Of Nigeria’s Artificial Intelligence Adoption, Access

Beyond the hype of global access and adoption of Artificial Intelligence, experts have insisted that Nigeria’s AI future depends on adoption, not access, writes Emma Okonji

Experts and a Nigerian-born technologist and strategist are making a strong case that AI adoption, not access, will decide Nigeria’s future.

According to them, Nigerians have embraced AI faster than the rest of the world. Technologist, Tochi Kanu-Ivi, is of the view the harder, more valuable work begins after they first try it.

Each time Kanu-Ivi had a conversation with Nigerian professionals about artificial intelligence, she never asked whether they have tried it, because almost all of them have tried it.

“That question is settled. But the honest one is harder, which is has it actually changed how you work? For most people, the answer is still no,” Kanu-Ivi said in an interview from Houston, where she works as a Customer Success Manager at UiPath, guiding Fortune 500 companies through the messy work of putting new technology to use.

The Supporting Numbers

Certain numbers have continued to back Kanu-Ivi’s framing. In a 2024 Google–Ipsos survey, 70 per cent of Nigerian respondents said they had used an AI application such as ChatGPT, Gemini or Claude in the previous year, compared with 48 per cent across the 21 countries surveyed. By the measure most commentators emphasise, exposure and initial use, Nigeria is not lagging, and in some respects, it is ahead.

What that statistic conceals, Tochi argues, is a second and far more consequential divide: the gap between people who have experimented with AI and organisations that have adopted it in ways that meaningfully improve productivity, service delivery or employment outcomes.

The Distinction

Globally, the World Economic Forum projects that AI and information-processing technologies could displace approximately nine million jobs while creating 11 million by 2030. In Nigeria, research from the International Finance Corporation estimates that roughly 28 million workers will need digital skills by the end of the decade.

But Kanu-Ivi said: “Awareness can be created in an afternoon workshop. Adoption cannot, and that is the part everyone underestimates.”

Emerging Framework

Kanu-Ivi’s response is a methodology she called EDITT — Empathise, Diagnose, Ideate, Test, Translate, which is a five-step approach aimed not at engineers but at the non-technical professional trying to make AI useful in an ordinary job. According to her, the framework begins by understanding a person’s actual work and fears, diagnoses the specific barrier in the way, generates realistic use cases, tests one in a low-risk two-week experiment, and ends by translating the result into measurable value the worker can show an employer. She has also published the framework as a free online self-assessment, which places a professional on a four-level readiness scale and generates a personalized 30-day plan.

She is careful about what she claims for it. EDITT is published as “Version 0.1,” and she describes it as an emerging, field-informed framework. “I’d rather be honest that this is an early, practical framework that I’m still developing. It’s a tool for thinking, not a guarantee,” she said.

Aligning with Kanu-Ivi, an AI Advisor at Mithril Advisory, Chinaza Orji, who reviewed the framework, said that restraint is itself notable in a field crowded with absolute promises.

According to Orji, “The discipline I look for is whether an approach starts with the business problem or the shiny tool. This one starts with the problem and the person. That’s the right instinct, even at an early stage. The open question, as with any young framework, is evidence; whether it produces results across many real organisations, not just a few.”Kanu-Ivi who did not dispute the point, said building that evidence remained precisely the work ahead.

Credibility Built on Implementation

Kanu-Ivi’s perspective comes from years spent working on enterprise technology adoption, particularly the less visible work that often determines whether a project succeeds. New technology may be available, but the real test is whether the people expected to use it actually change how they work. That focus has drawn recognition. Kanu-Ivi was named a 2026 finalist in ISG’s Women in Digital Awards, in the AI Champion category, and recognised among SuccessCOACHING’s 2025 Top 100 Customer Success Strategists, a list decided by direct voting among customer success professionals worldwide.

The track record informed a blunt critique of how many organisations approach AI.

According to Kanu-Ivi, “AI adoption fails when it’s treated as a software rollout. It’s an operating change. You have to understand the workflow, the people, the data, the risks, and the value before you decide what to automate. Skip that, and you get expensive pilots nobody uses.”

Managing Director of Haven Homes Nigeria, Ufuoma Ilesani, said the pressure was familiar. “There’s a real temptation to buy a tool because a competitor announced one. But the licence means nothing if the staff weren’t brought along and the data wasn’t ready. The hard part isn’t the enthusiasm. It’s the discipline between enthusiasm and outcome,” she said.

The Diaspora Question

Kanu-Ivi’s perspective also reflected a growing role for diaspora professionals in Africa’s technology conversation: people with experience implementing technology inside global enterprises who are now turning that experience toward African organisations and workers.

“The diaspora isn’t a solution by itself, and Africa doesn’t need lectures from abroad. But there’s a specific, useful thing some of us can contribute. Not building the models, but the implementation know-how. How you actually move an organisation from interest to measured value. That part travels,” she said.

It is a framing that sidesteps the usual emphasis on who builds AI in favour of who can help institutions use it responsibly. The question carries particular weight in sectors central to Nigeria’s economy, from banking and insurance to public services, healthcare, logistics and agriculture, where data is often fragmented and change-management capacity thin.

From Hype to Hard Work

Whether a single early-stage framework can shift any of this, is by Kanu-Ivi’s own admission, unproven. What she is more certain of is the underlying argument that Nigeria’s enviable head start in AI awareness will mean little unless it is converted into the slower, less glamorous work of adoption.

According to her, “The countries that win the next decade won’t be the ones that adopted AI fastest. They’ll be the ones whose people learned to work with it, proved the value, and taught the next person. Nigeria has the hardest part: a population that’s genuinely curious and already using these tools. The question is whether we treat that as the finish line or the starting line.”

For now, she is betting on the latter, and on the unfashionable idea that the future of AI will be decided less by access than by what people do after they first try it.