Firms urged to strengthen data systems for AI adoption

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Companies have been urged to strengthen their data management systems and improve access to quality datasets as they adopt artificial intelligence, with an analyst warning that hiring technically skilled workers alone may not translate into business value without the right organisational foundations.

AI and data analyst and applied AI practitioner Patricia Orji noted that Nigeria’s artificial intelligence conversation was growing rapidly, with more young people learning Python, machine learning, data science, and generative AI through universities, boot camps, and online courses.

However, she argued that the challenge extended beyond training people to use AI tools, pointing to the need for computing infrastructure, mentorship, real-world datasets, internships, research opportunities, and organisations where learners could apply their knowledge.

According to Orji, Nigeria’s AI challenge was increasingly a capability gap rather than simply a shortage of skills, because technical knowledge acquired by individuals did not automatically translate into the ability to develop practical solutions.

“Skills sit with individuals. Capability depends on the environment around them,” she stated in a note shared with The PUNCH.

She explained that a graduate could understand machine learning theory and earn several certificates but still struggle to build a meaningful solution without access to real problems, experienced mentors, and opportunities to work with real data.

Orji noted that developing AI solutions for real-world applications involved challenges often absent from classroom exercises and online tutorials. Outside training environments, data could be incomplete, business problems might not be clearly defined, and budgets could be limited. Practitioners also had to consider privacy, bias and user expectations when developing and deploying AI systems.

The expert added that artificial intelligence was not always the appropriate solution to a business problem, as some organisations might achieve better results through improved data management, automation or business intelligence.

“These lessons are difficult to learn through theory alone,” she said.

The analyst’s position highlights the importance of preparing organisational data systems before deploying AI tools, particularly where businesses lack reliable, well-organised information on which such systems can operate.

Companies seeking to benefit from AI must therefore consider not only the technical expertise of their employees but also the quality of their data, the clarity of their business processes and their understanding of what the technology can realistically achieve.

Orji warned that organisations could employ technically skilled workers and still fail to benefit from AI if their data was fragmented, their processes were unclear or their leadership lacked an adequate understanding of the technology’s capabilities and limitations.

She also identified infrastructure as a critical factor in developing Nigeria’s AI ecosystem, noting that AI development could require reliable broadband connectivity, cloud services, computing resources and access to quality datasets.

Where access to these resources was limited, she maintained, technical talent alone would not be sufficient to deliver meaningful results.

Beyond corporate investment, Orji called for a broader effort involving universities, companies, government, professional bodies, investors and technology communities to create opportunities for emerging AI practitioners to develop their capabilities.

She explained that universities had a role to play in developing talent, while partnerships with industry could expose learners to actual business challenges. Mentorship could help bridge the gap between technical knowledge and professional judgement, while research opportunities could teach emerging practitioners to test ideas properly.

Orji urged stakeholders to move beyond measuring progress in AI development by the number of people receiving training and certificates, stressing the need to examine whether learners had opportunities to practise, experiment and contribute to practical projects.

She maintained that Nigeria needed more people who could identify real problems, assess available data, select appropriate tools, develop responsible solutions and clearly explain the value of their work.

“The question, then, is not only: How do we teach more people AI? It is also: Where will they practise, who will mentor them, what infrastructure will they use, and which real problems will they work on?” she said.

Nigeria has seen growing interest in AI education and skills development, with universities, training providers and online learning platforms offering opportunities for people to acquire technical knowledge. However, Orji’s argument points to the need to connect such training with the infrastructure, professional experience and organisational support required to turn knowledge into practical applications.

She maintained that Nigeria had no shortage of people willing to learn AI but needed to create conditions that would enable them to apply their skills to commercially viable and socially relevant problems.

“The bigger challenge is creating the conditions that allow learning to become capability, and capability to become innovation, jobs, businesses and locally relevant solutions,” she added.

Justice has over three years experience spanning digital and print media. At The PUNCH, he currently covers the automobile sector with special interest in features and industry analysis.

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