You’ve probably used AI more than 10 times today without realizing it. It has suggested the fastest route to work, music you might like on Spotify, completed your email text on Gmail, or even flagged a suspicious transaction on your Opay app. No press release. No dramatic “Powered by AI” banner. Just quiet, helpful intelligence working in the background.
That’s the sweet spot.
As artificial intelligence becomes more embedded in everyday products and lives, the real competitive advantage is no longer whether you use AI, rather it’s how you use it. The products that will win are not the ones shouting about automation. They are the ones that make life easier while preserving clarity, empathy, and user control.
Intelligence Should Feel Invisible, Not Intimidating
The most successful AI integrations don’t overwhelm users with complexity. They remove friction.
When a music streaming app like Spotify curates a playlist that feels eerily accurate, of course the users don’t think about machine learning models. They think, “Oh wow, this app gets me, I just love my spotify’s playlist.”
When a fintech product like Opay detects fraud in seconds, users feel protected, not processed, “Wow, Opay is top tier with detecting fraud before I sent money to this account”
AI works best when it behaves like a thoughtful assistant, not an overbearing decision-maker.
The moment users feel replaced, judged, or confused by automation, trust begins to erode.
Automation Should Enhance Human Agency
One of the biggest mistakes companies make is designing AI systems that override users instead of empowering them.
Human-centered AI gives recommendations but preserves choice. It automates repetitive tasks but makes sure to leave critical decisions to the user. It predicts behavior but explains its reasoning when it matters.
A good example, smart email platforms suggest responses but don’t send them automatically. Financial apps categorize expenses but allow edits.
These small design decisions signal respect and in turn respect builds loyalty.
Empathy Is a Strategic Differentiator
As more products adopt AI, intelligence alone won’t differentiate brands. Emotional experience will.
Users don’t remember algorithms. They remember how a product made them feel.
Did it reduce anxiety? Did it simplify a stressful process for them? Did it communicate clearly when something went wrong?
Embedding AI without losing the human touch means designing systems that anticipate not just actions, but emotions. Clear microcopy, transparent data usage, graceful error handling, and accessible design all contribute to an experience that feels human even when powered by complex technology.
Trust Is the Real Currency
In an AI-driven ecosystem, trust becomes the defining growth lever.
Users need to understand:
What data is being used
Why certain recommendations appear
How to adjust preferences
When they are interacting with automation
Products that prioritize transparency and give users control will outperform those that treat AI as a black box. When intelligence is done with clarity, it definitely creates Adoption
The Future Belongs to Quiet Intelligence
The next wave of successful products won’t feel robotic. They’ll feel intuitive.
AI is now being embedded in scheduling tools, payment systems, healthcare platforms, education apps, and everyday workflows. But the brands that stand out will be the ones that design with empathy first and algorithms second.
Because ultimately, users don’t want more automation.
They want less effort, less friction, and more confidence.
And the best AI is the kind that helps them achieve that without ever making them feel less human.
Before Chowdeck showed up, ordering food without bleeding money on delivery fees or waiting two hours felt like a fairy tale.
Bike men would quote you 5k to cross two neighbourhoods, Jumia Food, and the others slapped ridiculous charges on every order, and most times your jollof still arrived cold.
Then Chowdeck landed in Lagos and quietly changed the game.
Chowdeck isn’t just another delivery app, it’s a community of riders, restaurants, and everyday hustlers who finally made “quick and cheap” mean something real.
In a city where traffic can swallow three hours of your life without warning, being able to get a full plate of egusi and pounded yam (or shawarma, or indomie deluxe) delivered for as low as ₦600 is revolutionary.
That’s cheaper than the okada you’d take to the buka and back, and you can get free deliveries and a couple other freebies with a members subscription starting at just ₦3,500 a month.
I’m the typical Lagos creative: 14-hour workdays, back-to-back Zoom calls, deadlines breathing down my neck.
The moment hunger hits, my brain wants to shut down. Stepping out isn’t an option. Lagos traffic doesn’t care about your client presentation at 4 pm.
Cooking? I love my kitchen, but not when I’m in the zone. Chowdeck became my lifeline.
I’ve ordered from Chicken Republic in Ikorodu to my house in Federal Low Cost Housing Estate at 7:47 pm and had hot food in my hands by 8:09 pm. Two thousand five hundred naira total, including delivery. Try doing that with any other platform in 2019–2021.
But it’s bigger than my personal convenience. Chowdeck created real jobs for thousands of young riders who now earn daily without needing a “connection” or university degree.
Restaurants that were barely surviving now get orders from customers 30 minutes away, they would never have reached. Small businesses think that a woman making killer asun in Surulere or the guy doing moi-moi and akara in Ajah can now list on Chowdeck and blow up overnight.
In a continent where logistics remains one of the biggest barriers to everything (e-commerce, healthcare, you name it), Chowdeck proved you can build hyper-efficient last-mile delivery that actually works for African wallets and African chaos.
They’re in Morocco, Kenya, Uganda, Ghana, Côte d’Ivoire, and still flying under the radar while the spotlight stays on fintech unicorns.
Chowdeck deserves way more flowers. In the middle of Lagos madness, they made it possible for us to stay locked in, eat well, and keep creating without breaking the bank or wasting half the day. If that’s not proper African innovation, I don’t know what is.
For more than a decade, the story of African technology has been told through startups. Venture funding rounds, new fintech apps, and fast growing companies have shaped how the world views the continent’s digital future. These companies have solved real problems and opened access to services that were once difficult to reach. But focusing only on startups can give a narrow picture of how technology ecosystems actually grow.
The next phase of Africa’s technology development will likely depend on something less visible but far more foundational. It will depend on infrastructure.
Not just internet connectivity or mobile coverage, but the deeper systems that make digital economies work at scale. Payments, identity, cloud platforms, cybersecurity, and data infrastructure are the foundations that allow thousands of companies to build services without starting from scratch. Across much of Africa, these foundations remain fragmented or controlled outside the continent. This has created a structural imbalance that shapes how African technology grows.
The rise of the application economy
The first wave of African technology growth was built on what can be described as the application economy. Founders focused on building products that solved immediate problems for users. Mobile payments, logistics platforms, e-commerce services, and digital financial tools emerged quickly as smartphone adoption increased and internet access improved.
This wave of innovation produced many impressive companies and brought millions of people into the digital economy. Yet most of these startups were able to move quickly because they relied on infrastructure that had already been built somewhere else. Cloud platforms hosted their systems. Global payment networks moved funds behind the scenes. Software development frameworks provided tools that allowed teams to build products faster.
This model allowed African founders to innovate rapidly, but it also meant that much of the value chain remained uneven. African companies built the applications, while the deeper infrastructure that powered those applications often remained outside the continent.
The infrastructure ownership gap
This pattern has created what can be described as an infrastructure ownership gap. African engineers contribute talent and innovation to the global digital economy, yet many of the systems that support that economy remain concentrated within a small number of global platforms.
Cloud computing is largely dominated by a handful of international providers. Payment settlement networks often rely on infrastructure located outside Africa. Many cybersecurity and identity systems used by African organisations are developed and operated abroad.
This situation does not simply reflect technological dependence. It represents a concentration of control within the digital economy. When the core infrastructure that powers businesses is owned elsewhere, decisions about pricing, data governance, and technological direction are often made far from the markets where those systems are used.
For startups, these limitations are not always visible in the early stages of growth. But they often become clear when companies attempt to scale across multiple markets or when operating costs become tied to infrastructure they do not control.
Why infrastructure shapes innovation
Applications solve visible problems, but infrastructure determines what is possible within an ecosystem.
A fintech startup can design a payment app that users find convenient, yet scaling that product across multiple countries becomes difficult if reliable payment rails do not exist across borders. An e-commerce platform can attract customers quickly, but without strong identity verification systems and fraud protection networks, trust in the platform can become fragile. A digital service can gain popularity, but if cloud infrastructure remains expensive or unreliable, the company’s ability to grow becomes constrained.
Infrastructure reduces these barriers by creating shared systems that many companies can build upon. Instead of solving the same foundational problems repeatedly, innovators can focus their energy on building services that deliver value to users.
This is one of the reasons why mature technology ecosystems often revolve around platforms rather than individual applications.
From the application economy to the infrastructure economy
Africa’s technology sector may now be approaching a transition from an application economy to what could be described as an infrastructure economy. In an application economy, companies compete by building services on top of existing platforms. In an infrastructure economy, companies create the platforms themselves and enable others to build on them.
This shift is already beginning to appear in several parts of the continent. Payment infrastructure companies are building systems that allow businesses to move money across multiple African markets. Data center investments are expanding as demand for reliable cloud services grows. Digital identity and verification platforms are emerging to support online commerce, financial services, and government programs.
These companies often receive less attention than consumer facing startups, but their impact is different in scale. Instead of serving individual markets or industries, infrastructure platforms can support thousands of businesses at once.
The platform multiplier effect
Infrastructure platforms create what might be called a platform multiplier effect. When a reliable infrastructure platform exists, it allows many other companies to innovate more quickly.
A strong payment infrastructure platform enables thousands of merchants to accept digital payments. A reliable cloud platform allows thousands of developers to launch applications without purchasing hardware. A trusted identity system makes it easier for businesses to verify customers and reduce fraud across multiple industries.
The impact of infrastructure is therefore not linear. A single successful application may serve millions of users, but a successful infrastructure platform can enable thousands of other companies to build services that reach millions more.
This multiplier effect is often what transforms early stage technology ecosystems into mature digital economies.
What needs to change
For Africa to move toward an infrastructure driven technology ecosystem, several shifts will likely be necessary.
Infrastructure projects require patient capital. Building payment networks, cloud platforms, or identity systems takes time and investment before financial returns become clear. Investors who focus only on rapid growth cycles may overlook these opportunities, even though their long term economic value can be significant.
Infrastructure also requires regional thinking. Many digital systems become most useful when they operate across borders. Payment rails, identity frameworks, and digital security platforms gain strength as they connect larger markets and reduce fragmentation across the continent.
Finally, infrastructure development requires deep technical expertise and operational reliability. Platforms that support thousands of companies must be secure, stable, and scalable. Fortunately, Africa already has a growing pool of engineers who have gained experience working on global systems and large scale platforms.
A different kind of technology leadership
Africa’s technology ecosystem has already proven that it can produce creative founders and ambitious startups. That entrepreneurial energy will remain essential to the continent’s digital future.
But the next generation of technology leaders may focus on building something slightly different. Instead of only launching consumer apps, some will focus on building the infrastructure that those apps depend on.
Payment rails that move money across the continent. Identity systems that allow citizens to access services securely. Cloud platforms that host businesses locally. Cybersecurity systems that protect growing digital economies. These platforms may not always attract the same headlines as fast growing startups, but they form the foundations on which entire ecosystems can grow.
The next phase of African technology
Africa’s technology sector has already shown that it can produce innovative companies and talented engineers. That was the first phase of growth.
The next phase may be defined by the development of infrastructure platforms that allow those innovators to scale more effectively across markets.
When those foundations become stronger, startups will find it easier to grow, digital services will become more reliable, and the continent’s technology ecosystem will gain greater independence and resilience.
The next African tech boom may not begin with a new app. It may begin with the systems that make thousands, or maybe even millions, of new apps possible.
An average child born in London, can scroll through platforms and instantly stories that resonate or books he wants to read.
He reads about autumn leaves piling up on the pavements or children building snowmen in winter, and it feels natural because those details reflect his environment.
The Lagos child, on the other hand, opens the same platforms and is bombarded by the same kinds of books. But, for him, the context might be difficult to understand.
Not because they are difficult words, but because they do not reflect his reality. His imaginations are limited to the environment around him, the dry chill of harmattan mornings, the excitement of suwe and ten-ten in the compound, or the ritual of Saturday morning Akara and Pap.
When the books accessible rarely reflect the realities of people, it doesn’t just create distance, it makes it harder to see their own stories as worthy of being told.
While the London child grows up affirmed by the stories around him, the Lagos child is left trying to measure his reality against tales that were never written with him in mind.
The challenge isn’t about having no books at all, it’s about visibility and access to their own stories.
Digitalisation promised to democratise storytelling. But when African books are hidden or hard to find, it blocks children from hearing their own voices. Instead, they grow up consuming their identity secondhand, through stories told by outsiders.
This lack of access makes them more vulnerable to stereotypes and incomplete narratives. As Chimamanda Adichie puts it in The Danger of a Single Story: “The single story creates stereotypes, and the problem with stereotypes is not that they are untrue, but that they are incomplete. They make one story become the only story.”
And this isn’t happening in isolation. The global book market is dominated by Western platforms that decide which stories rise to the top.
These platforms shape distribution, pricing, and discoverability.
For African readers, paying in dollars, navigating limited payment options, and fighting visibility algorithms makes access even harder. What starts as a visibility challenge quickly becomes both an economic and cultural one.
That’s why Chapter One is different.
Their pricing model is designed around local realities, so affordability doesn’t mean exclusion. At the same time, they’re amplifying African authors in ways that make their books as easy to discover as their global peers.
Fair value for the writer, fair access for the reader. Because the goal isn’t just “more books.”
It is ensuring the African child grows up with the same right to dream, imagine, and discover through stories rooted in their own world ; not just borrowed from someone else’s.
A few months ago, I was in a call with a company that had just been hit by a ransomware incident. They were not small. They were not careless. They had a security team, modern tools, and a Managed Detection and Response service watching their environment around the clock.
Managed Detection and Response, or MDR, is meant to do exactly what it sounds like. A specialist provider monitors your systems for suspicious activity and helps investigate and respond to threats. In many cases, they can isolate an infected machine, stop malicious processes, and then inform the organisation about what they have done and what needs to happen next. It is one of the most sensible ways for organisations that cannot staff a full security operations centre to get serious protection.
And yet, in this case, when the attack started, nothing important happened for almost two hours.
No critical system was isolated. No high-risk accounts were shut down. People were still debating whether the activity was “really an incident” or just a false alarm. By the time action was taken, the attackers had already moved through the environment and encrypted far more than they should have been able to.
After the call, someone said something I hear a lot. “We need to improve our security maturity.”
That was not the real problem. The problem was that, in spite of all the tooling and services in place, the organisation was not actually defensible.
Most organisations think about security in terms of what they have bought and what framework they follow. They talk about maturity levels, roadmaps, and coverage. But none of that answers a simpler question.
If something goes wrong tonight, can you stop it from turning into a business crisis?
Very often, the honest answer is no.
This is why I think we need a different way to think about security. Not in terms of how advanced it looks, but in terms of whether it clears a basic survival threshold. I call this threshold the security poverty line.
It is not a moral judgement. It is a practical one. In economics, being below the poverty line does not mean you have nothing. It means you do not have enough of the right things to absorb shocks. One unexpected expense can push you into crisis. In security, one ordinary attack can do the same.
Above the line, incidents still happen. Systems still get compromised. But the organisation has a chance to contain the damage and keep operating. Below the line, every serious incident becomes a gamble.
This problem shows up everywhere, but it is especially visible in Africa and other emerging markets.
Many organisations there are growing fast, adopting cloud services quickly, and digitising core processes under real economic pressure. They do not have the luxury of large in-house security teams. So they do what makes sense. They buy tools. They buy MDR. They buy insurance. They try to borrow “global best practice” and make it work locally.
And still, they get hit. Sometimes badly.
The easy explanation is to say the attackers are getting better. The more honest explanation is that many organisations are still structurally fragile. They are operating below a minimum level of security that makes real defence possible.
In the current geopolitical climate, this fragility is becoming harder to ignore. Modern conflicts are no longer fought only on physical battlefields. They increasingly extend into digital infrastructure. The ongoing tensions and conflicts in the Middle East have been accompanied by waves of cyber activity targeting energy companies, financial systems, government networks, and logistics platforms.
These incidents rarely begin with sophisticated zero-day exploits. More often they begin with the same weaknesses organisations struggle with everywhere else: exposed identities, poorly monitored systems, and slow decision-making when an incident begins to unfold.
In other words, geopolitical cyber operations frequently succeed not because the attackers are extraordinary, but because the target organisations are operating below their own security poverty line.
When nation-state aligned groups begin probing infrastructure, the difference between a minor incident and a systemic disruption often comes down to the same basic question: how quickly can the organisation detect what is happening, make decisions, and contain the damage before it spreads.
What does “defensible” actually mean?
It does not mean being hard to break into. That is a comforting idea, but it is not realistic. It means something more practical. It means you can spot trouble early enough, that someone can make a decision quickly, that someone can act without waiting for a committee, and that the business can find its way back to a stable state without weeks of chaos.
When any of those are missing, security turns into a form of organised hope. This is where the conversation about MDR often becomes confused.
Most modern MDR services can and do take action. They will isolate a compromised device. They will kill malicious processes. They will raise the alarm and explain what they have done. But in most environments, response is still a shared responsibility. The provider can contain part of the problem, but the customer still needs to reset accounts, check for lateral movement, decide whether systems should be taken offline, and deal with the business consequences.
This is exactly where many incidents go wrong.
If identity systems are weak, attackers can simply come back using another account. If nobody is quite sure who is allowed to shut down a critical server, action gets delayed. If logs are scattered and incomplete, nobody can see the full picture. If recovery has not been tested, restoring systems takes far longer than expected.
In other words, you can have good detection and even some level of response, and still be below the security poverty line.
I see this pattern in the UK and Europe. I see it even more often in African and other emerging markets, where environments are usually more mixed, budgets are tighter, and systems have grown in less tidy ways. Old servers sit next to new cloud services. Shared admin accounts exist because “that’s how it’s always been.” Internet links are less stable. Support contracts are more complicated. All of this makes fast, clean response harder.
And attackers do not need perfection. They just need enough friction in your response to buy themselves time.
The uncomfortable truth is that many organisations are not failing because they lack tools. They are failing because they lack coherent control over their own environments.
They cannot reliably say what they own. They cannot reliably say who can do what. And they cannot reliably act fast when something starts to burn.
This is what it means to be below the security poverty line.
It also explains why so much security spending feels disappointing. Money goes into things that sit above the line, while the foundations remain weak. More alerts. More dashboards. More reports. But when something serious happens, the same delays and confusions appear.
The shift in thinking is simple, but not easy.
Instead of asking, “How mature are we?” organisations should ask, “Can we actually survive the most likely incidents?”
For many, the fastest improvement will not come from buying something new. It will come from fixing identity properly. From reducing the number of powerful accounts. From making it clear who can isolate systems and disable access. From practicing recovery in conditions that feel uncomfortable. From deciding in advance who is in charge when things happen at the worst possible time.
These are not glamorous projects. But they move you from below the line to above it.
And once you are above the line, services like MDR start to deliver far more value. Detection becomes meaningful because response can follow. Containment sticks because attackers cannot simply walk back in. Recovery becomes a business process, not a crisis experiment.
The final, slightly uncomfortable conclusion is this.
A lot of organisations are not being defeated by brilliant attackers. They are being defeated by their own fragility.
The idea of a security poverty line gives leaders a more honest way to look at their situation. Not through the lens of how modern their stack looks, but through the lens of whether they can take a hit and stay standing.
In fast-growing markets, in emerging economies, and in fact everywhere, that question is starting to matter more than any maturity score.
Because in the end, security that only looks good on paper is not really security at all.
I still remember the first time someone mentioned Chamsmobile to me. It was during a casual conversation about digital payments and identity systems in Nigeria, and a friend said, “Have you checked what Chamsmobile is doing?
They are not loud, but they’re doing real work.” Out of curiosity, I went online to see for myself, and honestly, that small search opened my eyes to a brand that deserves far more attention than it gets.
What I love about Chamsmobile is that they are building solutions for realities we face every day in this country, not fantasies.
Many tech companies chase hype; Chamsmobile builds quietly for people who need technology the most: market women, transport workers, SMEs, civil servants, and communities usually left out of the main conversation.
One thing that really stood out to me is their focus on digital identity and financial inclusion. In a country where a simple thing like verifying someone’s identity can delay jobs, stop access to credit, or complicate business transactions,
Chamsmobile found a way to simplify it with their Kegow platform. It’s not just about transferring money. It’s about helping people prove who they are, opening accounts easily, and accessing financial services without stress.
I also respect how they operate. They don’t try to be everywhere at once; instead, they focus on solving real administrative and financial gaps in Nigeria.
For example, the way they’ve built partnerships with government agencies and private organizations shows that they understand the Nigerian system deeply. They’ve also created room for agents and small business owners to earn through their digital services, and for me, that’s one of the most practical ways a tech brand can empower ordinary people.
Another thing I admire is the trust they’ve built over the years. In a space where many fintechs rise fast and crash even faster, Chamsmobile is consistent.
They don’t make noise, but they deliver. They’ve been part of Nigeria’s digital identity journey long before it became trendy, with the help of their parents’ company, Chams Hold Co, and they’ve stayed committed to it through every challenge.
Chamsmobile makes me proud because they represent what African tech should be: innovation rooted in real problems, solutions created with Nigerians in mind, and a quiet confidence that speaks louder than hype. They may not always trend on social media, but they are impacting lives in a way that truly matters.
For me, that’s the kind of tech brand worth celebrating.
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.
In the high-velocity world of African startups, the “Human” in Human Resources is often the first thing to get lost in the sprint for scale. Between the high-burn culture of Lagos tech and the cold precision of multinational conglomerates, a new philosophy is emerging.
I sat down with Emmanuel Faith, a name synonymous with the “relatable” side of tech leadership. From the hallowed halls of General Electric (GE) to the high-stakes trenches of Cowrywise and Big Cabal Media (BCM), Faith has navigated the “storms” of the ecosystem with a unique blend of financial literacy and radical empathy.
The Origin: From Taxaide to the “Big Boy” Era at GE
Before he was an HR powerhouse, Emmanuel Faith was a Tax Intern at Taxaide Professional Services, starting just a day after graduation. While many wait for their “dream role” to find them, Faith used his time as a Tax Trainee to study the intersection of payroll automation and people operations.
When he eventually landed at General Electric (GE), he entered a world of “packaging” and prestige—jumping danfo buses from Iyana-Ipaja to VI by 4 AM, only to stroll into AXA Mansard later that morning looking like a top-tier consultant.
“There are two kinds of people: those who know GE and those who don’t,” Faith laughs. “I was a ‘big boy’ earning in the upper echelon, but the real growth happened in the ‘bubble assignments’.”
The “Bubble” Strategy: How Personal Branding Opened Doors
Faith didn’t start in the HR department at GE, but he made his intentions impossible to ignore. He pioneered the “Bubble Assignment”—proactively asking HR managers for tasks that wouldn’t breach confidentiality.
His breakthrough came when he branded a company-wide engagement “Chat and Palley.” By leveraging his writing skills and his charisma as an “emergency Emcee,” he recorded a 96% attendance rate—the highest the team had seen in years.
“The reward for good work is more work,”
Cowrywise & The “Multipotentialite” HR
If GE was his training ground for culture, Cowrywise was where his ability was truly tested. In the fintech world, HR isn’t just about payroll; it’s about product. Faith describes this as his “best work,” largely because he wasn’t confined to a silo.
“Cowrywise tested my ability. I wore a lot of shoes—shoes I was quite capable of wearing,”
Whether he was partnering with Dev teams to build a product or supporting the marketing engine, his Economics background became his “unfair advantage.”
The work ethic was relentless. At one point, the company expanded by nearly 90% within 250 days of his joining. Faith himself was in the office for 100 days straight—a testament to the “grit” phase many tech enthusiasts romanticise, but few survive.
Navigating the Storm: Why the Documentary?
Every leader faces a season of restructuring. At Big Cabal Media (BCM), Faith joined during a “storm” where several leaders had resigned. He was tasked with “studying the ship” during a period of significant organisational change.
When BCM eventually underwent a downsizing exercise, Faith found himself among those affected. While a layoff is often seen as a “dip,” Faith used it as a moment of deep reflection. It was during this period of silence—navigating the transition from a high-profile role to the “what’s next”—that the idea for his career documentary was born.
The documentary isn’t just a highlight reel; it’s an honest, behind-the-scenes look at the pressures, trade-offs, and emotional weight of HR leadership. Faith shared the idea with his editor during this reflective period as a way to “open source” the story behind the scenes. It serves as a playbook for resilience, showing that even when a role ends, your reputation and your “Why” remain intact.
The Philosophy: “Good work gets done in a great ambience”
One of the standout phrases Faith insists on is Ambience. In his view, charisma is a tool of leadership, but empathy is the foundation.
“People come to work for different reasons,” he explains. “Sometimes it’s the person with the ‘gist,’ sometimes it’s the person who dresses well. As HR, you must recognize these diverse motivations.”
By making himself “easy-going,” Faith created a safe harbour for talent. However, he warns that this friendliness must be balanced: “It’s good for people to recognise you as an easy-going person so that when you are being serious, they recognise that too.”
Redefining the Gap: The HR Clinic Portfolios
Emmanuel identified a glaring hole in the African ecosystem: Quality HR service provision. Through HR Clinic, Faith has provided HR advisory for startups, SMEs, and NGOs—including working with high-growth clients like Nestuge. He is democratizing HR knowledge, providing a framework for startups to treat their people as assets rather than expenses.
The Takeaway: Building Your Career Capital
For the 18–35-year-old tech cohort, Faith’s story (and his new documentary) offers a playbook on Resilience:
Don’t be shy about what you want: Ask rationally, but ask.
Bet on yourself first: Faith missed movies and social time during NYSC to work on his “Bubble assignments.”
Show your work: You need people speaking for you in rooms you haven’t entered yet.
Emmanuel Faith’s career proves that in the “New Africa,” the most valuable technology is the way we manage the people building Africa’s Tech.
Reevar was born from a simple but powerful question:
What if expertise didn’t depend on physical presence?
In a world increasingly driven by artificial intelligence and digital connectivity, knowledge still faces an old constraint, time. The most brilliant professionals can only be available for so many meetings, consultations, classrooms, or advisory sessions. Their thinking has depth.
Their insights have value. But their availability limits their impact.
Behind Reevar is a forward-thinking team of technologists, strategists, and product builders who saw this limitation not as inevitable, but as solvable.
The Gap They Chose to Solve
Before building Reevar, the team observed a critical paradox in the digital economy:
Professionals were building massive audiences. They were creating content libraries, hosting webinars and publishing thought leadership. Yet their true value, how they think, remains locked behind one-to-one access.
Content scales visibility. But thinking does not scale easily.
The team realized that what makes an expert valuable is not just information, it’s reasoning patterns, frameworks, decision-making logic, and contextual judgment. And none of that had been truly operationalized. So they built Reevar.
Turning Expertise into Deployable Intelligence
Reevar enables professionals to create AI-powered Digital Minds, structured, interactive representations of how they think, reason, and communicate within their domain.
These are not generic AI assistants. They are domain-specific, personality-aligned, reasoning-informed digital extensions of professional expertise.
Digital Minds on Reevar can:
Be discovered by individuals and organizations
Engage users in real-time conversations
Be embedded into websites, dashboards, and platforms
Function as scalable advisory or knowledge systems
In effect, Reevar transforms expertise into an employable, deployable, and scalable digital asset.
This introduces a new category of professional presence, one that is persistent, interactive, and not limited by geography or schedule.
The Team’s Philosophy
At its core, Reevar is built on a clear belief:
AI should not replace human intelligence. It should amplify it.
The team approached the platform not from a place of automation, but from augmentation. Their goal was not to create another tool for content generation, but to design infrastructure that multiplies professional impact.
With strong foundations in:
Artificial intelligence
Product architecture
Digital systems design
Human-centered experience
Strategic thinking
The team engineered Reevar to sit at the intersection of technology and human cognition.
Every feature reflects that philosophy, preserving authenticity while enabling scalability.
More Than a Product: A New Professional Layer
Reevar is not positioning itself as just another AI startup. It is building what could become a new layer of the internet, where expertise is interactive, deployable, and accessible in real time.
If social media scaled voice, and cloud computing scaled software, Reevar aims to scale human reasoning. The team understands that the future of work will be defined not just by automation but by cognitive productivity. By how effectively expertise can be accessed, distributed, and embedded into digital systems.
Reevar represents the beginning of that shift. And the team behind it is not simply launching a product. They are building the infrastructure for scalable human intelligence.
A Conversation with Oluwasanya Taiwo Ruth on Building Products That Resonate with Users
A form of innovation driven by empathy rather than dashboard metrics alone is a distinctive and remarkable perspective that she had brought to the field of product marketing. Her methodology combines technical discipline with genuine human concern, this is shaped by her journey from Nigeria to the UK and across FMCG, streaming services, and financial services.
I sat down with Oluwasanya Taiwo Ruth to understand how she balances security requirements with emotional engagement, captures user sentiment in real-time, and why she believes the most impactful products fuse engineering prowess with emotional involvement.
What are some of the product design lessons you have learned in designing consumer goods and streaming as well as financial services that you have applied to fintech to make sure offerings truly resonate with users?
One of the biggest lessons I’ve taken from consumer goods and streaming is that people gravitate toward products that reduce friction and make decisions feel effortless. In consumer goods, simplicity drives adoption; in streaming, it’s about instant clarity, users expect to understand what they can do within seconds. I bring that same mindset to fintech by making sure the core value of a feature is immediately visible, and that the path to taking action is as short as possible.
Another key lesson is that emotional trust is just as important as functional value. With streaming, users trust that when they hit play, the content will be there. With financial services, that trust takes on added weight because it involves people’s money. So I’ve learned to design for reassurance: clear language, predictable behaviors, and contextual signals that reinforce security and transparency. This has proven essential in helping fintech users feel confident enough to try new tools or shift financial behaviors.
From consumer goods, I also learned the importance of leaning into real-life use cases rather than abstract features. People don’t buy products; they buy outcomes. In fintech, that means illustrating how a feature helps someone save time, avoid fees, build habits, or reduce anxiety. Translating benefits into everyday scenarios ensures the experience feels practical, not technical.
How can you balance incompatible technical requirements of security, scalability, and compliance against emotional engagement that drives loyalty?
Since day one, our security, UX, and development teams adopted security-by-design, incorporating interconnected end-to-end encryption and robust data protection into every release pipeline, and UX design workstreams created micro-moments to provide moments of reassurance. You know, like that subtle success animation after a bill payment or budget milestone. Are you aware that a company that is a leader in customer experience can achieve more revenue growth than its peers, and I have seen that when users feel safe and appreciated, they can experiment with more advanced features instead of leaving the app.
Real-time feedback loops have been said to be essential. Can you tell me about your processes of capturing and utilizing user sentiment throughout the early product life cycles?
We’re working on a rollout where users are asked to submit a one-tap satisfaction rating with an optional text field after initial usage or after key interactions. Those inputs would feed a live dashboard tracked by product and engineering leads, allowing us to identify friction points quickly. In a previous project, this approach helped us spot a confusing button label that was driving a spike in errors within two days. We corrected it and watched error rates revert to baseline, an agility that reassured users we were listening and acting.
Digital socialization can strengthen the process of community building. What have offline brand communities taught you that applies to fintech platforms?
In many cases FMCG brands have ambassadors to sell products in the markets. What this means is that we can do this by providing a virtual product council of power users who would have early access to new features and video workshops each month. A community-driven development can also increase the adoption rates of our products, and our council members would become our most credible spokespeople, telling genuine experiences that attracted more referrals than any paid cycle.
So as a final question, I want to go back to your frontline experience and ask, what is the guiding principle that you could provide to fintech teams developing products to fit in our digital world?
The guiding principle is simple: pair strong technical discipline with genuine concern for real users. Fintech products must be designed with the practical financial challenges of target users in mind. When performance is seamless and communication feels human, users quickly become loyal advocates.
This was clear during Spendify’s virtual unveiling across Nigeria, Ghana, Kenya, South Africa, and the diaspora, where we showed how the platform helps people move from manual records to organised, digital financial management. In that balance of engineering clarity and human insight lies the core of products that truly make a difference.
About Oluwasanya Taiwo Ruth
Oluwasanya Taiwo Ruth is a product marketing strategist and author with over a decade of experience across FMCG, streaming, and fintech. Currently at Spendify, she’s a CIM member and graduate of the Forward programme. She’s the author of The Ad Man Guide and holds an MSc in Advertising and Marketing from The University of Hull, UK.
