Using AI Pragmatically: From Experiment to Business Results

Our learning journey started small: trying out initial tools, running internal trainings, exchanging insights as a team. From this, an internal learning space emerged where we could voice uncertainties and share experiences. This experimentation became the foundation for bringing the topic to life with clients.

Along the way, we explored different use cases – aiming to understand where opportunities and boundaries lie. Three insights stood out in particular:

• Reliability and Reflection
  In trainings, it quickly became clear: AI delivers impressive answers – but not always correct ones. We experienced firsthand that results need to be verified and critically interpreted. Reflection and methodological competence are therefore essential if organizations want to use AI effectively.
• Making Blind Spots Visible
  We had an internal meeting transcript analyzed by AI. The analysis brought new perspectives to light and revealed topics that had been underrepresented in the discussion. This showed us how AI can deepen reflection and improve decision-making.
• Recognizing Boundaries
  When experimenting with image generation, we soon reached practical limits. Not every application is mature enough for professional use. The learning: use cases need to be chosen carefully to create real value

All three experiences demonstrate: AI is not a self-runner – it’s a tool that creates impact only when applied in the right context. From this, several key insights continue to shape our work.

From our experience, we’ve distilled several core messages:

• AI is a change topic, not just a technology topic. It’s about culture, collaboration, and learning processes.
• Experimentation is essential. Progress comes from trying, reflecting, and adapting – and that’s also how fears are reduced.
• Teamwork matters. A small, dedicated team creates accountability and keeps the momentum going.
• Uncertainty is part of the process. What matters is staying open and reflecting actively.

These principles turn isolated experiments into systematic transformation. That’s where our approach comes in.

To help organizations move from endless discussions into tangible action, we use a structured framework: Rapid Results in 100 Days.
This approach combines focus and speed – creating space for measurable outcomes.

• Activate key players – cross-functional teams take ownership.
• Time-boxing – a 100-day focus prevents projects from stalling.
• Create an experimentation space – structured testing, regular progress checks, and transparent communication build visibility and trust.
• Secure resources – ensure access to time, tools, data, and support to enable safe exploration.
• Embed reflection – learning and transparency are integral parts of the process.

This turns early uncertainty into a clear learning and development path.

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Our own experiences – and our work with clients – have revealed three key benefits:

• Driving performance – teams achieve measurable progress within weeks.
• Fostering innovation – success comes from fast experimentation, learning, and iteration. AI accelerates prototyping and makes potential tangible.
• Building capability – employees develop strategic and practical skills (e.g., prompt design, tool literacy, critical reflection). AI fosters learning spaces where teams can explore uncertainty and co-create change.

The combination of these dimensions makes AI a powerful lever for change – showing that transformation is not about technology alone, but about how organizations learn and adapt. For us, AI is more than a tool – it’s a mirror for change. It challenges us to try new things, let go of the old, and tolerate uncertainty. At the same time, it highlights how essential learning culture and courage to experiment truly are.

When organizations invest in learning, they don’t just implement AI – they use it as a driver of transformation.

To move from insight to implementation, we recommend:

• Start small and concrete – choose use cases with visible impact.
• Create learning spaces – enable teams to share experiences.
• Build in reflection – evaluate outcomes both technically and culturally.
• Run several initiatives in parallel – collective learning accelerates progress.

These steps lay the groundwork for visible impact within a short time and help build confidence in working with AI.

AI is not a finished product – it’s a catalyst for learning and transformation.
Our own experience has shown: the greatest value emerges where organizations start small, reflect deeply, and learn systematically.

So, where in your organization could you achieve a measurable Rapid Result within 100 days?

We support teams in creating meaningful hands-on experiences with AI – generating value for efficiency, innovation, and culture. Let’s define your first step together.

Riverside Change Consultants at the Copilot training with Solvion