Scenic Mind · Product, AI, experience, delivery
AI Predictions. Turning a complex AI capability into a clear product.
This work focused on shaping AI Predictions within Test Your Ad into something teams could understand, communicate, and deliver. The value was not only in the capability itself, but in making it usable through better product thinking, clearer structure, and practical AI positioning.
CASE STUDY
From AI concept to product clarity.
CONTEXT
AI Predictions as a new capability within Test Your Ad
AI Predictions was introduced within Test Your Ad as a way to predict emotional response to advertising without relying on traditional survey input. The concept sat at the intersection of behavioural science, data modelling, and product experience. While the underlying capability was technically strong, it required clearer product framing to make it understandable, usable, and credible for both internal teams and customers.
CHALLENGE
Bridging a gap between technical capability and product clarity
The core challenge was not just building the feature, but making it make sense. Internally, teams needed clearer requirements, structured stories, and alignment on what the feature actually delivered. Externally, the proposition needed to be explained in a way that felt grounded, trustworthy, and easy to understand, rather than abstract or overly technical.
APPROACH
Applying product, delivery, and AI thinking together
My role focused on creating clarity across multiple layers. I worked to shape the product narrative, ensuring the feature was positioned around customer value rather than technical complexity. I improved story quality and backlog structure so delivery teams could work with confidence. I also helped translate the AI capability into simple, practical language that could be used consistently across product, marketing, and stakeholder conversations.
OUTCOME
A clearer, more usable and better understood product capability
The result was a stronger bridge between concept and execution. AI Predictions became easier for teams to understand, easier to communicate, and easier to deliver against. It shifted from being seen as a complex AI concept to a more grounded product capability with a clear role, clearer requirements, and a more coherent place within the wider Test Your Ad platform.
DELIVERABLES
What I delivered in this work.
Product framing
Defined and refined how AI Predictions should be described, positioned, and understood within the product and by customers.
AI explanation and positioning
Translated a non human, pattern based AI model into clear, simple language that could be used consistently across teams.
User stories and backlog clarity
Improved the quality, structure, and usability of stories so development teams could work with greater confidence and less ambiguity.
Cross team alignment
Helped align product, delivery, and stakeholder understanding around what the feature is, what it is not, and where it adds value.
CONTRIBUTION
Scenic Mind in practice.
Shaping the AI Predictions product narrative
Bridging technical AI concepts with product language
Reducing ambiguity in how the feature should be understood and used
Improving delivery readiness through better structured stories
Supporting alignment across product, engineering, and stakeholders
Positioning AI as a practical capability, not an abstract concept
PRINCIPLES
How I approached the work.
AI should be explained, not hidden
Trust comes from clarity. The model does not need to feel human, it needs to be understood.
Product value comes first
The focus is always on what the feature enables, not how complex the underlying system is.