AI product strategy: from feature to core
How to integrate AI into an existing product or build a new one where AI is the heart, not an "AI assistant" button bolted on top.
The problem we solve.
There are two ways to put AI into a product. One is adding a conversational assistant on the side and putting an "AI" label on the button. The other is rethinking the whole product assuming it can now understand intent, generate content or make decisions that used to be impossible.
The first is marketing. The second is product strategy. We help you do the second: identify which decisions AI changes in your product and redesign the experience accordingly.
What is included
- Diagnosis of AI opportunities within the current product
- AI-native product vision defined for 12-24 months
- Roadmap prioritised by impact on product metrics, not hype
- Technical feasibility analysis with your engineering team
- Organisational change plan: which profiles, processes and tools are needed
Our process for AI product strategy.
Discovery
Interviews with product, engineering and users. Analysis of the current value proposition and how AI can transform it.
Vision
We build the AI-native product vision with concrete scenarios and associated metrics.
Roadmap
A quarterly plan with quick wins, architectural milestones and an operating model to scale.
AI-native vs AI-augmented products
An AI-augmented product is the one you have today with an assistant added. An AI-native product is the one you would design from scratch assuming the LLM already exists. The difference lies in which parts of the product disappear because the model does them better (rigid forms, faceted search, deep navigation) and which new parts appear (free input, on-demand generation, agents acting on your behalf).
Other services within AI-native experiences
Functional AI prototyping
Functional prototypes connected to real LLMs to validate use cases in days, not months.
View service →AI agent design
Designing the agent experience: how they present themselves, escalate to humans and manage uncertainty.
View service →Context engineering
Designing the context that feeds the models: prompts, tools, memory, RAG and usage policies.
View service →Generative UI
Interfaces built on the fly based on user intent and the available context.
View service →Multimodal UX
Experiences that combine text, voice, image and video naturally within the same flow.
View service →