Futurelab trains product managers, designers and builders to judge where AI belongs in a product, prototype it quickly and evaluate it properly. Teams practise on their own roadmap with opportunity mapping, prototype sprints, evaluation design and AI-assisted research.
AI training for product teams
A practical programme for product managers, designers and builders working with AI-enabled products, workflows and new customer expectations.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at Xiaomi · Adda247 · Love in Store · iSaksham · Playmoolah
Product teams learn a simple test before they build. Tick what is true about your idea and watch the verdict change.
People do it many times a day or week, so a small improvement adds up to real value.
A wrong answer can be caught and corrected, or the cost of an error is low enough to manage with review.
The knowledge, data or examples the model needs are available, usable and allowed to be used.
You can say what good looks like and evaluate it, before and after launch.
The training draws on what we learned shipping our own AI products, from retrieval and research to proposals and listening.
A retrieval product: answers from an organisation’s own documents, with the evaluation work that makes it trustworthy.
A research product: gathers, analyses and summarises market research, now in beta.
A drafting product: client-ready proposals with sections and sources, exported to Word, PDF or PowerPoint.
Teams learn where AI changes product opportunities, how to prototype responsibly and how to tell whether an AI experience is actually useful.
Recognise problems that are suited to AI and the ones that are not, before the roadmap commits to them.
Use AI to accelerate discovery, synthesis, prototyping and communication across the product cycle.
Consider context, evaluation, failure modes and user trust from the first sketch, not after launch.
From feature to system Product judgment for a world where intelligence is part of the interface, and quality depends on context, evaluation and trust.
Each format works on your own roadmap, users and constraints, and can include designers and engineers alongside product managers.
Identify user problems where AI could deliver meaningful leverage.
Create a focused prototype to learn with users quickly.
Define what good looks like before shipping an AI experience.
Use AI to structure and synthesise qualitative and market inputs.
Three steps that take a product team from AI curiosity to a better product decision.
Build a grounded view of AI capabilities, limits and what they mean for products and users.
You get: A shared language for AI product work
Work through real product decisions, prototypes and use cases from your own roadmap.
You get: Ideas tested and a prototype started
Create team-specific workflows and a next-step product opportunity with a way to evaluate it.
You get: A product opportunity with an evaluation plan
We rarely see workshops so rooted in both grassroots understanding and future technologies. This was rare and valuable.
Aditya Tyagi, CTO
The workshop exposed us to the possibilities of AI in professional work setups and in the retail industry. It opened our eyes to practical applications we hadn't considered.
Aditya Goel, Co-Founder
What stood out was empathy. Futurelab's AI literacy work met us where we were, then gently nudged us forward.
Audrey Tan, Co-Founder
It is designed primarily for product managers and can include designers and engineers, so the whole team shares the same language for AI product decisions.
No coding is required unless you choose a more technical format. The focus is product judgment, prototyping and evaluation.
Yes. We tailor exercises to the product decisions already on your roadmap, so the session produces work your team can use.
It is centred on product judgment, user value and reliable AI experiences: which problems suit AI, how to prototype them and how to evaluate them before launch.
A useful test is whether the task is frequent, whether mistakes are recoverable, whether the needed context exists and whether success can be measured. The more of these are true, the stronger the case.
Tell us about your product, your team and the AI decisions on your roadmap. We will suggest a format that fits.