Four Imperatives to Help Demystify AI Use Cases: An African and Ghanaian Perspective

Introduction

With AI adoption accelerating across Africa, organizations from Cape Town to Cairo—and particularly in technology hubs like Accra, Ghana—are searching for winning AI use cases. These business applications can provide competitive insights or productivity breakthroughs to improve performance in Africa’s unique context.

However, for many African enterprises, this process feels like solving a giant jigsaw puzzle without a picture on the box. Significant trial and error is needed, along with strategic investments in technology and capabilities that address Africa’s distinct challenges and opportunities.

Defining what constitutes an AI use case remains unclear across the continent. A business executive at Ghana’s E-Services company described it as “an application of AI tools tailored to African markets that increases efficiency or improves revenue while addressing local constraints.” In contrast, a technology vendor at Nigeria’s Interswitch characterized it as “a proven application of our AI technology that has been successfully deployed in several African customer environments.” Perspective clearly matters, especially in Africa’s diverse markets.

Our research across key African markets, including Ghana, Kenya, Nigeria, and South Africa, has led us to conclude that a good AI use case results from a carefully calibrated “matching exercise.” Value emerges at the intersection of available data sets and business problems/opportunities specific to African contexts. This matching is particularly challenging in Africa, where many companies still struggle with data quality, readiness, and aggregation. Additionally, the continent’s business problems have unique dimensions related to infrastructure limitations, regulatory environments, and market characteristics.

In Ghana specifically, where the government launched its National AI Strategy in 2023, organizations face both unique challenges and opportunities. Many Ghanaian enterprises we interviewed highlighted the “language gap” between business executives and their data science counterparts—a challenge amplified by Ghana’s educational system, which has traditionally separated business and technical training.

So, what’s the answer to designing good AI use cases in the African context? The matching process between datasets and business problems/opportunities is rarely a one-off, especially in rapidly evolving African markets. It requires iteration, continuous learning, and time—all in environments that often demand quick results. There are four imperatives African organizations should follow when designing an AI use case, with special considerations for Ghana’s business landscape.

Imperative 1: Match your problem/opportunity with the right type of AI initiative in the African context

Language and definitions matter, especially in Africa’s multifaceted technology ecosystem where terminology can vary across regions and industries. We found significant overlaps in how African organizations define their AI initiatives. Understanding these distinctions is crucial, as AI initiatives have different durations, complexity levels, uncertainty/risk profiles, and expected outcomes.

AI experiments in the African context are small-scale, time-bound activities designed to test a hypothesis specific to local conditions. Ghana’s National Health Insurance Authority recently conducted an experiment testing whether machine learning algorithms could detect fraudulent claims by analyzing historical data patterns unique to the Ghanaian healthcare system. This lightweight initiative required minimal investment but provided valuable insights about the feasibility of more comprehensive fraud detection systems.

AI proof of concepts (POCs) or pilots across Africa are focused initiatives that prove the feasibility of an AI application under controlled local conditions. Ghana’s AgroCenta, for example, developed a POC for an AI-powered advisory system to help smallholder farmers optimize crop production based on Ghana-specific soil data, local weather patterns, and market conditions. This POC involved testing with a subset of farmers in the Eastern Region before broader deployment.

AI projects in African contexts are structured, well-defined efforts following methodologies adapted to local circumstances. MTN Ghana’s customer service AI implementation represents a full project—a comprehensive deployment of chatbots and predictive models that required months of development, adaptation to Ghanaian languages and communication patterns, and integration with existing systems.

Where do AI use cases fit in the African context?

AI use cases are the foundation that guides the direction of experiments, pilots, and POCs in African enterprises. They provide context and evaluation criteria tailored to African markets. For example, the Ghana Commercial Bank (GCB) developed a use case to improve loan approvals for Ghana’s large informal sector—a challenge distinct from similar applications in Western markets due to limited credit histories and formal documentation.

Developing successful AI initiatives in Africa follows an iterative process: use cases guide the matching exercise between datasets and business problems/opportunities unique to markets like Ghana, typically leading to an experiment. Experiments validate the hypotheses underlying the use case in the local context. Once validated, these experiments evolve into POCs, and successful POCs become pilots. Pilots inform broader deployment strategies, and successful ones transform into full-scale AI projects deployed across the enterprise.

Business context should drive AI use case development in Africa. In Ghana, this might involve responding to the government’s digitalization agenda or addressing specific challenges in key sectors like agriculture, where AI-driven soil analysis could significantly improve yields. Use cases help Ghanaian organizations define value pools to guide AI strategy implementation in an economy transitioning from resource dependence to knowledge and service orientation.

What criteria make a successful AI use case in Africa?

Our research across African markets revealed that successful AI use cases display specific characteristics in the African context:

• An iterative matching between a business problem/opportunity and available datasets that accounts for African data limitations and opportunities
• Validation methods for hypotheses that consider African infrastructure, connectivity, and technological realities
• A focus on industry domains relevant to African economies (agriculture, mobile money, healthcare, etc.)
• Timelines adjusted for African implementation realities, typically 4-12 months
• KPIs that reflect African business priorities and conditions
• Champions at the executive level who understand both local business contexts and technological possibilities

For instance, Hollard Insurance Ghana described an AI use case aimed at optimizing mobile-based micro-insurance products for Ghana’s informal sector. The company developed a machine learning model using mobile money transaction data, demographic information, and historical claims data—iteratively matching this insurance challenge with relevant datasets available in Ghana. The project aimed to validate hypotheses about reducing customer acquisition costs and improving risk assessment over an eight-month timeline adapted to Ghana’s market conditions.

Clear milestones included increasing policy uptake among previously uninsured populations by 20% and reducing claims processing time by 30%—metrics specifically designed for Ghana’s insurance landscape. A senior executive with deep understanding of both insurance and Ghana’s technological capacity championed the initiative, ensuring alignment with business objectives and advocating for broader adoption if successful.

Imperative 2: Define your matching dimensions for African contexts

While conventional wisdom suggests starting with a business problem and working backward to the required data, AI implementation in Africa often follows a different pattern. Sometimes you start with a business problem unique to African contexts, and sometimes with available African datasets. The technological backbone matters but should never be the starting point, especially given infrastructure constraints in many African markets.

Business problems in Africa require specific definitions relevant to local contexts. In Ghana, for example, a good business problem definition needs to be specific, relevant to the Ghanaian market, objectively measurable, and quantifiable within local data constraints.

For example, an executive at Korle Bu Teaching Hospital in Accra described an initially vague use case: “We want to use AI to make our hospital operations more efficient.” Such a broad definition provided little direction. After iteration, the team reframed it as: “We want to reduce patient wait times in our outpatient department by 30% by using AI to optimize scheduling based on historical patient flow, staffing patterns, and procedure durations.” This refinement initiated a productive matching exercise, examining outpatient records, staff scheduling data, and procedure durations specific to Ghana’s largest teaching hospital.

Existing datasets can also serve as starting points in the African context, where unique data patterns might reveal unexpected insights. For instance, Ghana’s Fidelity Bank applied unsupervised machine learning to mobile banking transaction logs without a predefined question. The analysis uncovered usage patterns suggesting opportunities for new financial products tailored to specific customer segments in Ghana’s growing digital economy. This data-first approach led to a focused use case for developing personalized financial services.

The matching process in Africa faces unique challenges. Datasets in African markets may have gaps due to limited digitalization or historical record-keeping practices. Business problems evolve rapidly in Africa’s dynamic markets where regulatory environments, infrastructure, and customer behaviors can change quickly. Additionally, both datasets and business problems have known and unknown factors that must be identified within the African context.

For effective matching in African contexts:

1. Assess both business problems and available data regardless of starting point. Key criteria for business problems include feasibility within African technological constraints and potential impact on performance in local markets. Data assessment should consider findability (can reliable data be found in African contexts?) and accessibility (can data be economically accessed given local connectivity and storage limitations?).
2. Iterate to qualify your matching dimensions for African realities. For example, could a business problem’s feasibility improve by adapting workflows to match Ghanaian operational patterns? Could proxy data from similar African markets supplement limited local datasets?

Understanding these matching dimensions is critical when developing AI use cases in Africa, especially in markets like Ghana where digital transformation is accelerating but still faces structural constraints.

Imperative 3: Iterate your matching exercise with African realities in mind

Once you’ve defined the data and business dimensions for your AI use case in an African context, begin the matching and iteration process. Rather than having business teams define problems and passing them to data scientists (or vice versa), build integrated teams from the start. This is particularly important in African contexts, where deep domain knowledge about local markets and practices must inform technical approaches.

The matching exercise in African contexts follows three phases:

1. Assumptions: Clearly articulate underlying assumptions for both dimensions with African contexts in mind. For business problems in Ghana, list conditions under which the problem occurs, considering local business practices, customer behaviors, and market realities. For data aspects, identify variables characterizing available datasets and additional sources relevant to Ghana. Ideally, these assumptions should suggest solution paths appropriate for African technological environments.

For example, when Ghana’s Mazzuma payment platform developed an AI fraud detection system, they began by articulating assumptions about typical fraud patterns in Ghana’s mobile money ecosystem and data variables accessible through their platform.

1. Validation: Increase confidence in business and data assumptions through research grounded in African realities. For business problems, conduct in-depth interviews with Ghanaian process owners and stakeholders familiar with local conditions. For data, verify that datasets exist, are accessible given local infrastructure, and have sufficient quality to support the use case within African technological environments.

When validating their fraud detection assumptions, Mazzuma interviewed mobile money agents across different regions of Ghana and assessed data quality from multiple transaction sources, recognizing different patterns in urban centers like Accra versus rural areas.

1. Insight: This is where close matching between dimensions occurs, blending data science with domain expertise specific to African contexts. Can the problem be sustainably solved with available resources in Ghana? Are organizational or workflow adjustments needed to address business challenges? Is additional data required to increase confidence? Do proposed AI models work within African technological constraints?

Mazzuma discovered that certain fraud patterns were unique to specific regions and market segments in Ghana, requiring location-specific model adjustments and additional data sources to improve detection accuracy.

The answers to these questions drive feedback loops and determine needed iterations. Value drivers become clearer as matching improves, allowing for preliminary estimates of business benefits tailored to African markets.

Set defined endpoints for use-case execution in African contexts, where organizational pressure may push for premature scaling. Establish timeframes appropriate for local implementation realities, clear metrics relevant to African markets, and well-defined outcomes. Organizational learning is particularly crucial in African markets, where AI implementation experience may be limited.

Imperative 4: Plan for scaling your AI use cases early in African environments

Use cases should be essential components of your AI strategy in African markets. Successful cases increase certainty about where real AI-driven business value exists in African contexts. However, individual use cases won’t drive ROI; value comes when applications scale across African operations. Early planning for the transition from successful use cases to transformative projects should address Africa-specific considerations:

• Will the scaled use case contribute to strategic objectives relevant to African markets (e.g., reaching unbanked populations, addressing infrastructure gaps, improving agricultural yields)?
• Does the use case solve repeatable business issues worth addressing with long-term AI solutions in African contexts?
• Are resources and budgets sufficient for scaling in markets like Ghana, where technology investments compete with numerous other priorities?
• Is the technical foundation scalable given African infrastructure realities? What standards should apply across use case portfolios in markets with varying technological capabilities?
• Are high-quality data sources sustainable long-term in African environments with potential connectivity and storage challenges?
• Can security, compliance, and ethics be managed at scale, considering Africa’s evolving regulatory landscapes? In Ghana specifically, how does the use case align with the Data Protection Commission’s requirements?
• Does the organization have skills and readiness to deploy at scale in markets where AI talent may be limited? In Ghana, are there partnerships with institutions like the University of Ghana or Kwame Nkrumah University of Science and Technology to support talent development?
• Is there clear budgetary responsibility and business ownership for embedding AI solutions into business processes in African operational environments?

Most African organizations run multiple use cases simultaneously, which works when governed by clear internal frameworks. Organizations should focus AI strategies on use case portfolios enabled by common technology and data backbones adaptable to African conditions.

When scalability is prioritized early rather than treated as an afterthought, AI use cases can transition from exploration to production in African environments, delivering meaningful business value over the long term despite challenging implementation conditions.

Conclusion

Business problems/opportunities and datasets can form powerful combinations for AI strategies in African markets. Successful use cases are central to finding business value, but the journey is long, iterative, organizationally complex, and structured—with additional layers of complexity in African contexts.

The more your “organizational algorithm” practices the use-case iterative muscle in African environments, the faster and better the matching becomes. Leadership, business transformation vision, and accountability are crucial success factors in markets like Ghana, where AI adoption is accelerating but still navigating unique challenges.

As Ghana’s burgeoning technology sector demonstrates, most AI “overnight successes” in Africa actually represent years of methodical work, adaptation to local conditions, and persistence through multiple iterations. Organizations that master these four imperatives for AI use case development will be positioned to unlock substantial value in Africa’s rapidly evolving digital landscape.