor available to turn entrepreneurial ambition into action, and AI helps remove those constraints.

For years, the benefits of the kind of automation that AI offers could only be enjoyed by large organizations that could afford data scientists, cloud contracts, and custom software. As a result, small businesses have lagged behind larger organizations in adopting new technologies. But that changed with smartphones and social media platforms, which put these capabilities right inside the tools people already own. With AI tools now widely accessible, an entrepreneur doesn’t have to understand how a neural network works; they only need to know how to open an app.

That is driving increased AI adoption. For instance, JPMorgan’s first report in the Small Business in the Age of AI research series shared that newer firms are “more likely to adopt AI at the outset and ramp up more quickly.” Small businesses are also consistently paying for AI, “using a wider range of services than before.”

What’s more, the technology could change how small businesses compete. Using generative AI, they can reduce the cost and time involved in developing ideas or producing content, communicate across languages more easily through translation tools, leverage AI-supported analytics to interpret customer preferences and market movements, and extend their responsiveness beyond their working hours through automated customer interaction.

A new study of SMBs in India commissioned by Amazon (AMZN ) Ads reported that almost 87% of SMBs say that with the help of AI-powered ad tools, they have been able to access audiences or channels that were out of their reach, while for three-quarters, AI-enabled advertising directly bolstered business growth.

AI doesn’t replace the entrepreneur; it augments their existing capacity. But it’s not all positive. As respondents in the Fed’s 2025 Small Business Credit Survey stated, while using the technology led to increased productivity (71%), improved quality of goods and services (39%), and higher sales (31%), it didn’t change labor costs.

AI usage also comes with other challenges. For almost half of respondents (46%), that challenge is accuracy, while for 43%, it was adapting AI tools to meet their business needs.

Finding tools to meet business needs has been the top challenge for more than half (54%) of those intending to adopt AI in the next 12 months. The time required to train employees on AI (37%) is another significant challenge for these firms.

Moreover, the shift from bespoke, expensive AI toward cheap, embedded AI makes the technology accessible, but accessibility isn’t the same thing as the ability to extract lasting value from it.

The distinction becomes particularly important in the informal economy. The latest study maps out this exact terrain, arguing that AI is emerging as a double-edged instrument for the roughly half of non-agricultural jobs worldwide that sit in the informal economy and generate close to a third of GDP in many developing countries.

The study’s main claim is that AI can be a genuine leveler, improving productivity and market access while exposing entrepreneurs to digital inequality, platform dependence, and new vulnerabilities.

AI Can Level the Playing Field or Deepen the Divide

The study titled ‘Artificial intelligence in the informal economy: game changer for microentrepreneurs?¹‘ isn’t a conventional large-scale empirical test but rather a conceptual paper that combines two established theories to create a framework, which the authors then illustrate using three real microenterprises

The framework explains how informal microenterprises can develop and deploy AI despite severe resource constraints. One of the two main perspectives integrated into this framework is bricolage theory, originally developed to explain how resource-starved actors get things done by creatively recombining the tools already available rather than waiting for the “right” resources to arrive.

When applied to digital tools, the authors define this as mobilizing and creatively recombining underused or disused digital resources. For an informal entrepreneur, this may mean repurposing an old smartphone, using a free analytics dashboard, combining inexpensive software applications, experimenting with accessible AI services rather than purchasing a sophisticated enterprise system, and using a messaging app’s built-in automation to create, deliver, and capture business value.

Digital resources, the authors argue, are particularly suitable for this kind of improvisation because they are portable, versatile, and increasingly interoperable. The second main perspective integrated in the framework is dynamic capabilities theory, which explains why some firms operating under almost the same conditions still pull ahead of their peers. The difference lies in a firm’s capacity to sense new opportunities, seize them, and reconfigure its resources as conditions change.

In relation to AI, this means it may generate useful information, but information alone does not create a competitive advantage. It’s up to the entrepreneur to recognize what matters, decide what action to take, and adapt the business accordingly.

This is why the study doesn’t treat AI capability and entrepreneurial capability as interchangeable but as complementary.

In this relationship, AI strengthens opportunity sensing, decision-making, and adaptation, while the entrepreneur’s capabilities determine whether those technological possibilities are converted into business outcomes.

Drawing on bricolage and dynamic capabilities theories, the authors introduce nine new propositions concerning the relationships among AI, digital bricolage, dynamic capabilities, competitiveness, and enterprise growth. These propositions are illustrated with three real microenterprises from Lagos, Nigeria.

The three cases are drawn from a broader group of 17 small businesses spanning healthcare, education, construction, logistics, agriculture, engineering, fashion, and consulting.

Healthcare Multimedia Services, Market Research Services, and Fashion Tech Innovations are three businesses selected for detailed illustration. These businesses demonstrate the most comprehensive use of digital technologies, combining smartphones, cloud platforms, low-cost software, analytics, augmented reality (AR), and other accessible technologies instead of relying on expensive bespoke AI infrastructure.

The findings describe a two-sided dynamic. On the empowerment side, tangible AI resources like mobile-money platforms and AI-driven analytics let informal entrepreneurs assess creditworthiness through alternative data, forecast demand, and reach customers well beyond their physical neighborhood. This kind of market access used to require formal banking relationships and capital that these entrepreneurs do not have.

The Lagos cases show this in practice. A healthcare-focused business combines basic AR and VR apps with existing hardware to deliver remote training and consultations. A market-research operation combines free analytics tools to mimic capabilities that would otherwise require proprietary enterprise software. A fashion-tech venture repurposes AR for virtual fitting rooms while training staff, through hands-on trial and error, to manage predictive algorithms they were never formally taught.

On the disempowerment side, the paper finds that reliance on mismatched, low-cost tools tends to produce fragmented capabilities that do not work together, create data silos, and limit how far a business can scale.

For instance, the fashion venture’s virtual fitting room works well for customer engagement but struggles to feed accurate data back into inventory management because the tools were never designed to interoperate.

The study has also flagged a structural risk: larger, better-resourced firms tend to extract more value from AI than small ones. This means that instead of disrupting existing market dominance, the technology may end up reinforcing it. In practice, this could result in entrepreneurs in rural or less-educated segments falling further behind their urban, better-connected counterparts.

The key argument of the study is that AI functions as both a leveler and a stratifier. The deciding factor is not the technology but whether an entrepreneur has the digital literacy, infrastructure access, and adaptive know-how to turn a free tool into a durable capability.

The study’s implications are more nuanced than the simple claim that AI helps small businesses. Per the study, inexpensive AI can meaningfully improve forecasting, customer engagement, market access, and operational decision-making.

But it also creates limitations, meaning the largest investment opportunity may belong to those providers who can package those capabilities into simple, integrated tools that entrepreneurs already use.

“For informal micro-entrepreneurs, who often face resource asymmetries and operate with limited institutional support, the prospect of AI brings a blend of hope and complexity: hope for overcoming historical barriers to growth and digital access, and complexity due to intensified challenges like double precarity, re-intermediation through algorithmic control and deepening resource inequality,” stated the study.

For policymakers, this means targeted interventions such as subsidized digital-literacy training, tax incentives for technology adoption, and partnerships between microenterprises and universities or tech hubs.

These interventions need to help entrepreneurs move from fragmented, improvised bricolage toward more integrated, scalable AI systems without losing the flexibility that made informal enterprise resilient in the first place. The cases presented in the study are illustrative, and the authors call for larger, longitudinal, and comparative research across sectors and geographies.

Meta Platforms (NASDAQ: META)

From an investment perspective, Meta (formerly Facebook) makes for an attractive option. The researchers also repeatedly reference WhatsApp, Facebook, social media, and digital marketplaces as tools through which informal businesses gather customer information, communicate, sell products, and assemble digital capabilities.

With a market cap of $1.424 trillion, META stock is currently trading at $564.29, down 15.3% YTD. It has an EPS (TTM) of 28.88 and a P/E (TTM) of 19.36. META pays a dividend yield of 0.38%.

Powered by AI and immersive technologies, Meta is building human connections. Through its products, the company enables people to connect and share with friends and family.

Meta mainly operates through two segments: Family of Apps (FoA), which includes WhatsApp, Instagram, Threads, Messenger, and Facebook, and Reality Labs (RL), which includes its augmented, virtual, and mixed reality-related hardware and software.

The company’s recent product strategy shows it is working on making AI an operating layer for businesses. In June, it introduced Meta Business Agent for businesses of all sizes, which is already being used by over a million active businesses.

The AI-powered agent was rolled out worldwide across WhatsApp, Instagram, and Messenger. The system can answer questions, recommend products, qualify leads, book appointments, and assist with sales, with the company intending to expand it into areas such as market research and competitive intelligence. For microbusinesses that can’t afford a dedicated customer-service employee, an AI agent can potentially handle part of that workload.

Initially free, Meta began charging for the agent this month on a per-token basis at $2 per million tokens.

Recently, while announcing the company’s financial results, founder and CEO Mark Zuckerberg shared that “AI is accelerating our core business today, powering our next generation of products, and opening the door to entirely new enterprise opportunities.”

For Q2 2026, the company reported revenue of $60.80 billion, up 28% YoY, though total costs and expenses grew faster, rising 55% to $42.03 billion, including $2.40 billion in legal proceedings and $1.18 billion in severance expenses. Capex for the period was $31.08 billion.

The company also reported family daily active people (DAP) of 3.60 billion on average, while average price per ad increased 12% YoY. During the earnings call, Zuckerberg said 3.6 billion people use at least one of its apps each day, with Threads reaching 500 million monthly actives, Instagram hitting 2 billion daily actives, Facebook over 2 billion daily actives, and WhatsApp peaking at 30 million messages sent per second.

During this period, Meta’s operating cash flow was $31.86 billion and free cash flow was $784 million. Cash, cash equivalents, and marketable securities at the end of June stood at $90.26 billion. Meanwhile, the company spent $1.35 billion on dividends and dividend-equivalent payments.

For Q3 2026, the company expects revenue of $61-64 billion and total expenses of $165-169 billion. On the earnings call, Zuckerberg stated that the company is “now at a point where our investments in AI are accelerating every major part of our core business. They’re improving the experience for people using our apps, driving better performance for advertisers, and helping our teams build new experiences and ship faster.”

Meta is also developing personal agents that will form the foundation for its next wave of products and revenue lines, with Zuckerberg speaking about building superintelligence focused on “distributing it widely and giving everyone the ability to direct it towards what matters to them.”

Conclusion

AI has permeated every sphere of our lives, and as the technology becomes cheaper and easier to access, it allows businesses that previously could not afford it to take advantage of enterprise-class capabilities. As a result, small and informal businesses are becoming one of the largest and fastest-growing user bases of this emerging opportunity.

However, success depends less on whether AI is available and more on whether these small businesses can turn digital bricolage into durable, sensing-and-seizing capability. Overall, the future of AI in the informal economy is likely to be determined as much by inclusion and capability-building as by the sophistication of the technology itself.

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References

1. Kolade, O., Egbetokun, A., Owoseni, A. & Woldesenbet Beta, K. Artificial intelligence in the informal economy: game changer for microentrepreneurs? International Journal of Entrepreneurial Behavior & Research (2026). https://doi.org/10.1108/IJEBR-12-2024-1457