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Innovation Journey

Duration

4 Months

Number of Participants

1800

Type

DISCOVERY engine

Impact

How we helped more than 1,800 Siemens R&D engineers worldwide turn a global AI ambition into a structured pipeline of actionable ideas.

The Overview

From AI Ambition to Action, Across Every Time Zone
A global Siemens R&D organization wanted to move beyond AI awareness and into real, hands-on application. The challenge: reaching more than 1,800 engineers across a dozen roles and multiple continents, each with different workflows, different tools, and different definitions of what AI could actually do for them. The Creativity Lab designed and facilitated a structured process that began months before the main event, guiding a network of over 130 ambassadors through deep problem space exploration before the organization came together for a full week of building, testing, and submitting real AI-powered solutions.

The Story

How we met.

The request came through the R&D organization’s learning and training network. A large Siemens division had already taken its first steps with generative AI, running an initial orientation for a small group. But first steps were not enough. Leadership wanted something bigger: a moment that would bring the entire global R&D community into contact with AI tools, not as observers, but as builders. The Creativity Lab was brought in to design the structure that would make that possible, and to ensure the week would produce more than enthusiasm.

The Problem

A Global Team Ready to Move, But Where to Start?
The R&D organization had the ambition and the tools. What it did not yet have was a shared understanding of where AI could create the most value, and a structured path from that understanding to real solutions. With engineers spread across Europe, India, Asia, Australia, and the Americas, spanning twelve distinct roles from software developers to finance experts to hardware architects, a one-size approach was never going to work. The risk was not a lack of motivation. It was the risk of moving fast in too many directions at once, solving symptoms instead of the problems that actually mattered.

Creativity Lab – Breakthrough Consulting

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Problem Space

Exploration

The Creativity Lab began working with a network of more than 130 AI ambassadors, the engineers and team leads who would later guide their colleagues through the main event. Over a series of weekly two-hour sessions spanning roughly three months, the team moved through a carefully sequenced process: mapping value flows within each role, conducting structured interviews across both insiders and outsiders to each function, challenging embedded assumptions through a technique called reversing assumptions, extracting jobs to be done, and reframing challenges into precise “How Might We” questions. Every insight was documented and cross-linked on shared digital boards, organized by role and ranked by perceived complexity and potential impact. By the time the preparation phase was complete, the team had surfaced more than 100 role-specific challenges and an additional 70 general use cases, giving the organization a rich, structured foundation it had never had before. The ambassadors did not just prepare content. They built genuine expertise in the problems their colleagues were facing every day.

Solution

Creation

The R&D AI Transformation Week ran across five full days. The first day was dedicated to inspiration: keynotes, internal use case presentations, and optional hands-on training sessions with specific AI tools available within Siemens. Days two, three, and four were the core of the work. Participants, organized into small teams by their ambassadors, selected challenges from the pre-built matrix, generated ideas using structured creativity methods including Crazy Eights, Forced Connections, and a collaborative building technique called “Yes And,” then converged on a single direction using a Now-How-Wow prioritization matrix. Teams then moved into solution framing, element storming to break large ideas into buildable components, and a full build day focused entirely on hands-on prototyping with AI tools. A dedicated idea validation canvas was available for teams whose ideas were too large to prototype in a week but too valuable to lose. On day four, teams tested, iterated, and submitted their solutions through a structured one-page format that fed directly into a live dashboard. The final day brought the organization together for a best practice showcase and an award ceremony recognizing solutions across multiple categories, including one for creativity. Across the entire week, 274 solutions were submitted. Of those, 129 were proof of concepts, 90 had reached minimum viable product stage, and 35 were already classified as productive solutions ready for use.
“Vibrant, energizing days.” “How did we even work without this?” “Learning, experimenting, breaking and fixing a few things, building an AI-powered future together.” These were among the responses collected from participants across the globe. The project lead at Siemens described the Creativity Lab’s contribution in terms of trust, commitment, and the ability to handle the unexpected, calling it a benchmark.

Outcome

More than 1,800 engineers across four continents spent a week not talking about AI, but building with it. The structured preparation phase meant that when the week began, no time was lost searching for the right problems to solve. Teams arrived with context, with pre-matched AI capabilities, and with a clear starting point. The result was 274 submitted solutions at varying stages of maturity, from validated concepts to tools already in productive use. Participants estimated significant time savings per solution, and while those projections were self-reported and varied in precision, the direction was consistent: the organization saw real potential for productivity gains across every role involved. Perhaps more importantly, the week shifted something harder to measure. Engineers who had never touched a generative AI tool left the week having built something with one. The question was no longer whether AI was relevant to their work. The question became what to build next.

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