As the global technology industry accelerates investment in artificial intelligence, one of Nigeria’s technology professionals, Joshua Asogwa, has called for greater emphasis on efficiency, reliability and practical value in the development of AI-powered products.

Asogwa, whose professional experience spans telecommunications engineering, semiconductor technology, artificial intelligence and product management, said the future of technology would depend not only on the sophistication of intelligent systems but also on their ability to solve real problems efficiently and affordably.

In an interactive session with newsmen, he said the growing computational demands of artificial intelligence had made energy efficiency a critical consideration in the design of digital products and the infrastructure supporting them.

“AI is making digital products more powerful, but greater computational capability also creates an efficiency problem. Devices still have battery limits, infrastructure has energy costs and networks have capacity constraints,” he said.

He added that technology developers must look beyond increasing computing power to consider how efficiently that power could be delivered to users.

“We cannot think only about making systems more powerful; we have to think about how efficiently that power is delivered,” Asogwa said.

With experience across Europe, Africa and the United States, Asogwa has worked in telecommunications engineering and AI product development, combining technical expertise with business strategy to develop systems that deliver measurable results.

A First Class graduate of Electronic Engineering, he is also an Erasmus Mundus scholar and holds an MBA from INSEAD, with additional studies at the Wharton School.

His professional experience includes telecommunications engineering roles at Qualcomm and Sequans Communications, where he worked on low-power systems, performance evaluation and engineering process improvements.

At Qualcomm, Asogwa said he was involved in defining requirements for Low Power Mode features on a 4G chipset, coordinating activities across more than 12 global teams and senior stakeholders.

According to him, the resulting features reduced device power consumption by approximately 25 per cent, demonstrating how improvements at the engineering level could translate into tangible benefits for users.

“The lesson for me was that very technical, system-level improvements can produce a very simple user benefit: the device operates more efficiently,” he said.

He explained that the increasing number of connected devices, including smartphones, vehicles, Internet of Things devices and industrial technologies, had made energy-conscious computing more important, particularly as AI expanded the demand for processing power.

“The goal should be to increase useful computing capability per unit of energy,” he said.

At Sequans Communications, Asogwa also developed a Python-based framework for evaluating Band Scan and Fast Scan algorithms used in device network-connection procedures.

He said the framework established a consistent methodology for comparing algorithm versions by defining key performance indicators, automating the extraction of performance data from system logs and enabling engineers to assess results systematically.

He also designed Python-based monitoring systems to support earlier defect detection during low-power stability testing, helping to improve engineering efficiency and delivery timelines.

According to him, such applications demonstrate the importance of data analytics in identifying technical problems early and improving decision-making.

“ If you cannot measure performance consistently, optimisation becomes much harder,” he said.

Beyond telecommunications, Asogwa has moved into AI product leadership, focusing on translating complex technology into solutions that address practical user needs.

He co-founded Scolarité, an AI-powered personalised-learning initiative aimed at improving educational outcomes, particularly for underserved students.

He said the project involved research with students and educators to identify learning challenges and translate them into product requirements, while his responsibilities included product strategy and collaboration with a cross-functional team on the platform’s architecture and development.

The experience, he explained, reinforced the importance of prioritisation and resource management in building technology products, particularly when operating with limited funding.

He also worked in AI analytics through Aeone, where he led product strategy and roadmap activities, collaborating with engineering, data science and design teams.

One of his key areas of focus was developing performance measurement frameworks and leading API integrations to improve real-time visibility into the return on investment of creator and brand campaigns.

Asogwa said measurable outcomes should remain central to technology development, arguing that teams must establish clear indicators of success before investing resources in optimisation.

“Without well-designed metrics, teams confuse activity with progress. I believe strongly in defining success before optimising toward it,” he said.

He noted that public discussions about artificial intelligence often concentrated on model capabilities while paying insufficient attention to the infrastructure, product design and user experience required to make AI dependable and economically sustainable.

“AI exists within a larger system — there is infrastructure underneath the model, a product around it and a user interacting with it,” he said.

He added that developers and technology companies must ask whether their systems were efficient, reliable, scalable and capable of solving meaningful problems.

According to him, the United States remains well positioned to advance AI development because of its strengths in semiconductor technology, cloud infrastructure, research and entrepreneurship.

However, he said continued progress would require infrastructure capable of supporting growing computational demands without a disproportionate increase in energy consumption.

He identified opportunities for innovation across hardware, software, systems architecture and product design, particularly as AI functionality becomes increasingly integrated into everyday devices and industrial systems.

Asogwa attributed his approach to the combination of engineering knowledge and business education, saying his studies at INSEAD and the Wharton School strengthened his understanding of product strategy, entrepreneurship, technology strategy and organisational decision-making.

He said technical feasibility must align with strategic value, particularly in an environment where engineering resources, capital and time are limited.

Looking ahead, Asogwa said he intends to continue working at the intersection of intelligent systems, product innovation and efficient digital infrastructure, with a focus on developing AI-enabled products and helping technical teams measure and improve performance.

“My contribution is not AI for the sake of AI. It is building intelligent systems that are useful, scalable, reliable and efficient,” he said.

He predicted that technology would become increasingly intelligent and less visible to users, who would judge products by their performance rather than the complexity of the systems behind them.

“Users will not care which model or algorithm is running behind a product. They will care whether it works — whether it is fast, reliable, affordable and useful,” Asogwa said.

He maintained that the next generation of technology leadership would depend on combining deep technical expertise with product thinking to ensure that innovation delivers meaningful economic and human value.