What Makes a Product Intelligent

The One Shift

This Week’s Deep Dive: What Makes a Product Intelligent

The rush to infuse products with AI is on , in 2017, only 20% of companies had AI in their offerings, whereas today 83%say AI is a top strategic priority . But simply slapping a machine-learning feature onto a product doesn’t magically make it intelligent. The shift is deeper: it’s about rethinking products from the ground up so they can learn and improve continuously, not just launch-and-leave. In other words, AI is everywhere, but true intelligence in products is still rareand that gap is an opportunity for those who get it right.

So what is an intelligent product? In essence, it’s a product designed to think, learn, adapt, and evolve as it’s used, continuously delivering increasing value, powered by data. Traditional products are mostly static: they do what they were programmed to do and maybe offer basic rule-based personalization. In contrast, an intelligent product is dynamic, it observes user behavior and outcomes, and its “brain” updates its behavior in response. It’s the difference between a standard light switch (flip the switch, light turns on) and a smart lighting system that learns your habits and automatically adjusts to your schedule. Intelligent products gather data from every interaction and turn it into actionable knowledge to improve the user experience over time . Importantly, all this happens behind the scenes; to the user, the product simply feels like it’s getting better and more personal the more they use it.

At the heart of an intelligent product is an architecture anchored on data and an intelligence layer. Data is the foundation, without rich, relevant data (from user behavior, sensors, transactions, etc.), the product has no fuel for learning. On top of that data foundation sits the intelligence layer, which we like to break down into the “ABC” of product intelligence: Artificial intelligence, Business intelligence, and Customer intelligence. These three work together (or in parallel) to drive a smarter experience. Artificial intelligence (AI) provides the predictive and adaptive capabilities (the “learning brain” of the product). Business intelligence (BI) provides analytics and insights, the feedback loops of metrics that tell you what’s happening and why. Customer intelligence (CI) brings in the voice of the customer, understanding user needs, context, and feedback to inform personalization and strategy. When you combine AI + BI + CI atop a strong data foundation, you get a product experience that can continuously refine itself.

In fact, we think of an intelligent product in terms of five key pillars that ensure this continuous evolution:

• Data (the Foundation): High-quality, relevant data is the fuel. It powers learning algorithms and provides the single source of truth for insights. Without data, even the best AI cannot learn or improve.

• Artificial Intelligence (AI): The predictive and prescriptive algorithms essentially the “brain” of the product. AI enables the product to make smart decisions (recommendations, predictions, automations) and to improve its performance the more data it gathers. This includes machine learning models, from simple recommender systems to complex deep learning and generative AI features.

• Business Intelligence (BI): The analytics layer that tracks and analyzes how the product is performing. BI is typically descriptive, think dashboards, KPIs, and funnels that help product teams understand user behavior and business outcomes. It’s the intelligence that the business uses to make decisions, ensuring the product’s evolution aligns with strategic goals.

• Customer Intelligence (CI): Deep understanding of the customer, who they are, what they need, how they use the product, and how they feel. This comes from user research, feedback, and behavior analysis. CI informs personalization and empathetic design; it ensures the product adapts in ways that truly resonate with users (often enabling that magical “it just knows me” feeling).

• Product Experience: The user-facing layer, where all the intelligence comes together in the actual features and interface. It’s the design, the interactions, and the value delivered to the customer. An intelligent product’s experience is typically personalized, fluid, and responsive. It may proactively adjust to the user (thanks to AI), present timely insights (thanks to BI), and feel tailor-made (thanks to CI). This is ultimately where the intelligence manifests to the user.

All five pillars work in concert. The data feeds the AI/BI/CI intelligence layer, and the insights from that layer inform changes in the product experience. Crucially, it’s a continuous loop: as users interact with the product, more data flows in, which the intelligent systems use to further refine the experience. The product is never “done” – it keeps getting smarter. (Interestingly, the core idea isn’t entirely new: back in 2002 researchers defined an “Intelligent Product” as a product with a unique identity that can communicate, remember information about itself, and even make decisions about its own destiny . That early vision – largely ahead of its time – is now finally realizable with modern AI and data infrastructure.)

What’s changing? In a nutshell, we’re moving from static products to adaptive, learning products. Instead of designing one-off features and hoping they work, product teams now must design feedback loops. An old product might give every user the same default experience; an intelligent product molds itself to each user’s context and behavior (often in real time). For example, yesterday’s approach to product design assumed you could map out a predictable user flow start to finish. Today, AI-driven products generate dynamic, personalized paths on the fly – meaning traditional linear design processes don’t always fit when the product can change its own behavior . Product teams need to embrace a new mindset: one focused on outcomes and learning rather than just outputs. As one recent playbook notes, “AI isn’t just changing what we build it’s changing how we build it.” It calls for more agile, experiment-driven development and continuous iteration.

What should product teams consider? First, think about data upfront. Intelligent products are only as smart as the data they can learn from. This means instrumenting your product to capture relevant data (and ensuring data quality and governance) is a priority from day one. Second, consider how and where to leverage AI in the user journey – not as a gimmick, but to genuinely enhance the experience (e.g. more personalization, less friction, smarter defaults). Third, build in analytics (BI) to measure and validate the product’s behavior. If your product is “learning,” you need to constantly check: is it learning the right things? Are the intelligent features driving the outcomes we want (retention, engagement, conversion, etc.)? Finally, keep the human element at the center: use customer intelligence to guide your AI. That means continuously gathering user feedback, observing user interactions, and even allowing user control over AI-driven features when appropriate (for example, letting users correct a recommendation or adjust their preferences). The goal is intelligence by design, where your product’s smarts aren’t just a bolt-on, but woven into its very fabric and purpose.

To summarize this paradigm shift: Data → Intelligence → Product. An intelligent product is an ever-learning system. It treats every user interaction as a chance to get better. Teams building such products actively design for change – anticipating that the product this month will behave slightly smarter than it did last month. This is a fundamentally different approach from the old “launch it and leave it” mentality. It’s more like tending a garden than building a statue. And as we’ll see in the story below, even a small experiment in applying this mindset can transform a product’s success.

Real Use Case & Story

Bringing it to life with a real story of success:

Who: Spotify’s personalization team (and one persistent engineer, Edward Newett)

What happened: In 2014, Spotify engineer Edward Newett had a hunch that the music app could do a lot better at helping users discover new songs. At the time, Spotify’s recommendations were buried on a page that few users visited. Newett decided to try a different approach: he convinced a colleague to join him in a hack-week project to prototype a personalized weekly playlist. The idea was simple but bold – every Monday, deliver each user a mix of songs they’ve never heard before, but which data suggests they’ll love . They cobbled together algorithms that looked at each listener’s favorite tracks and found hidden gems by analyzing what other users with similar tastes were enjoying. This side project, called Discover Weekly, was tested internally and quickly showed signs of delighting listeners. Spotify gave it the green light, and the feature officially launched in mid-2015.

The result? Discover Weekly became a runaway hit. Within a year, over 40 million users were tuning in to their custom weekly playlists . It turned passive Monday mornings into a mini music festival for each user – a fresh, tailor-made mixtape delivered like clockwork. The playlists were so spot-on that they spurred users to spend more time on Spotify, exploring beyond their usual artists. Moreover, this intelligent product feature started building careers for new artistswho suddenly found their songs on hundreds of thousands of playlists via algorithmic serendipity . For Spotify, Discover Weekly wasn’t just a feature, it became a differentiator. It leveraged the company’s massive data (listening history, playlist curations, etc.) and turned it into a personalized experience that competitors struggled to replicate. By 2017, over 30% of all listening on Spotify was driven by recommendations like those in Discover Weekly (up from <20% in 2015) – a clear sign that intelligent curation had changed user behavior.

Takeaway: A small experiment in product “intelligence” can have outsized impact. Spotify’s team didn’t set out to build a flashy AI for its own sake; they identified a user problem (stagnant listening habits and discovery fatigue) and applied data + algorithms to solve it in a delightful way. The lesson: Intelligent products often start with a focused insight – in this case, “make a playlist that learns what I like.” By empowering bottom-up innovation (a couple of engineers in a hack week) and iterating quickly with real user data, Spotify created one of its most beloved features. For product builders, the story underscores the importance of experimentation and listening to the data. An intelligent feature should earn its keepby genuinely improving user value. When it does, users flock to it – and it can even reshape an industry. Discover Weekly changed how people find music, and it reminded everyone that a product that learns will keep users coming back.

Intelligent Product of the Week

Spotlight: Duolingo

Why it’s intelligent:

• Adaptive learning: Duolingo uses an AI system called Birdbrain behind the scenes to adjust each lesson’s difficulty in real time. The app analyzes your performance (what you get right or wrong, how long you take, etc.) and then personalizes upcoming exercises to be just hard enough to challenge you without discouraging. This means no two learners follow the exact same path – the content dynamically adapts to your strengths and weaknesses . It’s like having a personal tutor who knows exactly which flashcards you need to review today.

• Personalized experiences at scale: The intelligence in Duolingo isn’t just in the lessons – it’s woven throughout the experience. Even the reminders and notifications you get are customized based on your behavior. For example, the app’s push notification algorithm chooses wording and timing that it knows have worked on you before (using A/B testing and past data) . If you tend to study at lunchtime, Duo the owl will nudge you at lunchtime with a message tailored to your progress (“Keep that 5-day streak going!”). This customer intelligence – drawing on millions of users’ data – makes the app feel uncannily in tune with each learner.

• Continuous improvement: Duolingo combines decades of educational research with AI to constantly refine its teaching approach. Every answer you give provides data on what you know and where you struggle. The BirdbrainAI is continuously updated with this flood of data, improving its ability to predict what lesson or review you should do next . The result is a virtuous cycle: as more people use Duolingo, its courses get smarter and more effective at teaching. The company even leverages generative AI (with GPT-4 in its premium Duolingo Max feature) to create conversational practice scenarios on the fly. This keeps learners engaged with fresh, contextually relevant content. In short, Duolingo exemplifies how an intelligent product delivers a deeply personalized experience to millions, simultaneously – something that would be impossible without AI and data at its core.

Rode POV: From our product lens, Duolingo is a case study in balancing AI-driven personalization with human-centric design. We admire how the app keeps the experience playful and approachable, you almost don’t notice how much heavy AI lifting is happening in the background. The intelligence is in service of the user’s goal (learning a language) every step of the way. One principle this illustrates is that the best intelligent products feel simple for the user, even though they’re incredibly sophisticated under the hood. Duolingo’s team also shows a keen understanding of when to let AI take over and when to add a human touch. For example, all those sentences and exercises generated by AI are still vetted and fine-tuned by human educators, ensuring the content makes pedagogical sense. If we were to suggest anything, it might be around transparency, giving learners a peek into what Birdbrain knows about their progress (some kind of “your learning strengths and gaps” dashboard) could turn the AI into even more of a motivating coach. Overall, Duolingo demonstrates a core tenet of intelligent product design: use data to empower users, not overwhelm them. The app doesn’t bombard you with stats; it just quietly adapts to keep you motivated. That’s a design ethos we believe all intelligent products should strive for, smart, but humble and user-first.

Pulse Poll

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Signals from the Field

Curation of interesting tools, links, and insights from the product, data, and AI world:

• 🔗 AI is Rewiring Product Development — “AI isn’t just changing what we build, it’s changing how we build it.” This piece argues that intelligent product development demands new operating models (more agile, continuous, and insight-driven) as AI upends traditional linear processes.

• 🔗 The Rise of Intelligent Products — A look at how products are evolving from static to dynamic. “Intelligent products gather and analyze their own data to continuously optimize the product to remain relevant and dynamic.”They use AI to adapt in real time, giving them longer life and more meaningful user experiences . (Great practical example in here about Alexa learning your routines!)

• 🔗 Designing for AI’s Unpredictability — An insightful design article pointing out that yesterday’s UX frameworks don’t fully fit today’s AI-driven experiences. Because an AI-powered product can change its behavior on the fly (e.g. generate different UI or content for each user), designers need new approaches to handle this “predictable unpredictability” .

One Thought to Leave

“An intelligent product is never finished, it keeps learning and improving with every interaction.”

What to Do Next

• What’s your favorite intelligent product or use case? Hit reply and let us know, we might feature it in an upcoming issue.

• Make your voice heard in the poll. Voting helps shape the discussion, and we’ll report back on what the community thinks.

• Enjoyed this issue? Forward it to a friend or colleague building at the intersection of product, data, and AI, let’s grow this community of innovators!