Executive sponsorship and a clear vision are decisive. Leadership ensures AI initiatives pay into business goals, receive adequate resources and gain organization-wide support – while giving teams the freedom to experiment.
We help your AI pilots arrive in everyday work.
Around 95% of gen AI pilots show no measurable P&L effect, often because they were scoped very small from the start. So we set an ambitious direction and begin with a small, low-risk first step. Leadership sets the direction, the teams bring their hands-on knowledge, and a first use case makes it into everyday work.
Almost every company uses AI, but few scale it. Redesigned workflows often make the difference: AI high performers redesign them almost three times as often (McKinsey 2025).
Leadership sets the direction. Teams find the lever.
A top-down mandate alone produces pilots without buy-in. Bottom-up experiments alone stay isolated solutions that don't scale. The value comes from combining both in a multi-directional strategy. According to MIT, this gap between vision and everyday work is where around 95% of pilots stall. A second gap matters too: between what would be possible and what people feel able to put in a budget request. We help close both.
Executive sponsorship and a clear vision are decisive. Leadership ensures AI initiatives pay into business goals, receive adequate resources and gain organization-wide support – while giving teams the freedom to experiment.
Proximity to daily operations and end-users is invaluable. This level identifies high-impact, feasible solutions for concrete problems in their own workflow, drives adoption, and ensures – through feedback – that tools are genuinely integrated into existing processes.
For every AI initiative – whether triggered top-down or emerging from a team – we think through six dimensions consistently. For each dimension we clarify the role of leadership and of the teams.
| Dimension | Leadership & management | Teams & ICs |
|---|---|---|
| Strategic focus | Set the vision, align use cases with business goals, define guardrails | Prioritize the most pressing pain points from daily work and customers; start human-centered and low-risk |
| Exploration | Create space, budget and permission to experiment | Test tools in practice, share findings, surface successful experiments upward |
| Responsible AI | Set binding AI standards, align with values, anchor governance | Adhere to standards, anticipate risks early, test for bias, safety and ethics |
| Resourcing | Provide platform, data access, skills and budget | Leverage existing data/tools; make the ROI case when more is needed |
| Impact | Define KPIs, connect impact to business goals | Quantify effects (efficiency, satisfaction, cost), track KPIs, make value visible |
| Continuous improvement | Institutionalize learning loops, enable scaling | Give feedback, iterate solutions, spread what works |
Good use cases are found systematically. We use the Creative Matrix for this, a method that connects AI capabilities with concrete business priorities.
Before the matrix: see the actual process
Event Storming starts directly with the work itself. In one room, with the people who actually run the process, we map the real sequence of events end to end, including the handovers, the waiting times and the workarounds that are rarely written down. The bottlenecks become visible before anyone says the word "AI". Only then does the Creative Matrix have something honest to work with.
How it works
A grid maps business goals (e.g. improve customer experience · increase efficiency · drive innovation · raise employee productivity) against AI capabilities (e.g. productivity assistance in daily work, conversational systems, semantic search over enterprise knowledge, custom AI applications on an AI platform). Each cell produces concrete "sticky-note" ideas.
Then we prioritize
We assess along two axes, impact and feasibility, and start where high value meets low risk. Every idea is validated before implementation.
The most effective use of gen AI augments human thinking and automates the repetitive. In both cases, people stay at the centre.
AI is only as good as the data it learns from. Before we scale, we build a robust data foundation.
Five factors of data quality
Accuracy · Completeness · Representativeness · Consistency · Relevance
Three factors of data accessibility
Availability · Cost · Format
Structured vs. unstructured
Tables vs. images/free text require different approaches. We clarify the type, quality and structure of your data as the first step of every implementation.
We honestly locate you in the maturity model and define the next realistic step – along the themes Learn · Lead · Access · Secure · Scale.
First experiments, individual tools, learning effects
Aligned use cases, governance, first scaling
AI as part of the operating model, organization-wide
Our approach – 5 steps
The EU AI Act phases in risk-tiered obligations for AI systems through 2028 – Article 4 AI literacy since 02.02.2025, Article 50 transparency from 02.08.2026, high-risk Annex III from 02.12.2027 and Annex I from 02.08.2028. We turn the technical requirements into system design – risk classification, documentation, logging, human oversight – working closely with your legal counsel.
Map your AI use cases against the Act's risk tiers (minimal, limited, high, prohibited), before you build.
The artifacts Article 12 expects (architecture specs, eval records, audit trails) are produced as part of how we build.
Review gates and override points, built directly into the system architecture.
Assessing the third-party AI providers you integrate against the obligations you carry as a deployer.
Our contribution is technical and does not replace legal advice. Final compliance sign-off stays with your legal counsel.
We start with an honest assessment of your maturity level and a prioritized use case that shows impact quickly. The direction can happily be bigger than the first step.
See what that looks like in practiceTell us what you're planningMethodological framework based on the Google Cloud AI Adoption Framework and the Responsible AI Principles. Figures: McKinsey, The State of AI (November 2025); MIT NANDA, The GenAI Divide (2025); Bitkom press release (September 2026). Figures as of the respective publication.