Just a few years ago, in the early days of the AI hype, the trend was to sprinkle a little intelligence onto existing systems and processes. It felt innovative — even transformative — simply to add “Artificial Intelligence” as a buzzword in product marketing. Everyone wanted to jump on the AI bandwagon, often treating it as a flashy enhancement rather than a fundamental shift in how products are designed and built.
This mindset led many organisations to retrofit AI onto legacy systems and processes, focusing on automation or simple chatbots without rethinking the core product or user experience. Such thinking framed AI as a bolt-on — a tool to make existing systems and processes feel more modern, rather than an opportunity to rethink how those systems should work altogether.
“We’ve got an ERP system — let’s add a chatbot so employees can access internal data using a ChatGPT-style interface.”
But as AI technology matures, a new approach is emerging — one that places AI at the very heart of the product, fundamentally changing how we design, build, and deliver value.
An AI-first approach is a strategic product decision. It involves building a product from the ground up with AI as the core capability — not a supporting feature. It asks: What if AI wasn't just integrated into the product — what if it was the product?
To help guide this mindset shift, we can learn from previous technology transitions. Back in 2011, following the rise of the iPhone, Luke Wroblewski coined the term “mobile first” in his book of the same name. He argued that websites and applications should be designed for mobile as the primary platform, not adapted from desktop versions. This sparked a revolution in UX design and product thinking.
Today, the same shift is happening with AI. An AI-first approach acknowledges that AI brings fundamentally different capabilities and constraints — from non-deterministic outputs and real-time learning to natural language interaction and personalisation at scale. Designing products with these superpowers at the center allows us to rethink not just the interface, but the entire value proposition.
Understanding Your AI Maturity
So how do you move from AI as a feature to AI as the foundation of your product?
The first step is to understand where you are right now. By evaluating your product’s current level of AI maturity, you can make smarter decisions about where to invest, how to evolve, and what kind of experience you’re really offering. Here’s a simple framework with 3 levels:
Level 1: AI-Enhanced
AI supports specific tasks, but the product experience remains largely unchanged.
Example: A basic chatbot for FAQs, or predictive text in a search field.
At this maturity level, AI is a feature, not a strategy. It adds convenience, but doesn’t alter the product’s core logic or user journey.
Level 2: AI-Integrated
AI becomes central to the user journey and starts shaping experiences.
Example: Intelligent assistants that help onboard users or suggest content based on user behaviour.
At this maturity level, AI contributes to business outcomes, not just UX improvements. It is embedded into workflows and starts to influence how users navigate and engage with the product.
Level 3: AI-First
AI is not part of the product — it is the product.
Example: A real estate app that functions entirely through AI-driven conversations and adapts listings in real time based on user behavior.
At this maturity level, the product is defined by what the AI can do — not just enhanced by it. The AI actively learns, adapts, and anticipates user needs in a way that’s foundational to the value proposition.
Real Estate Example Across Maturity Levels
Let’s bring this model to life through the example of a real estate app.
AI-Enhanced:
Users still apply filters like price and number of bedrooms. AI plays a supportive role — e.g., auto-tagging images (“balcony”, “pool”), predictive text in the search bar, or a basic chatbot answering questions. The overall experience remains static and rule-based.
AI-Integrated:
AI influences user interactions. The app might suggest filters based on previous searches, recommend listings similar to ones viewed, or proactively remind users about saved searches. AI begins to shape the user journey and personalize content based on usage context.
AI-First:
The entire product experience is reimagined around AI. Users type or speak natural language queries like:
“Find me a two-bedroom apartment in Sydney with natural light and a terrace under $800,000.”
The system interprets, retrieves, and refines results automatically. Over time, it learns what features the user tends to favor and offers dynamic, tailored suggestions — even before the user asks. AI drives discovery, curation, and interaction.
Is Your Product Really AI-First?
Reaching AI-first maturity doesn’t happen by accident. It requires intentional design decisions and a willingness to challenge existing processes, constraints, and assumptions. One of the most effective ways to assess your product’s AI maturity is by asking targeted, strategic questions.
These questions help clarify whether AI is simply enhancing the experience — or if it’s truly defining it. Use them as a self-assessment:
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Does AI define the user experience, or merely improve it?
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Would the product still function meaningfully without the AI?
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Does the system adapt and learn from user behaviour over time?
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Would your product still be valuable — or even viable — if a competitor offered the same features without AI?
That last question helps gauge whether AI is truly essential to your competitive advantage, or just a convenience layered on top.
If the answer to any of these is no, your product may still be operating at the AI-Enhanced or AI-Integrated level — and that’s perfectly okay. What matters is being aware of where you are now, and making deliberate choices about how far you want to go.
The Risks of AI-First Product Development
While AI-first thinking opens the door to bold new possibilities, it also introduces unique risks that product leaders must not ignore — just as mobile-first approaches introduced their own technical constraints, from screen size to battery life and connectivity.
One of the most common pitfalls in AI-first product development is focusing on the technology itself rather than on the real pain points users face. Just because AI can do something doesn’t mean it should be the centerpiece. Products that prioritize AI novelty over solving genuine user problems often miss the mark — they may deliver flashy or impressive interactions but fail to alleviate the core frustrations or challenges users actually experience. Without a clear connection to real pain points, AI risks becoming a gimmick rather than a meaningful solution.
There’s also the temptation to overpromise what the AI can do. Many teams market AI features as intelligent, adaptive, or even “human-like,” but deliver experiences that are clunky or unpredictable. This disconnect can severely damage brand reputation and erode user trust.
Another frequent mistake is assuming that the only — or best — way to incorporate AI is through a chatbot interface modeled after popular examples like ChatGPT. Product developers often default to replicating these familiar chat experiences without questioning whether they truly fit the user’s needs or the product’s goals. This “chatbot-first” mindset can limit innovation, forcing AI into a narrow interaction pattern that may not be the most effective or intuitive solution for the problem at hand. Instead, AI should be thoughtfully integrated in ways that naturally enhance the user journey, whether through voice, proactive recommendations, data visualizations, or even non-visual components such as backend processes, automation, or decision-making systems that work behind the scenes to improve the overall product experience.
AI-first products also face a heightened burden when it comes to privacy, ethics, and regulation. Decisions made by AI need to be explainable. User data must be secure. Teams must account for not just what the AI can do — but what it should do.
In short, going AI-first means playing at a higher level of complexity and responsibility. But for those who navigate it well, the payoff is significant: products that feel intuitive, proactive, and indispensable.
Conclusion: Let AI Lead, Not Follow
AI-first is more than a technical decision — it’s a design philosophy and mindset. It requires a deep understanding of what AI does well, and a bold willingness to rethink how products work from the ground up.
Rather than bolting AI onto existing systems and processes, AI-first thinking puts intelligence, adaptability, and learning at the center of the user experience. Just as mobile-first redefined how we build for mobile, AI-first will reshape how we build for intelligence.
The future won’t be defined by the products that use AI, but by those that are built around it.
Disclaimer:
AI was used to help organize, clarify, and refine the language in this article. All ideas, insights, and perspectives expressed are solely those of the author.
About the Author
Ché Kulhan is the founder of Acenia Solutions, an AI-first consultancy based in Europe. With over 20 years of experience in software development across banking, finance, and education, he now helps teams rethink product innovation through AI. Acenia Solutions builds and hosts personalised software products for small to medium-sized businesses—ranging from SaaS platforms to mobile apps—focused on democratising AI and transforming client data into AI-first products. Fun fact: way back in 1994, Ché studied several AI subjects at University—long before it was cool.
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