At a time when Artificial Intelligence (AI) is reshaping industries globally, business leaders and technology experts in Nigeria say the country’s biggest barrier to AI adoption is not access to tools or infrastructure, but the quality of data powering them.
The issue took centre stage on Wednesday at the Lagos Chamber of Commerce and Industry (LCCI) AI Summit in Lagos, themed “Adopting AI for Your Business: Nigeria Realities and Case Studies,” where speakers identified weak data practices as a critical gap in Nigeria’s digital transformation journey.
Adeyinka Aderombi, Chief Digital Information Officer at Rex Insurance, delivered a direct assessment of the challenge. “AI is not your problem. Data is your problem,” he says.
His remarks highlight a persistent gap among Nigerian businesses, many of which generate large volumes of data daily but fail to properly track, structure, or utilise it. He says the problem lies not in the absence of data but in how organisations manage it. “Everybody has data, the question is whether you have it, tracking it, documenting it, labeling it, and archiving it,” he says.
AI adoption in Nigeria linked with data quality
Aderombi explains that AI systems depend on the quality of the data they process. “Without data, there’s no insight. Without insight, AI cannot make deductions and get insight,” he says.
His remarks highlight a persistent gap among Nigerian businesses, many of which generate large volumes of data daily but fail to properly track, structure, or utilise it. He says the problem lies not in the absence of data but in how organisations manage it. “Everybody has data, the question is whether you have it, tracking it, documenting it, labeling it, and archiving it,” he says.
He warns that poorly managed data can undermine AI outcomes and produce unreliable results. “If you have data, and data is not enough, it’s not clean, it’s not put together, any AI will build on that nonsense. It will predict stupid things to you,” he says.
This risk, often described in AI circles as hallucination, presents challenges in business environments where decisions around finance, operations, and customer engagement increasingly rely on automated insights.
This risk, often described in AI circles as hallucination, presents challenges in business environments where decisions around finance, operations, and customer engagement increasingly rely on automated insights.
Beyond accuracy, speakers also raised concerns about bias and outdated information embedded in datasets. Aderombi notes that AI systems can reinforce patterns already present in data, leading to skewed outcomes. “That bias is not intentional, it’s just what is in the data,” he says.
Speakers also addressed operational realities facing Nigerian firms. Many businesses still rely on fragmented systems, informal record-keeping, or manual processes, making it difficult to build reliable data pipelines. However, they say addressing the problem does not necessarily require complex technology.
Aderombi says organisations can begin with basic tools while improving discipline in data management. “You can start with basic Excel. Just make sure these data are kept, they are structured,” he says.
The urgency of addressing data challenges is increasing as more companies integrate AI into core business operations, including fraud detection in financial services and demand forecasting in retail. Experts warn that without strong data foundations, these initiatives may underperform or fail.
Segun Okuneye, Deputy Director, Strategic Business Initiatives at ipNX Nigeria, places the issue within a broader national context. While acknowledging AI’s potential, he says adoption in Nigeria faces constraints such as regulatory concerns, infrastructure gaps, and the need for coordinated digital frameworks.
He says practical examples show that AI adoption is already underway. “These are not abstract ideas they are real case studies that prove AI is not just for advanced economies, it is for us and here and now,” Okuneye says.
Okuneye calls for stronger capacity building and collaborative ecosystems to support businesses navigating the AI transition.
Across sessions, speakers consistently emphasise that data readiness will determine the success or failure of AI in Nigeria.
Adaku Inem, Project Manager at EldaKerr, highlights the link between structured data and AI performance. She distinguishes between different types of AI systems, noting that analytical AI depends heavily on organised datasets. “Analytical AI uses structured data in order to function. It finds patterns, trends, and predictions,” she says.
Her remarks underscore concerns that some businesses may prioritise visible AI tools, such as chatbots, while overlooking the underlying data infrastructure required to generate measurable value.
As Nigerian companies expand their AI ambitions, discussions at the LCCI summit indicate that outcomes will depend on how effectively organisations build, manage, and govern their data assets.
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