When working with design-driven development and innovation, you need to be able to systematize your learning during a project and adapt your actions, decisions and experiments according to the most fresh and relevant knowledge and learning. But often our projects and the constellations of teams, organizations, partners, etc. are complex and work on many levels simultaneously. That’s why we need a systematic approach to what we investigate, how we investigate, and how we extract learning and activate it.

This tool is a single ‘mechanism’ combining a number of elements to help you and those you work with to continuously keep track of your research questions, knowledge and learning. The tool may seem a little overwhelming at first, but once you get used to it as a framework for talking about learning, it becomes a huge help in both smaller and larger projects and tasks.

Understand the model

Working design-driven in development projects means, among other things, that we strive to work iteratively as much as possible. In its simplest form, this means that we 1) Set up a hypothesis that we design a way to prove or disprove, 2) perform the experiment and, based on 4) the learning from this 5) we revise or adjust the original hypothesis. With each roll through this cycle, the hypothesis becomes more and more accurate or optimized as a solution or answer to what is being worked on.

The core of the tool/model is that our work takes place at different strategic levels at all times. Many of the tasks in a project take place on a daily basis, what you could also call day-to-day operations (what we do), while others take place on a tactical level (how we do it), often discussed and worked on in weekly team meetings, for example. Some is at a strategic level, such as what you discuss with a management or project manager (why we do it), and last but not least, at the level we call strategic narratives, which is the impact the project has in the world, on other initiatives and potential future value creation. This level is often discussed with an external steering committee or sounding board (What is the long-term value we create in the world).

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For each of these levels, there are different approaches, procedures, tools and ‘rules of the game’ for project execution and management. The pace of work and frequency of new decisions is also different. At the daily operational level, experience, knowledge and new decisions are created and made on a daily basis, whereas at the strategic narrative level there might be a steering committee meeting once a year or at the start and end of the project.

This allows you to design learning machines that work at different paces at different levels. So we don’t have to wait until the project is over before we start harvesting learning systematically, but we can do it continuously at operational, tactical and strategic levels, as long as we are clear about what questions and curiosities we are investigating at each level and how they affect each other.

The learning we gain at the operational level, in addition to optimizing our daily tasks, is intended to inform the tactical level on what adjustments need to be made here. The learning we gain at the tactical level should inform the strategic level and so on.

The Learning Mechanism helps you define which learning hypotheses (curiosities) you want to explore in the project you are working on, how you collect data (data collectors), how and when you process data and turn it into learning (learning machines).

How to use the tool

1. Brainstorm curiosities
   Start by spending a good amount of time brainstorming curiosities across the team and maybe partners and stakeholders. A curiosity is a genuine interest in understanding, debunking or confirming an assumption or area of the project. A curiosity is a sentence that starts with “I’m curious about…”. Write down your curiosities on post-its, one curiosity per note.
2. Define the four loops in time
   Explore natural boundaries between the four loops. Is the first loop (operation) running on a daily basis? The tactical loop runs for a round in a week’s time, similar to your weekly team meetings etc. Note the time and possible revision events (a meeting, an action, a decision-making event) for each of the four loops.
3. Sort curiosities by loops
   Now take all your curiosities and distribute them according to which level they relate to. Some curiosities might be about what you do on a daily basis, some about how you work, some about the purpose or impact of the project, etc. You’ll find that some levels get more curiosities than others. This indicates how you as a team think in the first place. If you only have curiosities at the strategic level, you may need to consider whether you’re focusing enough on the valuable learning you’re gaining on a day-to-day basis in the project. These learnings can over time helps to inform and determine the strategic direction of the project? If you find that some levels are too low on curiosities, spend some time discussing and brainstorming new curiosities to this specific loop.
4. Prioritize each pile of curiosities
   Now take each pile separately and prioritize them according to importance and how exciting you think the curiosity is. It’s about prioritizing both what’s most important for the project and what gives the team the most energy. Once you have done this for all four loops/levels, write the 1-3 top-priority curiosities for each level on the model. Take pictures of the others, or save them somewhere you can find them again when you’re further along in the project. For now, it’s important to narrow down you scope, so that you start to explore, consciously and subconsciously, the most important curiosities in your daily work. At this point, each team member can choose the curiosities that are particularly important to them in terms of personal interest or professional development.
5. How do you gather data and knowledge?
   Looking beyond your selected curiosities, consider what data you will need to collect during the project to explore your learning hypotheses/curiosities. In many projects, you collect too much data because you’re afraid you won’t have the data you need when it is time to evaluate. A huge amount of time is wasted documenting things that are not relevant at all. So this is your opportunity to document and collect data targeted at what you want to learn in the project. Too much data can often lead to apathy and by that losing learning potential, so be aware of how much you actually think is enough here. Maybe you don’t need to track every movement on your website or do 200 interviews. Note on the poster the main sources of data and how you will collect and store your documentation (data collectors). Of course, as the project progresses, it may be necessary to revise this part, but it often turns out not to be necessary.
6. How do you turn knowledge into learning?
   Many people mistakenly believe that data and raw knowledge is learning. But knowledge is only learning when it is activated and used to make better decisions and support better behavior. Therefore, you need to carefully consider where and how you apply your collected knowledge at the different levels/loops. A classic opportunity to establish a learning machine would be your team or status meetings where you incorporate a process that ensures you look at the experiences of the week/period and how they inform, refute or confirm the tactical curiosities you are working with. Similarly, at the board or steering committee meeting in relation to your strategic or strategic narrative-level curiosities. What is the process, how do you get from input (data/knowledge) to processing (learning machine) to output (learning and decisions) for each occasion. You’ll find that you need to think about the interplay between data collectors and learning machines. Of course, learning machines will also produce knowledge and experiences that are relevant at other levels than where it is operationalized (e.g. knowledge and ideas for the strategic level arise when you work with operational/operational learning). It’s important that you think of a good way to collect it and send it to the learning machines where the new data/knowledge is relevant.
7. Revise continuously, respecting the different paces
   Once you get started, you’ll quickly find that curiosities at the lowest levels of the model (operations and tactics) will quickly need to be replaced by new curiosities as they are more practical in nature. You quickly figure out how to do things, so to speak. It takes longer and requires more nuance to uncover the why, or strategic level. Use the model as a management tool to keep the different levels of the project separate so you can focus where it’s needed most. The larger and longer a project is, the more appropriate it is to keep track of the different levels and what you are investigating and learning about for each level. The pace of the first loop is very fast compared to the slow pace of the most strategic loop. A classic cause of stress-related anguish in modern people is that they get to work in strategic levels at the pace of the operational level. And it’s easy to do this if you don’t define what and how you work in the individual loops and events/learning machines

As you may have already figured out, this thinking and framework for learning can be adopted in projects that don’t necessarily have an expiration date – for example, as a framework tool for the learning organization, the learning life, etc.

Thinking, framing and cultivating genuine curiosity are at the core.