Since I did my initial post on using AI in scenario planning I’ve been doing more sessions with clients where we use AI engines to both map out potential futures and paths forward, and also to stress-test strategies. Scenario mapping is a really undervalued use of AI tools IMHO and there’s something about the ability to bring structured thinking and a non-human perspective into it which gets you to places that you couldn’t get to on your own.
Since I’ve finessed my process somewhat I thought it would be useful to revisit this technique and set out some useful ways of doing this in a more structured way. What follows is not the definitive version of how to do it, but just the way that I’ve found works best. I’m not a believer in ‘magic prompts’ but I’ve tried to make this as practical as possible so I’ve included some example prompts on a separate document in case that’s useful.
Stage One: Set up
The first step in the process is to define why you want to do mapping in the first place and to set it up in the best way possible. Think of this as a kind of ‘pre-flight check’ before takeoff:
- Project space: I find it most useful to use a Project Space for this exercise (usually in Claude or ChatGPT) but you don’t have to. The benefit of a Project Space is that you can populate it with as much context as possible to help inform the analysis and that it’s a persistent space that you can come back to again and again. You can set the Project Space up so that GPT will only look at insight material that you upload, or you can instruct it to use the uploaded context alongside other authoritative sources.
- Objective: Be clear on what it is that you are trying to understand. Are you mapping out potential future scenarios to inform strategy and foresight? Are you generating options and pathways forwards to achieve a vision? Are you wishing to stress-test a strategy to look for weakness or understand what would need to change if certain shifts happen? What decisions will this work inform? Who is this for? What is the single question this mapping exercise should inform? Each of these use cases are valid reasons to scenario map using AI but being clear on this up front helps you to frame the objective from the start. State that objective to the AI engine.
- Domain boundaries and timeline: Be clear on scope e.g. category or geographic boundaries, what to include or exclude. And set a specific timeline. Is this a one year outlook for near-term strategy testing, or a three year view to inform strategic options, or a ten year projection to investigate possible shifts and what they could mean for the brand/business?
- Context: Upload as much context as you can to the Project Space/chat so that the AI has something good to work on. This might be anything from market and trends reports, to customer survey outputs, interview verbatims, transcripts, strategy documents, previous scenario maps.
- You can see an example prompt for this stage here.
Stage two: Identifying variables and uncertainties
The next stage in the process is to identify potential variables that may impact the trajectory that the future takes. You may well have a view on this but the AI can also generally do a pretty good job of recommending key variables to focus on (particularly if you give it context). It can also help you to differentiate between what is already pre-determined and what is less certain (and therefore worth scenario mapping).
I’ve learned that there is a subtlety to defining variables depending on your reasons (and timeline) for scenario mapping, which we can frame using a version of the futures cone.
There are slightly different approaches depending on what type of scenario mapping you want to do.
- Probable and plausible futures: here we should be looking at variables which are more likely to change and impact the future. I generally find this more useful because you’re considering things that have a higher probability of changing which is more helpful for stress-testing strategies or understanding the impact of elements that are more feasibly going to change.
- Possible futures: here you’re either extrapolating shifts out to the long term to see what could happen, or you’re dealing with variables and changes which are high uncertainty but also potentially high impact. It’s a bit like weather forecasting in that the further out you get the less accurate you’re likely to be, but looking at high-impact/high-uncertainty shifts can be a way to understand possible scenarios based on ‘black swan’ events, or to open up completely new thinking about the future.
Having defined your context and need you can now use the AI to identify relevant variables (or suggest your own). I usually find it most useful to have the AI suggest four or five that I can select from. If it’s probable/plausible you’re interested in, ask the AI to set out the four variables that are the most likely to happen and that will have significant impact. If it’s possible futures, ask the AI to give you four of the most high impact/high uncertainty factors or shifts. Perhaps look for uncertainties that are relatively independent of each other, but the AI can rank them for you if needed.
You can see an example prompt for this stage here.
Stage three: Mapping scenarios, creating narratives, visualising outputs
Once you have your variables you can create your scenario matrix. There’s two approaches that I’ve found useful here:
- Option one: You can select two variables that you consider are the most relevant, likely or interesting. You can then ask the AI to combine the two on a 2 x 2 matrix with a high/low on each axis to create four potential scenarios. This is a more directed approach since it allows you to focus on just two of the most critical shifts and juxtapose them to see what emerges. I find this most useful for probable and plausible futures since it’s more likely that you’ll have a view on which variables are more important to consider and it genuinely throws up some interesting perspective when they are set against each other.
- Option two: You can simply ask the AI to mash all the variables together into a 2 x 2 matrix in a way that gives you the best perspective on different potential options or paths. I find this most useful for possible futures since you’re dealing with a wider set of possibilities and what you need is breadth and optionality.
The 2 x 2 will give you four different scenarios, each quadrant representing a different future world. Ask the AI to give each scenario a memorable name that captures its essence and then visualise it. When you’re creating the visual (I’ve found that Claude is better than ChatGPT for this and you can also create a Claude Skill that will ensure that it visualises it in the same way each time) you can ask it to write a 30 word description of the scenario underneath each title.
You can see an example prompt for this stage here.
Stage four: Pushing further
I’ve found that the scenario maps can form the basis for some rich discussion in a workshop or strategy session, but there’s a number of other things that you can do.
- Develop scenario narratives. If you want to go deeper you can ask the GPT to develop a more detailed description of what that world looks like for each quadrant, perhaps with a specific lens (e.g. consumer behaviour).
- Stress-testing strategies: You can take your current or proposed strategy and ask how it would perform in each scenario, or what weaknesses are revealed, or what areas would need to be adapted. A robust strategy is likely to be one that performs reasonably well across multiple scenarios. A fragile one may be highly dependent on one specific future.
- Identifying signposts: For each scenario you can also identify early indicators that would suggest the world is moving in that direction. This means that it becomes almost like a kind of monitoring dashboard. Signpost X emerging increases the probability of scenario Y.
You can see an example prompt for this stage here.
So there you have it. A practical (or as practical as I can make it) process for using AI in scenario planning. As I said at the start, I think this is a highly undervalued technique but it’s important to recognise that the purpose of doing it is less about predicting exact futures and more about giving you a different perspective, helping you to think differently, and to create and understand options. And more options are a useful thing, especially when the world is moving so fast.
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