1-day immersive training, on-site or online
For cross-functional AI teams (up to 24)
Comes with GPT-powered Copilot
Includes AI Problem Framing Playbook + Facilitation Slides
Leave with fundable AI opportunity briefs
This training is for AI teams who’ve been told to “do something with AI”—but aren’t sure where to start. Without clear problem framing, even top teams ship features no one adopts—and no one can justify.
+anyone building with AI who wants to stop guessing and start solving the right problems.
This isn’t a brainstorming session or a design thinking exercise. It’s a fast, focused way to turn vague AI ideas into fundable opportunities your execs can actually back.
We bring decision-makers and builders into the same room—and give them a practical and repeatable recipe to:
✔️ Link AI ideas to business goals & strategy
✔️ Validate against user needs and data
✔️ Score and shape what’s actually worth building.
By the end of the day, your team will leave with a crisp AI Opportunity Statement—something your leaders want, your users need, and your team can build with confidence.
The tech has changed. The failure patterns haven’t. AI initiatives stall. AI startups fail.
Not because the tech doesn’t work, teams are actually building faster than ever. But speed means nothing if you’re solving the wrong problem beautifully.
Module 1: What, When, and Why AI Problem Framing
Learn how Problem Framing works as a tactical tool to align AI teams and stakeholders before anyone builds. Because building fast means nothing if you’re building the wrong thing.
Module 2: Align With the Business
Connect AI ideas to real business goals — or eliminate them fast. Your team doesn’t need more ideas. It needs strategic clarity.
Module 3: Understand the Customer
Zoom in on the user segments who actually feel the pain. Because the best AI solution solves a specific problem for someone specific.
Module 4: Contextualize the problem
Ground your AI idea in real-world context — so it’s not just viable, but valuable.
Module 5: Craft your AI opportunity
Bring it all together into one crisp, fundable AI Opportunity Statement.
Co-founder | Trainer | Senior Innovation Consultant
John is a seasoned expert in innovation and business transformation, as well as the co-founder of the Design Sprint Academy. With over 20 years of experience as an entrepreneur and digital enthusiast, his goal is to simplify innovation and make teams more effective.
Beyond the training, your team leaves with a toolkit to keep framing the right AI problems long after this training:
AI Problem Framing Playbook — A step-by-step guide your team can use to run future framing sessions on their own
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Workshop Facilitation Slides — Clean, reusable slides to help you align teams and run internal workshops
AI Problem Framing Copilot — A custom GPT trained on the method, to help you refine ideas, guide you through the workshop, and craft clear AI Opportunity Statements—anytime, on demand.
Book a quick call and see how AI Problem Framing can help your team win the AI race.
Who should attend this training?
This 1‑day program is built for cross-functional AI teams—including product, data, engineering, design, CX, sales, and leadership. If your team is expected to build with AI but hasn’t been given clarity on what to build, this is made for you.
Is it about teaching AI tools or coding?
No. This is a problem-framing workshop, not a technical deep dive. We focus on scoping meaningful AI opportunity statements, aligned with business goals and user pain—before code or models are ever built. This is the opportunity to break silos and bring all involved in “building something with AI” to align and collaborate.
How is this different from design thinking or sprint training?
Problem Framing is a strategic alignment intervention, not an ideation or prototyping session. It’s designed to get teams on the same page before they decide what AI solution to move forward with.
Problem Framing is upstream thinking. It asks, “What is the problem we’re actually solving?
”Design Thinking and Sprints are downstream methods — they help you solve a problem after you’ve accepted a particular framing.
When should we use Problem Framing in our AI project lifecycle?
During the planning or discovery phase. This is when you’re defining goals, aligning stakeholders, and deciding if AI is even the right tool for the job.
Use Problem Framing to:
- Clarify the business problem behind the AI ask
- Explore whether AI is the right approach
- Identify early data needs and constraints
- Align your team before ideas harden into roadmaps
Think of it as a strategic checkpoint — the earlier you frame, the fewer wrong turns you’ll make later.
Do we need prior experience or data to participate?
No prior AI experience or data is required. You just need to bring your expertise — whether that’s in UX, product, engineering, business, or AI.
In the training, you’ll work in cross-functional teams, learning directly from others in the room.
If you’re a UX expert, you’ll hear firsthand from AI engineers about risks, limitations, and feasibility.
If you’re an AI expert, you’ll gain clarity on business strategy, goals, and what truly matters to users.
Everyone brings their lens — and leaves with a broader, clearer view of what makes an AI opportunity actually work.
Is the method suitable for repeat use across multiple teams or projects?
Absolutely. This training isn’t just a one-off workshop — it’s designed to be scaled across teams and reused for any AI initiative.
You’ll leave with:
- A step-by-step playbook your teams can run on their own
- Facilitation slides to guide internal workshops
- Access to the AI Opportunity Copilot (custom GPT) to support future framing sessions
Whether you’re launching one AI product or running dozens of pilots across departments, this method helps teams stay aligned, focused, and strategic — every time.
How does the method validate which AI opportunities are worth pursuing?
It doesn’t — and that’s by design.
Problem Framing isn’t a validation tool — it’s an alignment tool.
It helps your team define a clear, strategically sound AI opportunity before you invest time or money validating it.
Once you’ve framed a high-confidence opportunity, validation comes next — typically through rapid experimentation. (We recommend a Design Sprint for that.)
This training ensures you’re not testing half-baked ideas — you’re testing the right ones.
What preparation is required from participants?
None — just come ready to think.
Before the training, we’ll ask each participant to bring an AI opportunity or idea they’d like to explore. This could be a project your team is already committed to, or simply a promising direction you’re curious about.
You’ll get to frame the problem around something real — not a hypothetical case study — and walk away with sharper clarity on whether it’s worth pursuing.
What’s the difference between AI Problem Framing and your standard Problem Framing training?
Great question — they serve different audiences and moments.
Problem Framing (Strategic):
A high-stakes, leadership-level workshop used to align senior decision-makers before major investments. It’s insight-driven, often requires research and data, and is used to gain executive buy-in on strategic direction.
AI Problem Framing (Tactical):
A hands-on, team-level workshop used by cross-functional AI delivery teams. It doesn’t require prior data — just the right mix of experts (product, design, engineering, AI, business) who know the space. The goal is to scope, compare, and prioritize AI opportunities quickly — and choose what’s worth building.
In short: one sets the direction, the other scopes the opportunity.
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