AI Design Sprint

From AI idea to a working prototype – in just two weeks.

You know that Artificial Intelligence can move your company forward. But between “AI has potential” and “We have a concrete use case,” there is often a frustrating gap. There are plenty of ideas, but which one is technically feasible, economically viable, and ethically responsible? Without a structured process, AI initiatives get stuck in endless evaluation loops. Resources flow into concept papers instead of real results. And in the end, the question remains: Where do we actually start?

Duration
14 - 16 days
Format
Sprint
Target audience
CDO, CEO, Head of IT

Solution Approach

In two weeks, you will not only have identified the right AI use case, but also have a working prototype in your hands. The AI Design Sprint based on the 33A model is a structured, collaborative process that combines strategic thinking, technical validation, ethical reflection, and rapid prototyping in a compact format. Using proven tools such as AI Cards, canvas templates, and a clearly structured process, we take your team from the initial idea to a tangible result. Practical, responsible, and without unnecessary detours.

Benefits

  • Speed instead of stagnation through a clearly structured process that takes you from potential to prototype in days rather than months
  • Confidence in decision-making through validated use cases that have been assessed strategically, technically, and economically
  • Tangible results through a functional proof of concept that builds internal confidence and creates the basis for scaling
  • Responsible AI adoption through integrated ethics checks and transparent assessment criteria

Approach

  1. Phase 1: AI Opportunity Mapping (1 day): Together, we identify the relevant AI opportunities in your company using AI Cards, your business goals, and value streams. The result: a clear map of opportunities.
  2. Phase 2: Concept Development (1 day): The most promising ideas are developed into concrete use cases using the AI Use Case Canvas, an Impact/Maturity Matrix, and an integrated ethics check.
  3. Phase 3: Tech Check & Assessment (2 days): We assess data availability, technical feasibility, suitable model approaches, and economic viability. The result is a clear implementation path for the prioritized use case.
  4. Phase 4: Rapid Prototyping (7 days): Your prioritized use case becomes real as a clickable mockup or technical proof of concept. The focus is on user experience, data integration, and logical model structure. Optionally with Generative AI, NLP, or automation components.

Deliverables

  • Opportunity Map with a documented overview of all relevant AI opportunities within your company
  • Developed Use Case Concepts including canvas, Impact/Maturity Matrix, and ethics assessment
  • Technical Assessment with a clear evaluation of data availability, feasibility, and economic viability
  • Functional Proof of Concept or clickable mockup of the prioritized use case
  • Recommendations and Implementation Path for the next steps after the sprint
  • Shared Understanding within the team of the opportunities, limitations, and responsibilities involved in using AI

Speak with our experts!

Let’s work together to identify where the greatest potential lies and which measures will deliver the greatest added value for you.

Tim Schüning
Director Digital Business Consulting
Contact us!
Frequently Asked Questions

Do you have questions? We have the answers.

What is the 33A model and why do you use it?

The 33A model is a methodological framework for the structured development of AI use cases, inspired by the Google Design Sprint. It combines strategic identification, conceptual development, technical validation, and rapid prototyping in a compact process. The accompanying AI Cards make complex AI concepts tangible and encourage creative ideation within the team.

Do we already need AI experience or data to participate?

No. The AI Design Sprint also works without existing AI expertise or prepared data sets. What matters is that your stakeholders are willing to actively participate. Data availability is assessed during the sprint itself and directly feeds into the prioritization of use cases.

How does the AI Design Sprint differ from a traditional innovation workshop?

The key difference: the outcome is not a slide deck, but a working prototype. The sprint directly connects strategic ideation with technical validation and implementation in one continuous process instead of isolated phases.

Which roles and people should participate in the sprint?

Ideally, a cross-functional team from business, IT, and management. For Phases 1 and 2, subject matter experts and decision-makers are needed. In Phases 3 and 4, technical experts take the lead, while business consultants support economic assessment and project planning.

What happens after the sprint?

You receive a clear implementation path with concrete recommendations. Typical next steps include developing the prototype into an MVP, scaling to additional use cases, or building internal AI capabilities. We are also happy to support you beyond the sprint.