Closing the AI ​​gap: Practical steps for corporate adoption in Africa
Closing the AI ​​gap: Practical steps for corporate adoption in Africa

This article was contributed to techCabal by Eric Munene.

Does your company use artificial intelligence? If not, you’re not alone. Andela’s research shows that 61% of companies have not adopted artificial intelligence (AI) tools. However, the landscape is evolving rapidly. In Africa, over 2,400 companies specialize in AI. Statista predicts that the generative AI market in Africa will grow to a staggering $1.51 billion this year alone, with forecasts predicting a massive increase to $3.8 billion by 2028.

But it’s worth the effort, because companies that don’t adapt risk falling behind more advanced competitors. AI tools can process large data sets faster and leverage the data companies already have. While most companies collect and store vast amounts of data, they still need to leverage the revolutionary AI tools that can intelligently analyze and process that data.

To support this significant digital transformation, we have developed a four-step design thinking model (pdf) to help companies start an AI project.

1. Determine a use case

Understanding where AI excels and where a business can benefit most from it is a good place to start. Some use cases include summarization, documentation, content creation, design, programming, or personalization. While there are many more use cases, we recommend starting in one of these areas.

Many companies need help managing massive amounts of unstructured data, such as processing countless PDFs to produce letters and legal opinions, which traditionally requires significant manual effort in scanning and reviewing. Andela’s engineers supported this process by integrating ChatGPT into the company’s architecture to summarize the data, create content, and enable valuable conversations through prompts. This enabled the team to reduce research and drafting time by 80% and significantly streamline their document processing workflow.

Internally, Andela also uses AI within the Andela Talent Cloud to efficiently automate and manage the entire global talent lifecycle. It’s a mix of a fantastic matching team and AI, which is why we have a 96% success rate. Powerful AI matching algorithms learn from hundreds of touchpoints in the hiring process to find the best engineers for the roles and skills required.

Create a company survey

First, gather information from your business and from home about where AI can be most useful. We recommend conducting a company-wide survey to support data-driven decisions. Then, a steering committee was established, made up of advocates from all stakeholders related to the business problems identified. By having advocates from across the business, you gain buy-in from the entire organization to make the transformational changes you want.

Explain AI to your team

To find the right use cases, it’s important to explain AI and its potential. All of your stakeholders probably have the information you need, you just need to know how it can work. It’s worth talking to your engineers to present different possible use cases to the team, or bringing in a consultant who can train your teams on AI.

2. Create a business case

Next, use the data and input from the survey or committee to set priorities. Are there an overwhelming number of employees interested in AI support for a particular area? For example, information aggregation, accounting, or personalizing interactions with customers.

Summarize these findings, identify common themes, and link them to overarching business benefits and a solid business case. Potential benefits could include cost reduction, increased productivity, revenue generation, competitive advantage, a deeper understanding of AI capabilities, or increased employee satisfaction.

By incorporating a business case and ROI analysis, you can determine the focus for team development and business goals that will advance your generative AI project.

3. Validate your customer journey

Before starting a project, remember to keep your customer in mind. Get a clear picture of the customer journey and examine the pain points in awareness, consideration, decision, service and advocacy. How does AI solve a real customer problem?

Once you have defined your current customer journey, you can better understand what a new one might look like.

4. Define measurement

Finally, be clear about what metrics you will use to measure success and ROI. Ask yourself questions that the company will ask you to define those metrics. You might consider questions like these:

  • How is user engagement measured?
  • How does AI contribute to customer loyalty through hyper-personalization?
  • How much can we reduce costs?
  • How quickly can a workflow or process be improved?
  • Which programming languages ​​are your developers familiar with and is the architecture scalable?
  • What data do you need to make your AI successful?
  • How can you ensure that your AI meets ethical standards?


When defining your project, remember that you also need a solid team to turn that vision into reality.

As new models and generative AI processes mature and evolve, your team must be equipped with the appropriate skills. The success of the engine depends on the models themselves and a supporting architecture and ecosystem.

Depending on the use case, you’ll likely need large language model deployment engineers, data engineers, and software developers. New job titles are also emerging, such as AI content design engineers, ethics engineers, AI auditors, AI safety engineers, and prompt engineers.

How does Andela help companies in this area?

Andela helps companies optimize and automate their AI initiatives. Our AI and machine learning (ML) solutions support big data models and reduce the labor-intensive processes associated with them.

Our GenAI Impact Assessment (pdf) is a tailored six-week program focused on identifying and realizing a GenAI business solution. We assemble a fully managed team of experts to ensure the success of the program from start to finish. We can also help assemble AI talent and teams in the production phase and meet you anywhere on your AI journey.

Explore AI solutions, build and test infrastructure and AI models, create and define a model, and then learn how to scale it with the help of Andela’s experienced engineers.

Eric is IT Director at Andela and has over ten years of experience driving business growth and profitability. Eric is dedicated to innovation and ensuring IT is a strategic corporate asset.

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