Building AI-Native Products: From Model Integration to Ethical Product Scaling

AI is quickly changing from a product add-on to the core of new digital experiences. From instant language translation to health advice, AI products are changing industries and what users expect.

But doing this is not just about adding a machine learning model to an app. It needs careful planning of data, model use, and constant checks, all while dealing with ethical problems as AI use grows.

Damilola Ojo, a product manager skilled in AI projects, knows that making an AI product is about planning and control just as much as it is about technology.

She has guided teams from testing models to deploying them for many users and through every stage of AI delivery, from experimentation with Gen AI models to deploying production ready solutions for thousands of active users.

For her, it starts with really understanding the problem and making sure AI is the answer. Teams often rush to use machine learning without thinking if it will really make things better for the user.

This mindset was evident in her work in Kromium Health, where she led the integration of a GenAI-powered chatbot for digital health. Rather than simply adding on an AI model, Damilola worked with engineers, data scientists and clinicians to define the problem, curate the right data sets and ensure that the AI-driven recommendations were medically sound, easy to understand and useful in real-world contexts. This meant that the chatbot not only answered health queries but did so in a way that inspired user trust.

One of her main goals is to make sure model use fits smoothly into the product’s natural workflow. AI results must feel natural, intuitive, timely, and reliable to users, not like unclear, detached suggestions. This means data scientists, engineers, and designers must work together to shape the user experience so AI’s role is clear and valuable. Damilola has learned that when users know why the system makes certain suggestions and they understand AI’s reasoning, they are more likely to accept it and be happy with it. She has championed transparency features that explain why certain recommendations are made, especially health recommendations, to increase user acceptance and satisfaction.

Growing an AI product also means growing the infrastructure that supports it. Model performance can change over time for better or worse because of changes in user behaviour, evolving datasets, or external factors.

To fix this, Damilola stresses the need to build automatic monitoring systems that track accuracy, bias, and performance in real time. These systems can trigger retraining cycles or get human review when problems or anomalies are detected. Without these protections, an AI product could give unreliable or harmful results as it grows. 

Ethics are key to Damilola’s way of thinking. As AI products get bigger, the chance of unexpected problems also gets bigger. She makes sure bias testing, clear processes, and user permission options are part of the development from the start.

For example, in one AI-enabled customer service solution, she added ways for users to report incorrect or harmful AI responses. This not only improved the model over time but also created clear responsibility and built trust between the product and its users.

AI products often falter when they are moved from research to production environments, where messy real-world data and unpredictable user behaviour can challenge even the very best-performing models.

The reality is that test models might do well in the lab but struggle when used live, where data quality changes and unusual situations happen a lot.

Damilola mitigates this risk through staged rollouts, where models are tested in limited releases to a controlled user segment before being fully deployed.

This staged release lowers the risk of system failures and gives helpful info on how the AI works with real user data. At Kromium Health, this approach allowed her team to validate chatbot accuracy and user experience with real patient queries before full-scale release, which significantly reduced the likelihood of failures and increased user satisfaction from day one.

Finally, she knows that AI products are never really done. Continuous learning, regular and incremental improvements, and continued ethical checks are part of the long-term plan. She believes that scaling AI responsibly means finding a balance between caution and innovation, making sure growth does not go faster than the product’s ability to stay fair, correct, and in line with user needs.

The future of AI products will depend not just on breakthrough technology but on how carefully they are brought to market.Product leaders like Damilola, who use a mix of structured planning, careful work, rigorous execution and ethical thinking, will shape the next wave of intelligent products and make sure they grow in ways that create value for both businesses and society.