Esther Awa is a product leader with over six years of experience delivering technology solutions across Africa, North America, and Australia. She has built her career at the intersection of education technology, SaaS, and digital transformation, working with both international and local organisations to launch products that scale impact, improve efficiency, and generate measurable growth.

Her career spans roles in education technology, software-as-a-service, AI/ML-powered analytics, and digital media. She has worked with global companies such as Xceleon LLC and Prazle Inc. in the United States, as well as Nigerian tech innovators like Miva Open University and Tespire. At Tespire, she leads cross-functional teams, taking products from concept to market while overseeing go-to-market strategy, pricing models, and adoption plans.

In this interview, Esther shares insights on leading cross-functional teams, integrating AI into user-friendly platforms, and scaling education technology products across diverse markets. She also discusses how she balances technical demands with business goals and the role of user research in building globally relevant solutions.

In your experience, what are the critical factors for successfully scaling education technology products in emerging markets like Nigeria compared to more mature markets?
In Nigeria, partnerships are important. Working with schools, associations, and telcos often drives faster adoption than relying only on online channels.

Infrastructure is also different. Products need to work on low-bandwidth, low-cost Android devices, and support multiple payment options like USSD, bank transfers, and wallets.

In more mature markets, you are dealing with better infrastructure and faster self-serve adoption. Users expect richer media, deeper integrations, and strong compliance with established standards.

How do you approach aligning diverse cross-functional teams around a shared vision when developing technology products for international markets?
I start by making sure everyone understands exactly what we are building, who it is for, and the outcome we want. I put that into a simple one-page document that anyone can read and refer to. Then I break it into a few clear priorities for each market and link each one to a measurable goal.

We hold discovery meetings where we agree on who is responsible for what and how decisions will be made.

Once we start building, I keep progress visible through weekly check-ins and shared Jira boards. The vision stays the same, but we adjust how we deliver so it works for each market while keeping the product experience consistent.

Can you discuss a specific challenge you faced when integrating AI and machine learning into existing platforms, and how you ensured the technology delivered real value to users?
I led the integration of AI-powered video editing and content generation into an existing platform that was already popular with non-technical users. The goal was to help them produce professional-quality videos faster without needing advanced editing skills.

The challenge was twofold. First, we had to make sure the AI outputs were accurate, on-brand, and relevant. Second, we needed to build trust with users who were new to AI-generated content and worried about losing creative control.

We started by training the AI on a combination of public and in-house datasets so it could better understand our audience’s preferred styles and formats. We are still building this product and gradually introducing features to users.

The initial focus is on tools like text-to-image, text-to-speech, and other simple features. We will advance to full video creation where a single prompt can generate images, sounds, and captions in one workflow. The main goal is to build APIs that platforms can integrate seamlessly into their systems.

Each AI capability will give users control through editing options, previews, and feedback loops so the technology improves with use. Our key measures of success are ease of partner integration, adoption rates, and how much we reduce the time and effort required for high-quality content creation.

How do you balance the technical demands of product development with the strategic business goals of growth and profitability?
I work with a resource allocation plan that keeps the balance clear. Most of the team’s time goes to roadmap items that will drive growth, some is kept for platform stability and debt reduction, and the rest is used for experiments.

Every initiative is linked to a clear business goal. If we cannot connect a feature to revenue, retention, or efficiency, we question whether it should be built. We also check progress at set points in development, and if the data shows the impact will be too low, we stop or adjust.

What role does user research play in your product management process, especially when working across multiple cultures and regions?
It is central to how I work. I always start with conversations, observations, and prototype testing in each market. I make sure we talk to a mix of users from different backgrounds, not just the most accessible ones.

I combine what people say with data on how they actually use the product. When we make changes based on feedback, I always share that back with the users so they know their input made a difference.

Could you explain your method for prioritising features in a product backlog and how this impacts release speed and market fit?
I start with the outcome we are aiming for, then score features based on their potential reach, the confidence we have in their impact, and the effort required. To prioritise, I rely on the MoSCoW method, which is my go-to approach.

In this framework, Must-have features are critical for the product to function or meet the core user need, while Should-haves add strong value but are not essential for launch. Could-haves are nice-to-have features that can be included if time and resources allow, and Won’t-haves are deliberately excluded from the current release to maintain focus.

I align these categories with our desired outcome, then assess reach, confidence, and effort for each feature. High uncertainty features are tested as small experiments before committing them to the roadmap. Grouping related features helps us release in phases, learn from early usage, and adjust quickly for better market fit without slowing release speed.

What lessons have you learned from introducing digital transformation initiatives in educational institutions that other product leaders could apply?
Timing is everything. Avoid making major changes during critical academic periods. Data migration should be done in phases, with a short period of running both systems to catch problems.

Training should be tailored to each role, and I have found that having ongoing support sessions works better than one-time training. It also helps to have a fallback process in the first term after launch to reduce resistance.

How do you measure the effectiveness of innovations such as AI-driven exam analytics or automated administrative workflows in delivering performance improvements?
I look at both operational metrics and adoption. For exam analytics, I track how much faster grading gets done, how accurate the anomaly detection is, whether there is fairness across different student groups, and how often faculty override the AI.

For automation, I measure the time it takes to complete a process, the error rate, the number of hours saved, and whether service levels are met. I compare before-and-after data and often start with pilot tests before expanding.

In what ways do you see the relationship evolving between SaaS product management and educational outcomes in the next five years?
SaaS product management will focus more on measurable learning outcomes. Features will be designed and prioritised based on their impact on student performance and engagement.

Data will be used to track results in real time, and updates will be made faster to address gaps. Integration with other tools will make it easier for schools to connect teaching, assessment, and reporting in one flow.

What advice would you give to emerging product managers who aim to drive impactful technology solutions in sectors combining education and software-as-a-service?
Spend time with users in their environment. Watch how they teach, learn, and manage administration. Learn how to use data to make decisions. That means understanding analytics, running experiments, and measuring outcomes.

Be mindful of privacy and ethics, especially with AI. Build trust as much as you build features. Start small, deliver something that creates value quickly, and build from there. Keep track of your results so you can show the impact you have made.