A Simple Guide For African Businesses To Implement AI Transformation
With AI taking center stage in business circles, it is no surprise that African business leaders are beginning to question how to embark on an AI transformation initiative that will deliver consistent enterprise growth.
Make no mistake; Companies that do not adopt AI within the next five years will struggle to innovate and scale at the required pace.
In this article, I will outline the fundamental stages of AI transformation and how your company can adopt and implement safe and responsible AI for your business.
The Key Stages In AI Transformation
1. Identify Your Key Objectives: This is the most important part of this process. If you do not have a destination, it is better not to embark on the journey, right? You must answer the question: why do we need AI?
A few objectives for your business can include the following;
- Improving user experience across your products.
- Generating key business insights from your data.
- Improving internal processes by automating repetitive tasks.
- Intelligently identifying risks and mitigating them quickly.
Once you have identified your goals and objectives, the next step is to extract the relevant data needed to achieve those objectives. To measure and validate these objectives, come up with key metrics to help you validate them at the end.
2. Data Extraction: AI transformation is nothing without data, so it is imperative to build or maintain a good repository of your datasets.
You can extract raw datasets from your database or any storage medium used within your business.
For example, if a fintech business wants to improve the user experience and enhance customer payment discovery for its customers, it would need to extract all the raw payment types and service IDs from the database into a text file. This text file would serve as the foundational dataset required for the next phase, which involves data transformation.
3. Data Transformation: Whilst data is key for a successful AI transformation, the quality and structure of the data are also very important. Transforming the extracted raw data from the previous step into a structure that is fit for purpose is key.
Whether you plan to fine-tune an existing AI model or leverage some SaaS (Software as a Service) AI solution eventually, you will have to structure the data properly. You can build your own custom data transformer tool if you have an engineering team or use some Extraction Transformation and Loading (ETL) tools for this.
For example, the fintech business would have to transform the raw data extracted in phase 1 into a format that demonstrates the relationships between merchants and the services they offer. You can use a JSON (Javascript Object Notation) or Markdown file structure for this. These formats are easy to interpret by large language models (LLM) for predictions and inferencing.
4. Processing: At this stage, your data is ready and set for model training or indexing within a SaaS AI solution. Whatever approach you choose, it is time to upload the transformed data and wait for the ready-to-use AI solution that is now knowledgeable in your data. The output of this stage is usually an AI assistant or chatbot that is ready to take questions from customers or employees.
5. Storyboarding: This stage is where we validate the output of the already-trained AI solution.
An internal or closed external focus group of testers can be set up at this stage to validate the outputs from the AI solution. A set of questions for several use cases are generated and used for the validation. This stage is as critical as the first stage because this is where AI alignment happens and ensures it is fit for rollout.
The results of this stage are checked against set objectives. The outcome can be a trigger jump back to the data transformation stage to ensure the real objective is achieved. This iterative stage is repeated until the results align with the set goals.
6. Implementation and Monitoring: At this stage, you already have a working AI solution fit for use within the business or product for your customers.
It is usually advisable to stage your rollout. You can start with a private or public beta stage to help you gather feedback and iterate quickly before the full launch.
Monitoring your AI solution is also very critical. This gives you a proactive way to view and respond quickly to how people are using your AI solution. There are available tools for this depending on the route you take to deploying your solution.
Conclusion
As an African business, the time to start adopting AI within your business or into your product offerings is now. The way we work has shifted since the rise of AI assistants and agents. Whilst you are at it, keep an eye on the associated risks so you do not incur unnecessary costs in the course of transforming your business. I will talk about these risks extensively in my next article. Stay tuned!
Good luck and happy transformation.
About the Author
Uchenna Okpagu is an AI and machine learning expert, dedicated to the ethical and progressive integration of AI, with a focus on emerging markets like Nigeria and Africa. He has up to 20 years of working experience in banking and fintech as a Software engineer and a Technology leader.
He is a Certified AI Scientist (CAISTM) accredited by the United States Artificial Intelligence Institute and currently serves as the Chief AI Officer at Remita Payment Services, where he leads AI innovation and adoption across its product offerings and the enterprise.
Connect with him on LinkedIn.
