Kehinde Arigbolo on Harnessing Machine Learning for Product Excellence and Customer Delight

How data-driven insights transform user experiences, in brief, beyond efficiency, machine learning bridges the gap between data and delight and transforms insights into experiences that anticipate and exceed customer expectations; predictive product development uses ML-powered predictive models to identify pain points before they occur, making it possible to make proactive design decisions; personalization at scale relies on behavioral pattern analysis to help companies provide tailored experiences that make products intuitive and memorable; and human–machine collaboration leverages routine analysis automation to give product teams time to act strategically, creatively, and innovatively. In today’s modern digital economy, Kehinde Arigbolo is redefining what it means to create products that really resonate with users. Machine learning, to Kehinde, is not only a tool for efficiency but a connection between data and delight, which converts the insights into experiences that both predict and surpass customer expectations.

“Machine learning,” Kehinde explains, “enables us to see trends in user behavior that we would not otherwise notice. It is about knowing what people need before they can even describe what they need and then creating those moments of delight that transforms ordinary conversations to memorable experiences.”

Intuition to Intelligence: A New Product Development Paradigm

The traditional product development has long been based on a combination of intuition, market research, and trial and error. Although these methods have yielded successful products, they are associated with a lot of guesswork and reactive adjustments.

Throughout all product development stages, the strategy of Kehinde allows the teams to be reactive instead of proactive by incorporating machine learning in every phase. Predictive models can identify potential pain points and friction prior to making it to the user complaints or abandonment metrics.

The outcome is products that do not merely do what they are supposed to do, but are intelligent, responsive, and incredibly human in their interactions. This difference is critical in saturated markets where functional parity is now the standard, not a “difference.”

Uncovering Hidden Insights: The Art of Pattern Recognition

The true value of machine learning in product development is often defined in unexpected ways. Kehinde describes one of the most enlightening projects where her team found that users were ignoring a certain feature not because it was too complicated or poorly designed, but because the prompts did not align with the natural workflow patterns of the users.

“Traditional analytics informed us that the feature had low engagement, but couldn’t explain why,” Kehinde recalls. “However, machine learning analysis revealed that users wanted to access the functionality at different stages in their journey, rather than we had expected. After redesigning the trigger points and flow, the engagement grew overnight. That is the kind of insight machine learning can uncover, the subtle behavioral patterns that explain the “whys” behind the “what.”

This example explains an important principle: machine learning does not just quantify outcomes, it sheds light on causation. The knowledge of the underlying behavior of users allows product teams to create solutions that can solve problems that address root causes, rather than symptoms. This is what separates innovative products from those that are just refinements of the existing patterns.

However, the implication is not limited to personal features. When implemented systematically across product experiences, machine learning reveals the interaction of various factors, where friction builds up, and when moments lead to long-lasting positive impressions.

Personalization at Scale: Beyond Segments to Individuals

Beyond interface optimization, Kehinde focuses on the transformative nature of machine learning that allows personalization at scale. The traditional methods of segmentation cluster users into general groups according to demographics or simple behavioral characteristics. While useful, these approaches are bound to assume that different people can be viewed as homogeneous groups, overlooking the subtle preferences that shape personal experiences.

Machine learning changes this equation. By analyzing granular behavioral patterns, it is possible to determine micro-segments and even individual preferences with an impressive degree of precision using ML systems. Recommendation engines recommend content or products in an uncannily relevant manner. Adaptive interfaces change their presentation and functionality according to individual user preferences on how to work.

According to Kehinde, “These interventions make ordinary products a personal experience,” Kehinde adds. “The same application can feel completely different to two users, not because we have built two different products, but the experience is tailored to the unique needs and preferences of each person.”

The scalability of this approach is a quantum leap in comparison to the traditional efforts in personalization, which were often manually configured or resource-heavy in customization. Machine learning automates the process of learning the preferences of individuals and providing personalized experiences, which makes genuine personalization cost-effective even when dealing with products with millions of users.

The Experience Gap: Where Functionality Meets Delight

Kehinde introduces an interesting model of how to consider the quality of products: the gap between usability and experience. Usability is important so that the products work properly and that they can be used without difficulty. However, experience is more than just that: how products are experienced, whether they are memorable, and whether they create genuine delight.

“The best metric is customer delight,” Kehinde states. “Efficiency and functionality are the table stakes, needed, but not enough. Unless they have products that are intuitive and actually enjoyable, people will forget about them, refer others to them, and eventually won’t remain loyal to them.”

Machine learning helps fill this gap in experience in many ways. First, it allows products to be smart and change their behavior depending on what users are attempting to achieve. Also, it drives proactive functionalities, which provide useful recommendations to users before they have to ask. Finally, it allows smooth experiences through learning from past interactions and automating repetitive activities.

Consider how modern apps save the preferences of users, propose the next steps, or even change the interface depending on the usage habits. These little touches add up to experiences that are considered considerate and elegant, products that show that they know their customers and not just serve them.

Empowering Product Teams: The Human Element

While the emphasis is on how machine learning improves the products delivered to end users, Kehinde points out another equally significant benefit; its effect on product teams. Machine learning liberates engineers, designers, and product managers by automating routine analysis and pattern detection so that they can focus on higher-order challenges.

“The numbers are handled by technology, while nuance, empathy, and creativity are handled by humans, Kehinde explains. “This division of labor plays to the strength of both machine intelligence and human understanding.”

Traditional product development often submerges teams in data analysis, data cleaning, statistical tests, report generation, and attempting to extract meaningful patterns from noise. While this is necessary, this work is time and intellect-consuming, which might be allocated to a more strategic activity.

Machine learning automates a large part of this analytical workload and will point out useful patterns and insights without requiring manual investigation. So, product teams can spend less time looking for “what happened?” and more time trying to figure out “what does this mean?” and “what should we do about it?”

“It is a collaboration between machines and people,” Kehinde argues. “Information can tell you what is happening, but it is up to human to decide what this should mean and what to do. This is where true innovation happens, where algorithmic intelligence meets human judgment.”

Adaptive Products: The Evolution of User Experience

Looking toward the future, Kehinde envisions products that evolve alongside their users, continuously learning through behavior, emerging trends, and changing preferences. These adaptive systems will blur the boundaries between product, experience, and service, creating remarkable offerings responsive to individual and collective needs.

“It is not just about creating smarter algorithms,” Kehinde argues. “It is about making products that evolve with people that are ahead of the curve to anticipate needs, and continually surprise and delight in natural and organic ways.”

This vision goes beyond mere personalization but genuine adaptation.

Products that observe the needs of the users and make changes accordingly. Interfaces that identify when one is in need and provide contextual assistance. It has systems that identify new use cases and extend their functionality to accommodate them.

Machine Learning as Strategic Lens
By her contribution, Kehinde demonstrates that machine learning is not merely a technical feature, but a strategic prism for forming meaningful relationships between products and their users. The change in this view has far-reaching consequences for the way organizations develop products.

Instead of perceiving ML as an addition or a tool to be put into practice, the framework by Kehinde puts it in the context of a core strategy of learning about the user and designing an experience. This mindset affects the very beginning of the product conception, as well as the continuous optimization and development.

Hence, organisations that adopt this strategic view of machine learning have benefits that are not limited to the specifics of a product. They develop institutional abilities to understand user behavior, how they react to market signals and iterate accurately. These capabilities compound over time and form organizational learning curves, which are more difficult to replicate by competitors.

The Path Forward: Balancing Data and Design

With machine learning becoming more complex and accessible, the question that product teams are struggling with is not whether to adopt it but how to integrate it thoughtfully into development processes. However, Kehinde’s work provides an example: leverage ML to clarify the needs and behavior of users, but retain human judgment regarding response to such insights.

Thus, the best products will be those that employ the analytical capability of machine learning and maintain the empathy, creativity, and strategic thinking that only humans provide. Technology and humanity collaborate to create experiences both intelligent and human.

Finally, for organisations seeking to take their products from functional to exceptional, this approach by Kehinde Arigbolo provides an interesting roadmap, where data fuels pleasure, and machine learning bridges understanding users and exceeding their expectations.