How Bable Is Reinventing How Smart Cities Develop New Solutions
BABLE Smart Cities wanted to grow faster. Its work was still tied to project fees. In a three-step program we mapped the current model, generated platform ideas with the team and set priorities for new services. BABLE now has a clear platform roadmap and uses AI to deliver knowledge at scale.

BABLE Smart Cities was founded in 2017 as a spin-off from the Fraunhofer Research Society and is headquartered in Germany. The company set out to help cities become smarter, more sustainable, and more livable by curating expert knowledge and brokering collaboration between municipal decision makers and private solution providers.
Today, BABLE has grown to more than sixty people and operates partnerships and projects across more than thirty countries. The long-term vision is a platform where public and private actors can exchange ideas, find solutions and build new services that improve quality of life for citizens.
The Challenge
By late 2021, BABLE had established itself as a strong enabler in the European smart city ecosystem. A steadily growing online platform supported their work. However, much of the value creation still depended on individual consulting projects rather than scalable, self-service offerings.
Leadership recognized a critical constraint: to grow their impact and scale the business without losing focus or flexibility, they needed clarity on which parts of their offering could be standardised and delivered as self-service. They also needed to understand how to evolve the revenue model, make better use of data, and maintain a simple user experience as the business scaled.
Our Approach
We supported BABLE from November 2021 to January 2022 using a modular program based on the St. Gallen Business Model Navigator methodology. The work was anchored by two on-site workshops, complemented by preparation and follow-up.
Understanding the Status Quo
We worked with BABLE's core team to map their current business model by breaking it down into its component parts: customer segments, value propositions, revenue mechanics, and the end-to-end customer journey. We explored customer personas and their key problems and needs. We reviewed the extended ecosystem around BABLE to surface constraints and opportunities. This analysis led to a focused research plan and a prioritized list of opportunity areas for further exploration, including technical scalability of services, customer self-service, and data monetisation.
Ideation With the Extended Team
Using more than fifty-five business model innovation patterns, we facilitated a structured ideation session with roughly thirty participants from across BABLE. Starting from the agreed opportunity areas, teams generated and ranked ideas. Using the Platform Value Canvas, we shaped these ideas into specific services designed around distinct customer needs—services that could be delivered repeatedly at quality and were mapped with a strong focus on real user needs and scalable delivery mechanisms.
Assessing and Prioritizing Services
Based on the newly developed ideas, we assessed the most attractive services and prioritized them for structured development. The outcome was a clear platform roadmap with a sequenced approach to building high-value services, along with the foundations for an evolved business model.
Leveraging AI as an Enabler
One of the most critical enablers in BABLE's transformation has proven to be the integration of AI across their offering. By embedding AI into their knowledge base, BABLE was able to make its accumulated expertise more accessible, more actionable, and faster to apply.
Impact
BABLE now has a platform roadmap with prioritised services that can scale beyond individual consulting projects. The organisation has shared clarity on what remains bespoke and what becomes standard, self-service, or data-driven. Early integration of AI is accelerating access to guidance for users and supporting lighter, more repeatable delivery.
This shift moves BABLE from enabling individual projects to enabling entire ecosystems, with a model that can grow with their community.
Richard Stechow
Key Learnings
Precise mapping comes first
Platform transformation begins with a clear understanding of the current business model and deliberate decisions about what to standardise. This foundation enables structured scaling.
Pattern-based ideation breaks conventional thinking
Using business model patterns helped the team explore viable options across different opportunity areas, moving beyond established ways of working.
Expert knowledge becomes repeatable through systematisation
BABLE's competitive advantage lies in its accumulated expertise. Making that knowledge accessible and actionable—through both platform design and AI integration—turns it into a scalable asset.
AI accelerates impact when paired with clear service design
The integration of AI into BABLE's knowledge base works because it's grounded in disciplined service design and a focus on real user needs. Technology is the accelerator, but purpose and design drive the outcome.


























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