CPO at AI-native company shares how AI enables intent-first processes
Discover how an AI-native CPO uses intent-first product development to improve learning velocity, accelerate discovery, and turn ambiguity into clearer product direction.
Christian Braun
Chief Product Officer at Depix AI
AI Integration: AI is reshaping product leadership by merging human vision with intelligent systems for creative advancements.
Intent-First Model: Depix AI uses AI to prioritize intent over form, improving speed and product development alignment.
Operational Logic: AI changes product dynamics, emphasizing context, reliability, and how users interact with features.
Discovery Process: AI aids rapid testing and discovery, focusing on clearer product directions over mere visual outputs.
Leadership Challenges: AI boosts innovation but increases demand for clarity and disciplined product leadership to ensure trust.
Christian Braun is Chief Product Officer at Depix AI, where he builds AI products that assist product professionals. Christian is shifting Depix's product approach from form-first to intent-first, meaning that AI is being used to translate intent into meaningful directions much earlier in the product lifecycle. This has made the team faster, more aligned across disciplines, and more effective at turning ambiguity into actionable product direction.
We sat down with Christian to ask about this shift and more. Here's what he had to say.
Redefining the role of product leaders
I’m Christian Braun, Chief Product Officer at Depix and CEO of MAGING GmbH. My background is rooted in design, innovation, and product creation — especially in industries where form, function, and brand experience have to come together at the highest level.
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Over time, I moved from shaping products in a traditional way to asking a more strategic question: What happens when we no longer start with form alone, but with intent? That shift brought me into product leadership and, naturally, into AI.
I believe the future of product leadership lies in combining human vision with intelligent systems. AI should not dilute originality; it should strengthen it.
What makes this moment so powerful is that AI is redefining the role of product leaders. We are no longer just managing roadmaps or optimizing features. We are helping shape entirely new ways of thinking, creating, and building. In my current role, I work on products that use AI not just as a technical layer, but as a creative and strategic force — one that can unlock speed, clarity, and entirely new outcomes.
I believe the future of product leadership lies in combining human vision with intelligent systems. AI should not dilute originality; it should strengthen it.
Hybrid delivery by design
I lead the product organization at Depix, where we build AI-native products for the future of product creation. Our mission is to help teams move beyond traditional, linear workflows by using AI to translate intent, context, and design direction into actionable product outcomes.
The products we build sit at the intersection of creativity, product strategy, and advanced AI. They support workflows such as concept generation, visual product exploration, CMF direction, and the early shaping of product ideas before they enter more conventional development processes. Our users include designers, product teams, innovation leaders, and enterprise organizations looking to rethink how products are conceived and developed.
We are a focused and highly agile product organization. This allows us to work with speed, stay close to user needs, and continuously refine both product experience and value proposition. We serve a mix of forward-thinking individuals, small creative teams, and larger organizations that want to integrate AI into real product development environments.
Our delivery model is hybrid by design. We build scalable software products with a SaaS mindset, while collaborating closely with strategic customers to develop workflows, validate adoption, and unlock enterprise value. This combination allows us to scale product innovation while staying deeply connected to real-world application.
Why AI changes the operating logic of products
Before starting an AI journey, every product leader should understand one thing: AI is not just a feature upgrade — it changes the operating logic of the product.
That matters because many teams approach AI by simply adding it to an existing workflow and expecting disproportionate results. In reality, AI often changes how it creates value, how users interact with the product, how it makes decisions, and where trust becomes critical. If you treat it as a layer on top, you may get novelty. If you treat it as a change in system behavior, you can create real product advantage.
AI shifts the center of gravity from static functionality to dynamic judgment. It affects inputs, outputs, feedback loops, quality control, and the role of the user. That means product leaders need to think not only about capabilities, but also about context, reliability, accountability, and adoption.
So the first mindset shift is this: Do not ask, “Where can I add AI?” Ask, “How does AI change the logic of the experience, the workflow, and the value proposition?” That is usually where the real product opportunity begins.
How an intent-first approach revolutionizes product processes
One concrete change I made in the last year was to move our product process from form-first to intent-first.
Before AI, product teams often started with features, interfaces, or visual outputs. We changed this. Today, we begin with intent: the goal, context, constraints, and desired outcome behind the product decision. AI made this shift practical because it translates intent into meaningful directions much earlier in the process.
Before AI, product teams often started with features, interfaces, or visual outputs. Today, we begin with intent: the goal, context, constraints, and desired outcome.
This resulted in a major change in how we build and manage products. We now spend less time debating isolated features and more time shaping the system’s understanding, decision quality, and usefulness in context. This has made us faster, more aligned across disciplines, and more effective at turning ambiguity into actionable product direction. It also decreased the amount of time spent exploring weak or misaligned directions.
In our experience, the impact has been roughly a 5x to 10x improvement in iteration speed during early product exploration. But the most important metric is learning velocity. Instead of spending most of our time producing a small number of options, we spend more time evaluating which direction is worth pursuing.
That changes the economics of product discovery in a meaningful way. AI did not just accelerate our workflow; it changed our operating model.
How focusing on intent changed the trajectory of a product
Here's an example. Early on, with one of our products, we tested a key product question: "Should the product focus mainly on generating visual outputs faster, or on helping users define stronger product direction earlier?" AI significantly improved that discovery process by allowing us to experiment with both models concretely.
We could rapidly test different intent structures, interaction patterns, and output behaviors, and then observe where users gained the most value. What became clear was that users did not just want faster generation. They wanted more clarity at the start of the process — a way to turn vague goals into coherent product directions.
That insight directly shaped the product decision. We chose to build around an intent-first model rather than a purely output-driven one. AI made that possible by letting us prototype and compare product logic much earlier than traditional discovery methods allowed.
Why AI challenges traditional user-intent assumptions
One product assumption AI forced me to let go of is this: that value starts only once the user knows what they want.
In traditional product thinking, we often assume the user arrives with a reasonably clear task, intent, or solution in mind, and the product’s job is to help them execute it better. AI changed that for me.
It became clear that some of the highest-value moments actually happen earlier — when users lack clarity but have direction. They may have a goal, a tension, a brand ambition, or a vague idea of what they want to achieve, but not a defined solution. AI can create value precisely in that ambiguous space by helping users explore, interpret, and shape intent before execution.
AI can create value precisely in that ambiguous space by helping users explore, interpret, and shape intent before execution
That changed how I think about product design. I no longer see the product only as a tool for completing tasks. I increasingly see it as a system that helps users form better intent, not just act on existing intent.
Why AI widens options and humans narrow them
I rely on AI most in areas where speed, synthesis, and breadth matter: discovery, pattern recognition, concept exploration, early experimentation, and the rapid evaluation of possible product directions.
For example, AI is extremely useful for processing signals from users and markets, generating and comparing multiple solution paths, accelerating UX and concept iteration, and exposing tradeoffs earlier than traditional workflows. It is particularly strong when the goal is not to produce one answer, but to widen the field of high-quality options.
What remains firmly human are decisions around vision, prioritization, taste, ethics, and accountability. Roadmap choices, strategic sequencing, brand-defining product decisions, and final technical tradeoffs still require human judgment because they depend on context, consequence, and responsibility in ways AI does not fully own.
What remains firmly human are decisions around vision, prioritization, taste, ethics, and accountability.
So, I do not think of AI as the decision-maker. I think of it as an intelligent system for expanding the decision space. The human role becomes even more important at the point where options must be narrowed, tradeoffs accepted, and a direction owned.
Why AI integration boosts both the upside and the downside
The results of AI have been strong on both the upside and the downside.
On the upside, AI has materially improved speed, breadth, and learning velocity in product work. We can explore more directions earlier, test assumptions faster, and create decision-supporting artifacts with much less friction than before. In discovery and early experimentation, especially, this has compressed iteration cycles and helped teams move from vague ideas to structured product directions much more quickly. Qualitatively, it has also improved alignment because design, product, and engineering can react to something concrete much earlier in the process.
On the downside, AI also introduces new risks. It can create an illusion of certainty because outputs often look convincing before we truly validate them. And plausible but unreliable behavior like this causes trust erosion. AI can also flood teams with options, which makes product judgment more important, not less. In some cases, the challenge is no longer generating possibilities — it is maintaining rigor, taste, prioritization, and strategic focus.
So, the net result has clearly been positive. But I would frame it this way: AI has increased our productive capacity, while also raising the bar for leadership discipline. The teams that benefit most are not the ones using AI the most, but the ones using it with the clearest judgment.
What risks AI-driven products face at launch
The hardest part of launching an AI-driven product is not building intelligence — it is productizing uncertainty.
Early on, it is tempting to focus on model capability and impressive outputs. But in real product environments, users care just as much about consistency, control, predictability, and whether the product fits naturally into their workflows. AI can generate excitement quickly, but excitement is not the same as trust.
AI can generate excitement quickly, but excitement is not the same as trust.
If I had understood that more deeply from the start, I would have invested even earlier in expectation-setting, interaction design, feedback mechanisms, and clearer boundaries around what the system does well versus where human judgment is still needed. I also would have been more careful not to mistake strong demo moments for durable product value.
What could I have avoided? Mainly avoidable disappointment and adoption friction. The biggest risk in AI product launches is often not technical underperformance — it is overpromising experientially. That is a lesson I now take very seriously.
Why the whiteboard is still a product leader's most important tool
Even with all the fancy AI tools, the whiteboard — physical or digital — is still the most important tool for product leaders. Because the most important work in product is still framing. Everything else is downstream of that.
So, I use the whiteboard at the very beginning of the process, where clarity matters more than detail. It is where we frame the problem, map the system, surface assumptions, and align the team before moving into specs, design files, or execution tools.
Once the direction is clear, we translate that thinking into the digital workflow: product requirements, design systems, prototypes, roadmaps, and experiments. The whiteboard is where complexity becomes understandable. The digital stack is where that understanding becomes operational.
Why product leaders must focus on clarity
My advice: Do not treat this moment as a tooling shift — treat it as a product thinking shift.
The leaders who will do best are not those who add the most AI features fastest. They are the ones who rethink how they create value, where human judgment matters most, and which workflow parts they should redesign rather than simply accelerate.
So stay curious, experiment aggressively, but be disciplined. Learn the technology, yes — but spend even more time understanding trust, context, decision quality, and adoption. AI increases leverage, but it also punishes shallow product thinking very quickly.
If I had to summarize it in one line: Be bold about experimentation, but even bolder about clarity.
Be bold about experimentation, but even bolder about clarity.
Follow along
You can follow Christian Braun on LinkedIn. And check out Depix and Product Vision.
More expert interviews to come on The CPO Club!
