7th NAMS Western Region Convention

The biggest untapped opportunity is our own data. Nigeria produces a lot of biological information, from hospital laboratory results to soil, water and crop samples, and very little of it is digitised, organised or shared. AI is only as useful as the data it learns from, so whoever builds good local datasets will be able to build tools that fit Nigerian problems instead of importing ones trained elsewhere.

Close behind that are diagnostics and surveillance: reading microscopy and culture results faster, and tracking patterns of antimicrobial resistance across labs. None of this needs frontier research. It needs people who understand both the biology and the tools.

In the unglamorous parts of the work. Most labs and research groups still lose hours to manual record-keeping, re-typing results, and searching literature by hand. AI tools that already exist, and are often free, can summarise papers, help draft protocols, organise data and flag errors.

The next step is analysis: image recognition for microscopy and colony counts, and simple models on existing lab data. Starting there saves time today and builds the data habits that more ambitious AI work depends on later.

Look for problems people already pay to solve. Agriculture is one: crop disease detection, biofertilisers and biopesticides, and reducing post-harvest losses. Food processing is another, where many traditional fermentation processes could be standardised and made safer. Water quality testing, bioremediation of polluted sites, and diagnostics for clinics outside the big cities are all real markets.

The businesses that last usually start as services, such as testing, data analysis or contract research, before they become products. AI makes those services cheaper and faster to deliver.

Research is mostly rewarded for being published, not for being used, so very few projects start by asking who needs the result and whether they would pay for it. That needs to change at every level.

Practically, it means earlier conversations with industry, clinics and farmers; technology transfer offices that actually help researchers protect and license their work; shared access to equipment; small grants for turning a result into a prototype; and more collaboration between science and business or computing students. Partnerships like the ones at this convention are part of that bridge.

Money, infrastructure and isolation. Lab work is expensive, power is unreliable, and many students do not know anyone who has built a science business.

Each has a workaround. Start with services and small pilots that need little equipment, and use shared or university labs. Use cloud tools and AI to do analysis that used to need expensive software and specialist staff. Learn the regulatory path, such as NAFDAC, early rather than late. And build a team that mixes skills, because the scientist rarely needs to be the coder, the marketer and the accountant as well. Associations like NAMS are where those teams can start.

It would look like local companies selling tools and services to hospitals, farms, food processors and government, built on Nigerian data and Nigerian expertise. Universities would have working incubators, students would graduate comfortable with both lab work and data, and investors would understand that science businesses take longer to grow.

What needs to happen now is simple to say: digitise and share data responsibly, teach basic data skills such as Python and bioinformatics alongside microbiology, and create more collaboration between science departments, technology companies and industry.

Start with the problem, not the technology. Write down in one paragraph who has the problem, how they deal with it today, and why your idea is better. Then talk to at least ten of those people before building anything.

Next, find one person whose skills complement yours, whether in business or in technology. Use the free AI tools available today to learn quickly and test ideas cheaply. Enter competitions like the research pitch at this convention, and ask for mentorship. You do not need to know everything before you start; you need to start small enough that being wrong is cheap.