SME spending on AI platforms has grown 10x in a year across Asia Pacific. Eighty-four per cent plan to spend more while nearly seven in 10 are open to AI agents handling their payments.
This level of appetite is striking, especially for businesses that often run with limited time and lean teams. But it also creates a harder question: will all this AI spending make SMEs better run businesses, or just give them more tools to manage?
Many small businesses are already digital, but not necessarily connected. Payments, accounting, inventory and customer data often sit in different places. A supplier payment due tomorrow is not just an expense; it touches cash flow, stock availability, supplier relationships and the owner’s ability to fund the next opportunity. When those signals remain separate, even a fast-moving business can make slow decisions.
The gap is not interest. It is trust.
The adoption barrier is increasingly less about curiosity and more about confidence. A Mastercard research with 2,700 SMEs in Asia Pacific found that 69% of Australian SMEs were open to AI-driven payments, yet loss of human control was the top barrier among those not fully convinced.
China showed even higher openness at 97%, but the leading concern was unclear AI decision logic. In Thailand, 79% were open, while fraudulent payment risk topped the list of barriers.
Different markets and varying hesitations but the same lesson. SMEs are not asking AI to do less. They are asking it to be more transparent, controllable and accountable. This matters because AI needs to be much more responsible and reliable when it moves from simply summarizing a document to making recommendations that affect cash flow, suppliers or customers.
Start with the decision, not the tool.
The first practical step for SMEs is to choose the business decision before choosing the AI tool. Which decisions are slow, repetitive or made with too little visibility? Cash-flow planning, supplier payments, reconciliation, recurring spend and inventory replenishment are often better starting points than generic productivity use cases because they sit close to working capital and operating resilience.
The second step is to connect only the data that matters. AI does not need every piece of business information to be useful. It needs enough context to avoid giving generic answers: clean payment records, consistent invoice data, current supplier details, sales signals and real-time cash-flow visibility.
Some organizations in our region are already paving the way. For instance, in Malaysia, agribusiness ABI AGRO combined accounting software, marketplace channels and ChatGPT-supported customer engagement as part of its digitization journey, achieving a 5% revenue increase and a two-step reduction in process cycle.
While not every SME should copy these tools, the lesson here is that AI works best when it is tied to a clear workflow and supported by relevant and timely operating data.
Build rules before scaling.
The third step is governance. Before AI is allowed near payments or supplier decisions, SMEs should define approval limits, user permissions, exception handling and escalation paths. Which suppliers are approved? What spend thresholds require human review? What happens when an invoice, quote or stock level does not match expectations? Can an action be reversed or overridden?
That is the foundation for the next phase of agentic commerce. As AI moves from assisting to acting, the point is not to let technology spend freely on behalf of a business. The point is to turn intent into action inside explicit permissions, security checks and auditable records.
For an SME, a useful agent might flag a cash-flow pinch, recommend a different supplier payment date or method, and prepare the transaction for final approval. It should not act beyond the rules the owner has set.
Measure decision quality.
Lastly, SMEs should judge AI less by novelty and more by its decision quality. How quickly can the owner see a cash shortfall? How many payment exceptions are reduced? How much manual reconciliation work is avoided? How often are decisions made with data rather than instinct alone?
Time saved still matters. But the bigger opportunity is moving from isolated automation to better intelligence across the business. A chatbot that drafts an email may save minutes. An AI layer that connects cash flow, payables, receivables, and inventory can help business owners make decisions with more visibility, control, and confidence.
For small businesses, the next wave of AI should be judged by a simple measure: does it help owners make better decisions?
