The interplay between contemporary business and AI: What should SMEs do to catch up?

Across both developed and developing economies, business remains the heartbeat of growth—whether state-owned or private, profit-seeking or not-for-profit. Yet the mechanics of doing business have shifted decisively. What once revolved around in-person buying and selling now runs through digital channels, powered by data and Artificial Intelligence (AI). For small and medium-sized enterprises (SMEs), especially in emerging markets such as Ghana, this shift is both a challenge and a once-in-a-generation opportunity.

AI is no longer just about processing data. With machine learning (ML) and generative AI, systems can spot patterns, make predictions, converse with customers, and even produce images and marketing copy. As technology matures, modern businesses are retooling to enhance productivity, refine customer experiences, cut costs, and increase margins. The question is no longer if AI matters, but how fast SMEs can adapt—and where to start.

What AI really adds to business value

• Automation: Streamline repetitive tasks—from data entry to support triage—freeing teams for higher‑value work.
• Prediction: Forecast demand, detect churn risks, and optimize inventory based on real patterns, not guesswork.
• Personalization: Tailor offers, pricing, and messaging for better conversion and loyalty.
• Content at scale: Generate product descriptions, ads, and visuals faster and cheaper.
• Insights: Translate raw data into decisions, surfacing what drives sales and where to allocate budget.

Why many SMEs lag behind

Despite AI’s promise, SMEs face real friction: limited budgets, scarce expertise, fragmented or low-quality data, resistance to change, and uncertain regulation. In Ghana and similar contexts, early chatbot deployments are emerging, but the underlying data is often underused—rarely feeding predictive models that improve marketing, procurement, or service. The result is partial adoption: tools without transformation.

A practical catch-up roadmap for SMEs

• Start with high-ROI use cases: Pick 1–2 tangible problems with clear payback. Common winners: customer support chatbots for FAQs, sales forecasting, inventory optimization, lead scoring, and automated marketing content.
• Get your data house in order: Consolidate key datasets (sales, customers, inventory, support logs) into a simple, secure repository. Clean obvious errors, standardize formats, and define basic governance (who owns what, who can access).
• Leverage cloud and low-code tools: Adopt affordable SaaS and no-code/low-code AI tools to cut time-to-value. Integrate them with your website, POS, CRM, and social channels.
• Build an AI-ready culture: Upskill staff on data literacy and prompt-writing; appoint an internal AI champion; celebrate quick wins to reduce resistance and build momentum.
• Implement responsible AI guardrails: Set policies for data privacy, model monitoring, bias checks, and human-in-the-loop review—especially for customer-facing and financial decisions.
• Partner where it counts: Collaborate with local universities, tech hubs, and vendors for training, pilots, and mentoring. Consider shared services to reduce costs.
• Secure funding smartly: Explore microfinance, development programs, and government-backed loans for digital transformation. Prioritize investments that show measurable payback within 6–12 months.

The Ghana lens: from pilots to productivity

Ghanaian SMEs are beginning to use chatbots to handle inquiries and complaints, improving response time and customer satisfaction. The next step is to convert those interactions into insight: analyze common issues, identify churn signals, forecast demand by region or channel, and feed these findings into procurement and marketing plans. Pair this with generative tools for content and design to reduce campaign costs and speed up go-to-market.

To scale sustainably, SMEs should align with local regulations, advocate for clearer AI policies, and push for accessible financing and digital infrastructure. Industry associations can play a vital role by aggregating demand, negotiating better vendor terms, and deploying shared training.

Risks to manage—and how

• Data privacy: Limit sensitive data sharing; use role-based access and encryption.
• Hallucinations and errors: Keep humans in the loop for customer responses, finance, and compliance.
• Bias: Audit training data; test outputs across segments; document model limitations.
• Vendor lock-in: Favor open standards and exportable data; pilot before long contracts.
• Change fatigue: Communicate benefits, train continuously, and recognize early adopters.

A 12-week plan to get moving

• Weeks 1–2: Pick one use case; define success metrics (e.g., 20% faster responses, 10% lower stockouts). Inventory data sources; set access and quality rules.
• Weeks 3–6: Pilot with a low-cost tool. Integrate with your CRM or website. Establish human review and logging. Train frontline staff.
• Weeks 7–10: Measure impact; refine prompts, workflows, and data feeds. Add simple dashboards.
• Weeks 11–12: Decide: scale, iterate, or pivot. Document ROI and lessons. Plan the next use case.

Bottom line

AI is reshaping commerce—from how products are discovered to how operations run behind the scenes. For SMEs, especially in markets like Ghana, the winners will be those who start small, move fast, and learn continuously. Prepare your data, focus on clear payoffs, upskill your people, govern responsibly, and leverage partnerships and financing. The opportunity is real, the tools are ready, and the path is practical.

About the author

Jacob AZAARE holds a PhD in Management Science and Engineering. He is a Senior Lecturer in the Department of Business Computing, School of Computing and Information Sciences, University of Technology and Applied Sciences, Navrongo.