By Dickson Omobola

As the future of business becomes increasingly digital, it is essential for leaders and engineers alike to embrace automation in a way that prioritises both growth and human value. In this interview, Oluwasegun Haziz, a seasoned software engineer, discusses how intelligent software solutions can unlock scalability and productivity. Excerpts:

You’ve worked in software engineering for nearly a decade. What inspired your focus on building systems that drive efficiency and productivity?

Over the years, I have seen how the right software can completely change how a business operates, from cutting down repetitive tasks to unlocking new ways of working. What drives me is solving real-world problems. I enjoy building tools that not only work technically, but help people get more done, with less stress and friction. Whether it’s a growing startup or a large organisation, the common goal is efficiency, and that’s where intelligent automation shines. When systems work smoothly in the background, teams are free to focus on innovation and growth.

In your own words, what does intelligent software really mean? How is it different from just regular automation?

Traditional automation is like a to-do list, it follows a set of rules without asking questions. Intelligent software, on the other hand, is more like a smart assistant. It learns, adapts, and sometimes even predicts. This is not just about automating a task but noticing patterns and flagging unusual trends before anyone asks. For instance, instead of just sending an invoice, intelligent systems can flag unusual billing patterns or predict late payments based on customer history. That layer of insight is what makes it intelligent. It combines logic, data, and sometimes machine learning to help businesses scale smarter, not just faster.

As a seasoned professional, how do you ensure solutions are human-friendly when building them?

It’s easy to fall into the trap of over-engineering, building systems that are technically impressive but hard for people to use. My approach is always to start with the end-user. I ask: Will this make someone’s day easier? Will they trust it to do the job right? Whether it’s a backend system or a customer-facing tool, the design has to feel intuitive. Intelligent software should feel like a helpful teammate, not a mysterious black box. Communication, transparency, and simplicity go a long way in building trust and adoption.

Focusing on balancing technical excellence with human needs. How does that philosophy show up in your day-to-day work?

I think of myself not just as a coder, but as a bridge between business needs and technical solutions. A lot of my time is spent talking with non-technical stakeholders, understanding their pain points, their goals, and what success looks like for them.

Then I translate that into systems that actually solve those problems. I also make sure the team I work with understands the why behind what we’re building. That alignment makes us faster, more focused, and better at delivering value.

From a productivity angle, what are some of the first areas you recommend automating in a growing organisation?

Start with the tasks that are repetitive, rule-based, and time-consuming—things like report generation, data entry, invoicing, or customer email responses. These might seem small but they are great entry points. Next, look at decision-making bottlenecks. Are people waiting on approvals or reports? Can a system surface that information automatically? Automate to free up mental space, so your team can focus on strategy, creativity, and growth. Also, always automate with intention; it is essential to understand the process before you try to improve it. That’s where real productivity lives—not in working faster, but in working smarter.

Are there any pitfalls or common mistakes you see companies make when they try to scale with automation?

The biggest one is automation for the sake of automation. Just because you can automate something doesn’t mean you should. Start with a clear business goal. Another pitfall is failing to invest in training or change management. People need to understand what’s changing, why it’s changing, and how to use the new system. Lastly, I’ve seen companies neglect the data side. Intelligent software is only as good as the data it runs on. If your data is inconsistent or incomplete, the software won’t deliver the expected value. You need clean, connected data, and that often takes just as much effort as the automation itself.

Looking ahead, what excites you the most about the future of intelligent software?

What excites me most is the growing accessibility. We’re entering an era where non-engineers can build workflows using low-code or no-code platforms, and AI tools are enabling small teams to do the work of much larger ones. It’s a game-changer. I’m also inspired by how intelligent systems are being used in high-impact areas, like predicting crop yields in agriculture, diagnosing diseases in healthcare, ensuring inclusivity for the unbanked or making education more adaptive and inclusive. We’re no longer just building apps; we’re building systems that touch lives.

As a seasoned expert, that’s what it’s all about—using our skills to create something bigger than ourselves.