AI product strategy
Defining where AI creates real value — and where it doesn't.
Founder of DADA
Aïcha ChennaouiBuilding AI products people actually adopt.
Founder of DADA•AI Product Builder•AI Product Strategist
I combine product thinking, artificial intelligence and human-centered design to turn ideas into intelligent products that solve real business problems.
Intelligence, made into products
Trusted by teams building with AI
Founders/Product teams/CTOs & CPOs/Innovation labs/Venture-backed startups/Enterprise/Founders/Product teams/CTOs & CPOs/Innovation labs/Venture-backed startups/Enterprise/
Sectors and logos are illustrative — replaced with real engagements.
About
I build AI products, not demonstrations.
I'm a product builder working at the intersection of product, AI and design. I don't sell prompts. I ship products.
My work starts with the business, not the model. I frame where AI creates real value, design the experience, build the system, and stay through adoption. I can talk to a board in the morning and to engineers in the afternoon — same language, different altitude.
Business
I start from the problem worth solving, not the technology. AI only matters when it serves a clear outcome.
Human
I design for the people who use the product. An unused tool created no value.
Technical
I build the real thing — assistants, retrieval systems, agents, copilots — through to production.
How I work
One standard: it has to be genuinely useful.
What I do
- Understand
- Research
- Design
- Build
- Evaluate
- Measure
- Improve
What I don't do
- Sell hype
- Replace people
- Put a chatbot on everything
- Ship useless POCs
- Make slides for months
AI expertise
Six ways I put AI to work.
Product building
Turning an idea into a usable product, from prototype to production.
Agents & automation
Reducing repetitive work with reliable, well-scoped agents and workflows.
Retrieval & knowledge
Building intelligent knowledge systems: RAG, search, documentation engines.
Copilots
Assistants shaped to your teams, their language and their processes.
Adoption & enablement
Making AI stick — autonomous, confident teams.
The DADA Method
How intelligent products get built.
A framework refined across real products. Six phases, one loop — depth over ceremony.
- 01
Understand
The business, the people, the constraints that are actually real.
- 02
Frame
Where AI creates value — and the sharpest problem to solve first.
- 03
Design
The experience and the system, tested with the people who'll use it.
- 04
Build
The real product — assistant, retrieval, agent or copilot — to production.
- 05
Evaluate
Reliability, safety and usefulness, measured against evidence.
- 06
Scale
Adoption, enablement and continuous improvement over time.
Selected work
Products I've designed and built end-to-end.
SaaS · Funding intelligence
Subsidia
A funding-intelligence platform that aggregates grants, tenders and public calls worldwide — search, eligibility filters and saved alerts. Full-stack, Next.js + Supabase.
Live full-stack MVPCase study on request
AI product · Digital twin
Shadow Worker
An AI that learns your writing style from past messages and drafts replies in your own voice — a behavioural clone. Structured outputs, suggestion-only, privacy by design.
Privacy-first · suggestion-onlyCase study on request
Enterprise AI · Cookieless adtech
AI Adoption OS
A Slack-native AI adoption operating system, deployed on a cookieless adtech company's real workspace. Bolt + n8n + PostgreSQL, built to measure time saved per team.
Deployed in productionCase study on request
Local-first AI · Legal
ABOS — LegalOS
A local-first AI operating system that generates business apps from plugins. First plugin: a private, offline assistant for law firms — RAG over confidential documents, sourced drafting, zero network egress.
Local-first · zero network egressCase study on request
Full-stack · Brand research
Brand-lift platform
Product design and full-stack work on a brand-measurement platform — studies, questionnaires, KPIs and benchmarks — plus its marketing site and CRM. FastAPI + Next.js.
Product + site + CRMCase study on request
AI strategy · Fractional Head of AI
AI strategy & enablement
Fractional Head of AI for a scale-up: process audit, AI adoption strategy, use-case prioritisation, team enablement, and a system to track time saved per person — turning “we should use AI” into a governed, adopted practice.
15–30 person team · in productionCase study on request
A selection of AI products I've shipped through DADA. Detailed case studies on request.
Services
Three ways to work together.
Defined scope, defined outcome. Pick the one that fits where you are.
2 weeksFounders & startups
AI Product Sprint
From idea to a working AI product prototype — framing, design, build, tested with real users.
A tangible product you can show, test and raise on.
Book a sprint1–2 weeksTeams unsure where to start
AI Adoption Audit
I audit your processes, map where AI creates real value, and hand you a prioritised, costed roadmap.
A clear plan — what to build first, what to ignore.
Book an auditMonthlyScale-ups going all-in
Fractional Head of AI
Ongoing strategy, building and team enablement — an AI leader without a full-time hire.
AI shipped and adopted, month after month.
Discuss a mandateWhat clients get
Not promises.Concrete results.
A clear view
Knowing exactly where AI serves your goals — and where it doesn't.
Useful tools
Systems built for your reality, not demonstrations.
Autonomous teams
Durable adoption, carried by understanding and enablement.
Measurable gains
Time, reliability and quality — tracked over time.
AI actually used
Because a tool no one adopts created no value.
Trusted by the teams I work with
What teams take away.
« She turned a vague intuition into an AI product our teams actually use. »
« Finally someone who talks about AI without the jargon — and delivers. »
« A secure internal assistant, adopted in weeks. Zero smoke and mirrors. »
« She understands the board as well as the engineers. Rare. »
« Our team owns AI today. That was the point. »
« From framing to production, no friction. Exactly what we needed. »
« She turned a vague intuition into an AI product our teams actually use. »
« Finally someone who talks about AI without the jargon — and delivers. »
« A secure internal assistant, adopted in weeks. Zero smoke and mirrors. »
« She understands the board as well as the engineers. Rare. »
« Our team owns AI today. That was the point. »
« From framing to production, no friction. Exactly what we needed. »
« From framing to production, no friction. Exactly what we needed. »
« Our team owns AI today. That was the point. »
« She understands the board as well as the engineers. Rare. »
« A secure internal assistant, adopted in weeks. Zero smoke and mirrors. »
« Finally someone who talks about AI without the jargon — and delivers. »
« She turned a vague intuition into an AI product our teams actually use. »
« From framing to production, no friction. Exactly what we needed. »
« Our team owns AI today. That was the point. »
« She understands the board as well as the engineers. Rare. »
« A secure internal assistant, adopted in weeks. Zero smoke and mirrors. »
« Finally someone who talks about AI without the jargon — and delivers. »
« She turned a vague intuition into an AI product our teams actually use. »
Testimonials are illustrative — replaced with real client quotes.
Free guide
Where AI actually creates value.
A 10-point audit to spot the AI use cases worth building — and the ones to skip. Straight to your inbox.
Writing
Notes on building AI products.
Essays on AI product design, agents, human-AI collaboration and product strategy.
The editorial section is coming — first essays in progress.
Designing for trust in AI products
6 min
What makes an agent actually useful
8 min
Where AI creates value — and where it doesn't
5 min
FAQ
What leaders ask.
Available for new engagements
Let's build the AI product your team will actually use.
Tell me about your context, your teams and what AI could really change there.