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By Rita Okoye
For many small and medium-sized enterprises, artificial intelligence can seem far removed from the everyday realities of running a business. While much of the discussion around AI focuses on sophisticated systems and large technology investments, smaller companies are often dealing with more immediate questions about customers, inventory, cash flow and operating costs.
Emmanuel Uzoh, a Business Relationship Manager in the SME Division of Guaranty Trust Bank, sees a more practical opportunity for smaller businesses: using technology to make information they already generate more useful in everyday decisions.
His perspective comes from working with SME customers to understand their operations, financial needs and business performance. That experience has exposed a recurring problem. Smaller businesses often possess useful information but lack the time or analytical capacity to examine it systematically.
A retailer records sales and inventory. A distributor receives customer orders and tracks stock. A service company generates invoices and monitors payments. Transaction histories, customer records and expense information accumulate as these businesses operate, even when the information is spread across different systems.
The question is what a business can learn from it.
For Uzoh, that means starting with a decision rather than with the technology. Instead of asking where artificial intelligence can be introduced, a business can identify a recurring problem and determine whether better analysis of existing information could help solve it.
Inventory provides one example.
A retailer deciding what to purchase for the coming weeks can examine historical sales, recent demand, available stock and seasonal patterns. Analytical tools can help bring those signals together, making it easier to identify products that may require replenishment and those whose demand is weakening.
Customer information presents another opportunity. Uzoh developed a method at GTBank for uncovering the unmet needs of inactive businesses and using those insights to tailor products and services to their needs. His work has helped reactivate hundreds of dormant business accounts, bringing more than $2 million in deposits back into the bank.
The experience points to a broader opportunity for SMEs using artificial intelligence. AI-assisted analysis can help businesses examine customer information quickly and at scale, uncovering unmet needs and identifying opportunities that might otherwise be difficult to see. For smaller businesses, that speed can matter. Earlier insights can support faster decisions about customers, products and resources, helping businesses respond to opportunities sooner and scale more effectively.
For an SME, that could mean understanding why previously active customers have stopped buying, identifying products existing customers may need but are not currently using, or finding recurring problems that suggest an opportunity for a new service.
The technology does not have to determine the answer on its own. Its value can be in helping businesses uncover opportunities for management to investigate, understand and act upon.
Financial information can be particularly important for companies operating with tight cash positions. Historical payment and expense patterns can provide greater visibility into periods when cash requirements may increase, allowing management to prepare before pressure becomes immediate.
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These applications have something in common: the technology is supporting a specific decision rather than attempting to run the business.
That distinction reflects Uzoh’s experience in banking, where understanding a company often requires more than looking at a single financial measure. A business may have growing sales while experiencing cash-flow pressure because customers are paying slowly. Another may appear stable overall while changes in its financial activity suggest that its circumstances are shifting.
The usefulness of information depends on connecting it to the decision being made.
This approach can also help SMEs avoid unnecessary technology spending. Once a recurring operational problem has been identified, the company can determine what information is available and whether analysis or automation offers enough practical improvement to justify the investment.
The quality of that information matters.
Inaccurate inventory records, inconsistent customer information or incomplete financial data can undermine an analytical system regardless of how sophisticated the technology is. In some businesses, improving how information is collected and organised may therefore create more immediate value than adopting a complex model.
Human judgment also remains necessary when circumstances depart from historical patterns. A sudden decline in sales may indicate weaker demand, but it could also result from a temporary supply problem. An increase in expenses could signal inefficiency or reflect a deliberate investment intended to support future growth.
Automation presents a similar trade-off.
A repetitive process may be a good candidate for automation, but automating a poorly designed process can simply allow the same inefficiency to occur faster. A business trying to reduce costs should first understand where time or money is being lost before deciding whether technology is the appropriate solution.
That problem-first approach is particularly relevant for SMEs because their resources are limited. Smaller companies may not have dedicated data teams or large technology budgets, leaving less room for investments that do not address a clear business need.
The potential benefits, however, do not have to be dramatic to matter. Better inventory decisions can reduce unnecessary purchasing. Earlier visibility into cash requirements can give management more time to respond. Automating repetitive administrative work can allow employees to spend more time on customers and other activities requiring human attention.
For Uzoh, this practical use of AI is more important than whether a smaller company can describe itself as technologically advanced.
The relevant measure is whether the technology improves a decision, reduces avoidable work or helps management understand the business more clearly. For SMEs deciding where to begin, the first question may therefore be simpler than the technology itself: which recurring business decision would benefit most from better information?
