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From AI momentum to lasting capability

Our experts Matthias and Maike demonstrate how organizations unlock hidden AI potential and translate it into lasting capabilities and competitive advantage.

Most AI pilots fail to scale – some studies say as many as 95%. Organizations invest heavily, creating the appearance of momentum. But without strategy, governance, and organizational change, AI remains a thin layer on top rather than becoming part of the operating system. The result: visible pilots to celebrate, but little lasting capability. 

At Ray Sono, we’ve seen this first-hand. In recent months, Matthias and Maike have worked with organizations across industries – guiding motivated teams, ambitious leaders, and highly engaged ambassadors. In doing so, they share the insights and methods behind our approach: how we steer AI projects from initial curiosity to measurable business impact – turning energy and experimentation into lasting competitive advantage.

Beyond lists

How do organizations unlock hidden AI potential? 

The case we refer to is one we encounter frequently: there is energy and curiosity around AI, but initiatives often remain superficial. Departments produce use case lists most of them focused on personal productivity or experimentation, with minor efficiency gains rather than generating transformative business impact.

In this project, the mandate was broader: strengthen AI culture, assess readiness, and build a foundation for scaling. That meant going beyond what was already on the table. Together with process owners, we stepped into the day-to-day. Into the workflows where real decisions are made and value is created. By challenging assumptions and mapping processes with those who run them, we uncovered where AI could unlock sustainable, organization-wide impact. And these are not the “fun” use cases that usually come to mind when approaching AI implementation superficially, but the ones that truly make scaling stick. 

The outcome was a sharpened portfolio:

  • more than 80 initial ideas  

  • narrowed down to six core business processes 

  • 16 clearly defined AI slices, clearly scoped process areas where AI integration delivers meaningful value 

  • five strategic lighthouse projects, all supported by a shared governance and evaluation model

Our approach

Three levers for lasting AI capability 

We’ve distilled our experience into three levers that consistently make the difference:

Create clarity

Icon Create clarity

A roadmap starts with visibility.

We help organizations clean up and structure their AI landscape. With our AI Readiness Framework, we assess maturity, spot overlaps, and build the shared visibility that makes scaling possible.

From Ambition into action

Icon Turn ambition into action

Structure is stronger than intention.

Even the best ideas fail without delivery mechanisms. That’s why we combine portfolio structuring with capability models and governance frameworks – turning ambition into an executable roadmap with clear priorities and resources.

Empower culture

Icon Empower culture

People are the real multiplier.

Employees won’t embrace AI without understanding its value or feeling supported. AI use is never for its own sake; it must serve meaningful goals in the interest of employees. We enable champions and spark a culture that makes scaling stick.

Our AI Capability Model

It puts the levers into execution by translating ambition into measurable capabilities. Rather than focusing on lists of use cases, we define the organizational capabilities a company needs to scale AI – from governance and infrastructure to change management and culture. Each capability is described, linked to business impact, and assessed across maturity levels. This makes AI readiness measurable and provides leaders with a concrete roadmap: where to focus, how to allocate resources, and how to move from experiments to embedded capability. 

Outlook

From momentum to transformation 

The next chapter of AI will not be written by endless lists of use cases. It is being written by organizations that go deeper: embedding governance, building readiness, and nurturing a culture that scales. 

That is exactly what Matthias and Maike are working on together with the client right now: 

  • Rolling out the AI capability roadmap, focusing on which capabilities to advance first and aligning resources accordingly. 

  • Embedding governance frameworks into daily operations so AI shifts from side experiments to a managed business capability.

  • Supporting internal champions to gain visibility, strengthen confidence, and spread adoption across teams. 

What makes this progress move quickly is the combination of internal expertise and external experience: champions inside the organization know where the pressure points are, while we bring the frameworks, methods, and thousands of hours of implementation practice to make scaling stick. 

And that is what excites us most: partnering with ambitious organizations to turn curiosity into competitive advantage, here and now. 

If your organization is stuck in pilot mode, it’s time to shift from lists to lasting impact. 

About Maike

Maike Funkert, Senior Consultant Product Design & Research at Ray Sono, turns research insights into design decisions that move organizations forward.
With a background in social anthropology, innovation consulting, and design thinking, she helps companies understand real needs and translate them into services, products, and processes that work in practice.

Her approach combines empathy and structure: listening closely, testing assumptions early, and guiding teams through complexity with a clear sense of direction. For Maike, user-centered design is not just a method – it’s a way to create solutions that are relevant, feasible, and built to last.

About Matthias

Matthias Grimme, Senior Consultant Product Design & Research at Ray Sono, explores digital opportunities through research, strategy, and a strong user focus. 
With more than nine years of experience in service and strategic design, he helps organizations turn digitization into services and business models that work in reality. His approach is user-centred and hypothesis-driven – testing ideas early, learning fast, and developing solutions that create measurable value. 

What sets him apart is his ability to connect deep research with practical design, ensuring that innovations are not only desirable, but also feasible and viable. 

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