This article was contributed to techCabal by Eric Mumene.

Is your business leveraging artificial intelligence? If not, you’re not alone. Andela’s research shows 61% of enterprise organisations haven’t adopted artificial intelligence (AI) tools. However, the landscape is rapidly evolving, with over 2,400 businesses in Africa specialising in AI. Statista projects the Generative AI market in Africa will soar to a staggering US$1.51 billion this year alone, with forecasts indicating a monumental rise to US$3.8 billion by 2028. 

But the reward is worth the effort, as companies failing to adapt risk falling behind more forward-thinking competitors. AI tools can make quicker work of large data sets and leverage the data companies already have. While most companies collect and store tons of data, they still need to utilise the revolutionary AI tools that can analyse and act upon that data intelligently. 

To support this significant digital transformation, we’ve created a design thinking model[pdf] with four steps to help businesses start an AI project. 

1. Determine a use case

Understanding where AI excels and where a business can most benefit is a great starting point. A few use cases include summarisation, documentation, content creation, design, programming, or personalisation. While there are many more use cases, we recommend starting within one of these realms. 

Many businesses need help managing vast quantities of unstructured data, such as processing countless PDFs to produce letters and legal findings, which traditionally consumes considerable manual effort in scanning and reviewing. Andela engineers have helped this process by leveraging ChatGPT into the business’s architecture to summarise the data, create content, and enable valuable conversations through prompts. As a result, the team has achieved an 80% reduction in time spent on researching and drafting, significantly streamlining their document processing workflow. 

Internally, Andela also leverages AI within the Andela Talent Cloud to efficiently automate and manage the complete 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 touch points in the hiring journey to pinpoint the best engineers for the roles and skills required. 

Generate a company survey 

To get started, gather input from your company and home on where AI can be the most beneficial. We recommend generating a survey across the company to help with data-driven decision-making. Then, a steering committee was set up, comprising champions from all stakeholders related to the identified business problems. By having advocates across the business, you gain buy-in across the organisation to help get the transformational changes you want.

Explain AI to your team

Explaining AI and its potential is crucial to gathering the proper use cases. All your stakeholders likely have the info you need; they just need to know how it can work. It’s well worth speaking with your engineers to present various possible business use cases to the team or bringing in a consultant who can educate your teams on how AIs are trained. 

2. Build a business case 

Next, leverage the data and input collected from the survey or committee to prioritise. Is there an overwhelming amount of employees interested in AI support for a particular area? For example, the summarisation of information, accounting, or personalising interactions with clients. 

Aggregate these results, identify common themes, and align them with overarching business benefits and a solid business case. Potential benefits may include cost reduction, productivity enhancement, revenue generation, competitive advantage, a deeper understanding of AI capabilities, or enhanced employee satisfaction. 

Incorporating a business case and ROI analysis will help determine the focal points for team development and the business objectives your generative AI project will advance. 

3. Validate your customer journey

Before you start any project, remember to stay focused on your customer. Get a clear picture of your customer journey and examine the pain points across awareness, consideration, decision, service, and advocacy. How does AI solve a real customer problem? 

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

4. Define measurement

Lastly, understand what metrics you will use to measure success and ROI. Ask yourself questions that you will get from the business to help define these. You can consider questions like: 

• How is user engagement measured? 
• How does AI help with retention through hyper-personalisation? 
• How much can we reduce cost? 
• How fast can a workflow or process be improved? 
• What 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 your AI meets ethical standards? 

Prerequisites 

Remember that as you define your project, you will also need a solid team to bring this vision to life.

As new models and Generative AI processes mature and evolve, your team must be equipped with the relevant skills. The engine’s success 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 titles are also emerging, such as AI content design engineers, ethical engineers, AI auditors, AI security engineers, and prompt engineers. 

How does Andela help businesses in this space?

Andela helps businesses streamline and automate their AI initiatives. Our AI and machine learning (ML) solutions power big data models and reduce the labour-intensive processes associated with them. 

Our GenAI Impact Assessment[pdf] is a tailor-made six-week program that focuses on identifying and realising a GenAI business solution. We set up a wholly managed team of experts to ensure the program’s success from start to finish. We can also help set up AI talent and teams at the production stage, meeting you anywhere in your AI journey. 

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

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