Blessing-Kayode: I’m Focused on Applying AI to Reduce Cognitive and Operational Burdens in Complex Workflows

United State of America based Nigerian, Eyimofe Blessing-Kayode, who is currently on the verge of revolutionizing the restaurant industry with Software Engineering, had a chance meeting with THISDAY’s Raheem Akingbolu and opened up on his adventurous journey in the tech world which has led to many accomplishments, including his ability to design the leading automation platforms that replace manual, error-prone operational workflows in high-volume restaurants across the United States.

With AI, a lot is happening across human endeavours, from technology to creativity and beyond. Yet it has been criticized in some quarters as a threat in the workplace and knowledge seeking. What is your take?

I see AI less as a threat and more as a force multiplier. Historically, every major technological shift has raised similar concerns, but what ultimately matters is how the tool is applied. AI does not eliminate the need for human judgment, creativity, or expertise. Instead, it changes where human effort is most valuable.

In practice, AI is best used to remove repetitive, low leverage tasks so people can focus on higher order thinking and problem solving. In the workplace, that means fewer hours spent on manual processes and more time spent on strategy, creativity, and decision making. In education and knowledge seeking, it can accelerate learning when used responsibly by helping people understand complex concepts faster rather than replacing understanding altogether.

You have had a taste of education in Africa and the United States. Can you draw a comparison, and what advice would you give to drivers of African education policies to meet world standards?

At the primary and secondary levels, education in Nigeria is extremely strong in terms of content and rigour. Students are exposed early to mathematics, sciences, and analytical thinking at a level that, in many cases, exceeds what is taught in the United States at the same age. That foundation produces students who are disciplined and intellectually capable.

Where the system begins to struggle is after that stage. Higher education in many parts of Africa relies heavily on theory, with limited opportunities for students to apply what they are learning in practical, real-world settings. Closing that gap requires sustained investment, not just from governments but also from private institutions and industry. Research funding, partnerships with companies, and project-based learning environments are critical to creating a more immersive and competitive educational experience. When theory is paired with hands-on practice, the talent is already there to compete globally.

From the last time we heard about you, we know you are a graduate of Mechanical Engineering and Mathematics from Northeastern University, Boston, Massachusetts. How did you venture into the world of tech?

My path into technology was less of a pivot and more of an extension of how I was already trained to think. Mechanical engineering and mathematics taught me how to break complex systems into components, understand failure points, and design solutions that work reliably under real-world constraints. Software became the most powerful medium to apply that way of thinking at scale.

While still in school, I began building software to solve practical, real-world problems, starting with Billbuds, a platform designed to help renters set up and manage essential utilities like electricity, internet, and gas when moving into new apartments. That experience exposed me to how fragmented and inefficient many everyday systems were and how thoughtfully designed software could dramatically simplify them. From there, I moved deeper into building data-intensive systems, which ultimately led me fully into software engineering.

We know how tough it is to land employment in two of the five topmost tech companies in the U.S., and you had to choose one when most people struggle to even get into one. Can you take us through that experience and how it contributed to where you are now?

I was fortunate to receive offers from both Microsoft and Google, which in itself was a challenging and humbling experience. Ultimately, I chose Google because its engineering culture and emphasis on large-scale, data-driven systems aligned more closely with the kind of problems I wanted to work on long-term.

Working at Google was formative because it exposed me to how large-scale systems are designed, evaluated, and maintained when reliability truly matters. The interview process required deep problem-solving and system design skills, but what stayed with me most was the rigour of decision-making once inside. I worked on systems that supported millions of users, where small technical choices could have outsized consequences. That environment trained me to think carefully about scalability, data integrity, and long-term maintainability, lessons that became foundational when I later built my own platforms.

Congratulations on co-founding a tech company that helps restaurants across the U.S. recoup funds and prevent revenue loss. How has that been?

Co-founding PrepProof has been both technically challenging and deeply rewarding. Restaurants operate on extremely thin margins, and many lose significant revenue due to operational errors, disputed transactions, and manual reconciliation processes that are difficult to manage at scale. We built PrepProof to automate those workflows, allowing operators to recover funds that would otherwise be written off as losses.

What made the experience especially meaningful for me was seeing the system operate in real environments across the country. PrepProof has processed and corrected millions of transactions and helped operators recover millions of dollars collectively. For many franchise owners, this meant the difference between absorbing losses and reinvesting back into their staff and operations. Building technology with that level of tangible impact reinforced my belief in solving hard, unglamorous problems with well-designed systems.

Going back to the issue of AI, which has generally been regarded as the future, what are you doing in that space right now given your drive for constant growth?

Right now, my focus is on applying AI to reduce cognitive and operational burdens in complex workflows. Many industries still rely on humans to make repetitive, error-prone decisions at scale, and that is where AI can be most effective. I have been working on systems that use AI for tasks like classification, anomaly detection, and decision support, always supported by strong underlying infrastructure to ensure accuracy and accountability.

My approach is practical rather than experimental. AI works best when it is embedded into systems that already understand the domain deeply rather than being treated as a standalone solution.

Finally, how do you plan to make use of AI in your next project?

In my next project, I plan to use AI to further automate high-friction operational processes, particularly in industries where expertise is scarce and errors are costly. The goal is not to replace human judgment but to amplify it by embedding intelligence directly into the tools people already use.

When applied thoughtfully, AI allows small, focused teams to operate at a level of scale and precision that previously required entire departments. That is where I believe its most meaningful impact lies.