AutoScout24 · Strategic AI Design Leadership
Shaping How AI Should Work in Product
At AutoScout24, I owned the Visual AI Framework and the design standards behind it, led UX capability-building around AI-assisted workflows, and contributed senior design direction to early AI-assisted product concepts.
The work mattered because teams needed clearer decision criteria for when AI should be visible, what level of review or control it required, and how UX workflows should adapt as AI changed product development.
- Role
- Principal Product Designer, AI Design Lead
- Owned Directly
- Visual AI Framework, AI guidance for UX, and education paths for AI-assisted workflows
- Shaped
- Early AI-assisted product concepts and product direction
- Aligned
- Senior product, design, and technology leaders on AI-assisted workflow change
Why This Work Existed
AI work was accelerating before shared standards existed
The challenge was less about one feature and more about preventing inconsistent AI patterns from spreading across the product before there was a reusable model for designing them.
- Without shared standards, AI cues, labels, and interaction patterns could drift across products.
- Teams needed clearer rules for when AI should be obvious to users and what level of review or control it required.
- The UX team needed practical education paths for AI-assisted workflows as AI changed product-development and design practice.
Role
What I owned and where I shaped direction
I owned the reusable standards work and the AI design guidance behind it. I also led cross-functional capability work around AI-assisted workflows, aligning the work with senior product, design, and technology leaders. On product concepts, my role was senior design direction: helping shape key flows, principles, and tradeoffs alongside other designers and cross-functional partners.
Owned directly
- The Visual AI Framework
- AI design guidance and education paths for the UX team
- Cross-functional capability work around AI-assisted product-development workflows
- AI prototyping capability across the broader product organisation
Shaped through influence
- Senior design direction on early AI-assisted product concepts
- Alignment with senior product, design, and technology leaders on AI-assisted workflow change
- Key flows and experience principles on early AI product work
- Guidance that shaped decisions beyond direct reporting lines
Visual AI Framework
The most concrete output was a reusable AI communication model
The framework turned broad AI principles into a reusable standard teams could use to make more consistent product decisions.
Visibility scaled with user impact
The core rule was simple: AI should become more visible as its effect on user understanding, decisions, and control increases.
Different AI behaviours needed different signals
The framework distinguished between low-visibility assistance and cases where AI generated, summarised, recommended, personalised in a non-obvious way, or acted on the user's behalf.
Controls increased with stakes
As AI moved closer to decision-shaping or action-taking, the design needed stronger review, consent, editability, override, and exit.
In practice, subtle assistive behaviour could stay quiet, while generated summaries, recommendations, and non-obvious personalisation needed clearer labelling and a stronger review path.
The framework combined a shared AI visibility model with reusable signals such as labels, badges, gradients, and icons, plus guidance on review and override. It gave teams a clearer basis for deciding what needed explicit signalling, what could remain lightweight, and when stronger review or control was necessary.
Leverage
The value was in reusable standards and organisational capability
This work was less about a single shipped feature and more about giving teams a clearer basis for early AI product decisions and AI-assisted ways of working.
Early product direction
Alongside the framework work, I contributed senior design direction to early AI-assisted product concepts. Because the work was still upstream, my contribution focused on flows, principles, and decision criteria before patterns hardened.
Internal capability building
I also led AI guidance for UX, education paths for AI-assisted workflows, and prototyping capability across the broader product organisation. As part of that, I led cross-functional capability work with senior product, design, and technology leaders on how AI-assisted workflows were changing design practice and product development.
The strongest proof here is in the reusable standards, decision-making guidance, and capability-building context rather than post-launch metrics. The product concepts were still early, so this page is intentionally about how the work set direction rather than claiming downstream product outcomes.