The tech industry has a fetish for shiny objects.
Right now, that object is AI. Every startup pitch deck mentions it. Every product roadmap promises it. Every conference panel dissects it. And yet, for all the noise about artificial intelligence revolutionizing product development, most organizations are building AI features that nobody asked for, solving problems that don’t exist, and wondering why adoption rates are dismal.
Here’s what they’re missing: technology has never been the bottleneck in product innovation. Understanding human behavior is.
I’ve spent nearly two decades building digital products across Nigerian fintech, capital markets, and UK healthcare, markets where the stakes are high, the margins are thin, and users have zero tolerance for products that waste their time. What I’ve learned is that the most transformative products aren’t the ones with the most sophisticated technology. They’re the ones that deeply understand what people actually need and remove every barrier standing between them and that need.
AI doesn’t change that equation. It amplifies it.
The Empathy Deficit in Product Development
Let me tell you what I see when I look at most “AI-powered” products hitting the market: solutions in search of problems.
A chatbot that forces users through ten questions when they just want a phone number. An AI recommendation engine that suggests things nobody wants because it’s optimized for engagement metrics, not actual usefulness. A “smart” interface that’s so clever it confuses the humans who need to use it.
The problem isn’t the AI. The problem is that the teams building these products never stopped to ask the most important question: What is the actual human problem we’re solving, and is AI the right tool to solve it?
I learned this lesson early, not in a boardroom, but on contact center floors managing customer complaints. You hear what people are actually frustrated about. You see where systems fail them. You understand the gap between what executives think customers want and what customers are actually experiencing.
That ground-level empathy, the ability to see your product through the eyes of someone who doesn’t care about your technology stack, who just wants to accomplish a task and move on with their life, is the foundation of good product development. Always has been. Always will be.
AI should enhance that empathy, not replace it.
What AI Actually Does Well (And What It Doesn’t)
Here’s the uncomfortable truth about AI in product innovation: it’s exceptionally good at scale, pattern recognition, and automation. It’s terrible at understanding context, nuance, and what people actually care about.
AI is able to analyze millions of data points and reveal patterns that no human would ever know existed. That’s a powerful thing. I’ve had experience with big data and predictive analytics to understand that if you have the right data infrastructure in place, AI will deliver insights that change the way you interact with customers.
But this is what AI cannot do: it cannot tell you why the customer is agitated. It cannot understand that because a customer fell off in the middle of your product is not because the interface was confusing, it’s because they got a call from their kid’s school and had to handle an emergency. It can’t differentiate between a user that is struggling seriously and a user who is browsing your product out of curiosity.
Context is everything. And context requires empathy.
The best product teams I’ve worked with use AI to handle the things humans are bad at, processing massive datasets, identifying patterns at scale, automating repetitive tasks, so that humans can focus on the things AI is bad at: understanding what people actually need, designing experiences that feel intuitive, making judgment calls when the data is ambiguous.
Designing for Humans, Not Algorithms
One of the most dangerous trends I see in product design is groups coding for their algorithms instead of for their people.
I’ve seen this play out in financial services, where banks build loan approval systems optimized for risk models that are technically sound but produce outcomes that feel arbitrary and unfair to customers. I’ve seen it in healthcare, where digital platforms optimize for operational efficiency but create experiences that leave patients feeling like case numbers instead of humans.
The technology works. The algorithms are accurate. But the products fail because nobody stopped to ask: What does this feel like from the other side?
Enterprise Design Thinking, which I’m certified in, teaches you to start with the user’s needs and work backward to the technology. Not the other way around. It’s a simple principle, but it’s surprising how often organizations ignore it when they’re excited about new tech like AI.
Here is the test that I use: Can you tell me what your product does and why it matters without talking about the tech? If you can’t, then you are building a feature, not solving a problem.
When I led transformation projects, whether it was optimizing customer engagement strategies at scale or redesigning digital health experiences, the question was never “What can the technology do?” It was “What do people need, and what’s getting in their way?”
Once you answer that, the technology choices become obvious.
The Products That Win: Invisible Intelligence
The best AI-powered products are the ones where users don’t even realize AI is involved.
Take the things you’re using day-to-day that seem effortless. That anticipate what you need before you ask for it. That removes friction without making you think about how they’re doing it. That’s not magic, that’s intelligent design backed by smart technology.
I saw this principle at work during a digital transformation project I led that drove over 400% subscription growth during the pandemic. The product wasn’t technically complex in terms of AI, but it succeeded because we obsessed over the user journey.
We identified every point where someone might drop off, get confused, or lose trust. We eliminated those points one by one. The technology served the experience, not the other way around.
Contrast that with products where the AI is front and center, “Look at our machine learning! Look at our algorithm!”, and users are left confused about what they’re supposed to do with it. That’s a product intended for a tech conference demo, not real humans trying to solve real problems.
The magic of excellent product design is making complex things simple. AI should be the engine in the under-the-hood, not the dashboard that you get users to drive.
Where Empathy and AI Actually Intersect
So, where does AI genuinely enhance empathy-driven product innovation? Three places:
1. Personalization at Scale
Empathy in product design used to mean “understand your customer segment.” Now it means “understand each customer individually.” AI makes that possible.
I’ve worked on customer engagement data models that used analytics to identify patterns in behavior, who’s about to churn, who’s ready for an upsell, who needs support before they even ask for it. When done right, this doesn’t feel creepy or invasive. It feels like the product gets you.
But here’s the critical part: the AI identifies the pattern; humans design the response. You don’t automate empathy. You use intelligence to know when empathy is needed and empower humans, or very carefully designed experiences, to deliver it.
2. Reducing Cognitive Load
Good AI removes decisions users shouldn’t have to make.
When I was streamlining customer onboarding, the goal was always to remove unnecessary steps. AI can do that quicker by pre-filling data, offering next actions, or taking users to where they need to go without asking them to wade through complex menus.
But you must be intelligent. The wrong kind of automation is frustrating, like chatbots that trap you in loops when all you want to do is talk to a human being. The right kind of automation is when the product appears to be doing things for you instead of pushing you down a scripted journey.
3. Surfacing Insights That Drive Better Decisions
One of the most powerful uses of AI in product development is helping teams see what they’re missing.
I’ve led discovery initiatives where data analysis revealed gaps in processes that were costing organizations millions, problems that were invisible because systems weren’t designed to surface them. AI can process massive amounts of operational data and flag anomalies, trends, or opportunities that human analysts would never catch.
But again: AI surfaces the insight. Humans decide what to do about it. The combination is powerful. AI alone is just interesting data. Human judgment alone misses patterns. Together, they drive transformation.
What This Means for Product Leaders
If you’re leading product development in 2025, here’s what you need to internalize:
Stop asking “How can we use AI?” Start asking “What problems are our users actually facing, and would AI help solve them better than other approaches?”
Invest in understanding human behaviour before you invest in algorithms. Customer journey mapping, user research, data flow analysis, voice of customer programs, these aren’t nice-to-haves. They’re the foundation. If you don’t understand what people need, your AI will optimize for the wrong things.
Design for outcomes, not features. Folks don’t care that you’re doing machine learning. They care if your product makes them get what they want faster, easier, or better than someone else can. If your AI isn’t moving those needles, it’s decoration.
Remember, empathy does not scale through automation. AI can help you observe patterns and make experiences more intimate, but you cannot replace human judgment, creativity, or the ability to perceive context. The best products use AI to augment human capabilities and not replace them.
Be willing to say no to AI if it’s not the right solution. In certain situations, the best solution is fewer complexity points of infrastructure, better process design, or simply removing unneeded complexity. Don’t add AI features just because they’re cool. Add them because they produce more authentic solutions, better than simpler solutions.
The Next Generation of Product Innovation
The products that will win in the next decade won’t be the ones with the most sophisticated AI. They’ll be the ones that use technology, (AI included) to genuinely understand and serve human needs at scale.
That requires empathy: the ability to see your product through your users’ eyes, to understand their frustrations and goals, to remove barriers instead of adding features.
And it requires intelligence: the ability to process complex data, identify patterns, personalize experiences, and make smart predictions about what people need before they articulate it.
The intersection of empathy and AI isn’t about making technology more human. It’s about making human-centered design more powerful.
I’ve seen transformation happen when organizations get this right, when they stop chasing technology trends and start obsessing over user outcomes. When they use data and analytics to understand behavior, not just measure it. When they design experiences that feel effortless because the complexity is handled invisibly.
That’s the future of product innovation. Not AI for AI’s sake. Not empathy as a buzzword in your mission statement.
Real empathy. Real intelligence. Real products that solve real problems.
Everything else is just noise.
Kehinde Ejukorlem is a digital transformation and product innovation leader with over 15 years of experience delivering technology-enabled solutions across financial services, healthcare, and emerging digital ecosystems. She has led digital innovation initiatives at organisations including AXA Health UK, Crowdyvest, Avon HMO, InvestNaija, and Diamond Bank (now Access Bank), where she has driven the adoption of digital platforms, data-driven decision-making, and customer-centric product design.
She played a key role in delivering the MTN Nigeria Public Offer digital investment platform, a landmark initiative that enabled millions of retail investors to participate in capital markets through a fully digital channel, advancing financial inclusion across Africa.
Currently contributing to digital innovation within the UK technology ecosystem, Kehinde specialises in digital strategy, product development, artificial intelligence, and enterprise design thinking. She also mentors emerging professionals and advises startups on product-led growth and digital transformation, while advocating for increased female representation in technology leadership.
