Today, existing local organizations with a demonstrated track record in the use of drones, robotics, data, and/or AI join and come together in the network through the “Flying Labs Local Model.” One of the Flying Labs network’s key strengths is its diversity; the very diverse setup of each Flying Labs, the diversity of existing expertise, and the vibrant cultural diversity with over 30 Flying Labs being part of the network today. This diversity is vital to growing the network. How reductive and limiting would it be to have a “one-size-fits-all” approach when growing a global network of local experts?
At the same time, diversity is also a challenge when it comes to network governance—a key success element when shifting power to a decentralised network. Several donors and partners have asked us repeatedly, “who is responsible for what?” and “how can you make certain that the network evolves in strength and quality?” And as the network grows, individual Flying Labs themselves have struggled to understand how they can grow and contribute to the network’s overall strength. Therefore, we embarked on an extensive co-creation mission together with Flying Labs throughout 2020 to find answers to these questions and complement the network affiliation model with an additional model, the “Flying Labs Global Model.”
The Global Model is almost like our own ISO standard, fully adapted to our context.
Why is this new model so essential to the success and long-term sustainability of the Flying Labs network? And why have we waited until 2020 to introduce it?
Here a summary of our thoughts on these questions:
- Decoding words such as Sustainability, Impact, Collaboration, etc.: we use these words daily, so do Flying Labs, donors, and partners. However, are we talking about the same thing? Possibly not. It was important for us to determine what exactly each of these words means for Flying Labs. Doing so only five years after establishing the network allows us to take advantage of the many experiences we jointly accumulated over the last years. It also allows us to include the very pertinent questions of donors and partners for each definition and create a common language for all involved. For example, when we talk about ‘impact,” we are all aligned on what this means for our work.
- Translating “Strength” into key objectives: just as for the previous points, talking about “strong Flying Labs” can be interpreted in many ways. Therefore, the Flying Labs Global Model’s first goal was to translate the word “strength” into Flying Labs’ context, looking at it from various angles. The reason why it was so essential to co-create this model with a large number of Flying Labs of multiple settings as well as “age.” For new Flying Labs who have just joined the network, strength might boil down to something entirely different than a Flying Labs who have been part of the network since its inception. Doing so after accumulating the first years of experience in Flying Labs allows us to create a model that genuinely focuses on growth and is not just a simple reporting tool to please stakeholders with numbers.
- Identifying key strengths and gaps: Each objective is broken down into five-to-eight easily measurable criteria (easily meaning by answering “Yes” or “No”). Flying Labs perform a periodic self-evaluation of these criteria. The results are summarized in a simple graph (or selfie) for each Flying Lab, identifying strengths and gaps quickly. The data of all Flying Labs is then aggregated into a network-wide benchmark. Given the model’s broad scope, Flying Labs’ goal is not to hit a 100 percent mark on each criterion and objective. It’s to become aware of their strengths and gaps and collaborate within the network. If one Flying Lab chooses to improve a specific objective, they can easily find another Flying strong in this particular area. Once more, the network’s diversity is its key strength, allowing each Flying Labs to adapt to the local needs.
- Identifying capacity strengthening needs: For WeRobotics, the network-wide benchmark’s main advantage (and the benchmark’s changes over time) helps us identify areas requiring more dedicated capacity-building and specific criteria in need of more resources to support Flying Labs successfully.
- Taking responsibility: The ‘Global Model’ allows all involved to understand their responsibilities clearly. Flying Labs are responsible for deciding where they want to invest their resources and what objectives they want to pursue. They are responsible for setting their goals and improving their skills to create a diverse and robust network—with support from WeRobotics. Flying Labs and WeRobotics are jointly responsible for evolving the model’s objectives and criteria to adapt to changing needs.
Successfully implementing this new model in October 2020 allowed us to create a first baseline and network-wide benchmark. After deciding on the next steps and goals, each Flying Lab will improve its identified gaps. Flying Labs do so either with the help of resources created by WeRobotics and through mentorship from other Flying Labs. The next round of self-evaluations this Spring will allow us to improve, grow, learn, and adapt continuously. As one Flying Lab put it so perfectly: “The Global Model is almost like our own ISO standard, fully adapted to our context. While the first evaluation might be frustrating, the model allows us to create a common language and goal within our Flying Labs on how to become stronger and more sustainable all the while contributing to the overall success of the network. A temporary frustration worthwhile seeing the long-term outcome for our and all other Flying Labs.”
It’s important to always keep in mind that this model is first and foremost for internal use, understanding strengths and gaps, and addressing them, so Flying Labs have a clear path towards individual success and sustainability. It is not a tool for self-gratification or to impress others with performance. We would have built a very different model if this had been our goal.
Learn more on the Flying Labs Local and Global models on our website here.