I’m a big fan of the idea of AI amplifying human capability and have long been working to fully embed AI into my strategy work. For me it’s been a real game-changer. It’s not just about efficiency, although it is like having a mini-team of researchers always on hand which is amazing. It’s also that it gets me to places that I don’t think I would have got to on my own.
So I thought it would be useful to talk about how I’ve integrated AI into my strategy process. I’m going to refer to my use of ChatGPT Project Space as an example for this. I’ve only really been using it to its full potential for the past few months but I honestly think that it’s one of the most undervalued and underused features in ChatGPT (it’s over there in the left sidebar). There’s a particular reason why I find it so useful – it provides an evolving strategic thinking environment with a persistent context and AI fully embedded. Establishing a defined context for every project space which remains persistent across multiple conversations and files is particularly helpful. It’s like having a thinking partner that you can pick up a conversation with at any time, but who instantly remembers your project objectives, angle, research and the work done so far.
Project Spaces is only available to the paid tier (ChatGPT+) and the Teams and Enterprise versions, and to my knowledge Gemini and Claude don’t have equivalents (although Notebook LM is a very useful research tool), but I think many of the principles of what I’m talking about here have broader application. I’ve summarised a few key techniques for how I use it to create long-running, named workspaces (kind of like a combined digital whiteboard and research analyst), and to make it as useful as possible I’ve broken down some key steps, and given you a downloadable template at the end.
Start with a ‘project charter’
Start by naming the project, and then under ‘Add instructions’ you can set up specific contexts to inform the GPT and tailor how you want it to respond throughout the project:
- Objective: explain what you’re trying to achieve, goals and deliverables, key questions to answer
- Context: brief but relevant background to the project itself, such as why it’s important now, relevant challenges or opportunities. You can also set a strategic context for the GPT to ‘hold in mind’ throughout e.g. key priorities that you want to focus on, or known constraints and things to avoid.
- Timeline: A rough timeline can help. For example research (week 1), strategy design (weeks 2 & 3), final recommendations (week 4)
- Role and tone: I’m a big fan of giving AI context by asking it to play a specific role in prompts so here you can use the ‘add instructions’ box to set a default strategic mindset and tone for the project. For example, you could ask it to assume the role of a world-class strategy consultant specialising in a particular field, or to adopt a challenger mindset throughout the project. You can also set out your preferences for communication style and how you like to see outputs. And you can inform it of your preferred working style (e.g. ‘I like to collaborate iteratively and may ask you to refine or build on your outputs’ or ‘actively help manage the project by reminding me of past threads if relevant’ or ‘propose next steps if you see a logical progression’).
This set up is key as it establishes the defined context for how the GPT can add value to your strategic process, and it means that it retains a consistency of perspective and that you don’t need to rebrief it every time.
Curate your inputs
The project space can be used to pull together all kinds of inputs from research reports, to transcripts, to persona docs, strategy decks, customer journey information, PDFs, stakeholder interviews, briefing docs, brand guidelines, email summaries, meeting notes and so on. Curating these inputs is (for me at least) a key part of the strategic process as it enables you to synthesise insights from across a fragmented set of source material. I think of this as a repository for anything that may be relevant to the project.
As well as summarising key insights you can use this to give context, identify patterns or potential areas of tension. In a way you’re creating your own RAG (Retrieval-Augmented Generation: finding relevant information from specific knowledge sources, and using that to give context for the LLM to generate more accurate responses). Remember that your curated inputs will provide important context throughout so it’s important to keep these updated with new information as you get it. I’ve actually gone as far as creating cheat sheets for different strategic concepts or models that I can use as inputs to inform how the GPT thinks about specific contexts.
Toggle between more specific personas as you go
Once you’ve set the project space up in this way you can begin your analysis. The classic ‘4Cs’ (Customer, Company, Category, Culture) is a great way to delineate different research threads that you can then bring together. It can be helpful to use personas and to assign specific roles to the GPT (and even defined mental models) for different parts of the project. For example, asking it to act as a competitive intelligence analyst or a McKinsey strategy-consultant or a behavioural economist.
Toggling between different perspectives like this is great for testing thinking from different angles and getting diverse viewpoints. I’m an independent consultant and so tactical personas can amplify my ability to bring in different perspectives or approaches. It’s like having a multidisciplinary team at your disposal. You can think of the role that you put into ‘Add Instructions’ as a strategic co-pilot that is always there and context aware, and the tactical roles as temporary invited guests with individual skills to enrich or reframe thinking.
Track strategic thinking over time
You can build a kind of narrative arc of strategy as you go. Separate threads can be labelled and used for different stages of the project. For example, phase one might be research and landscape scanning, phase two could be emerging insights and tensions, phase three might be strategy synthesis, and phase four could be a communication or execution plan and an SLT-ready narrative.
You can ask it to cross-reference findings across inputs or threads and to keep a running log of key insights as you go. At various points you can ask it to summarise what has been discovered so far, or to reframe project goals based on the insights that have been uncovered, or to define the key threads that have emerged from the analysis. This tracks momentum but also helps with clarity and structure, and makes iterative learning possible and visible.
Co-developing frameworks and roadmaps
Being honest, this takes a bit more work to get it good, but it can be useful to see how the GPT assimilates information as a matrix, or a framework, or another output. You can use these at different stages of the process. I may use them as inspiration but I usually end up doing the final work myself as this is an important part for me to think through rather than the AI. But it can provide a useful starting point.
Running scenarios
Bearing in mind you will have loaded the GPT with potentially significant amounts of context and information to work from it can be really useful to run scenarios or foresight exercises as part of the process. For example, you could ask it for three future scenarios based on weak signals or insights which you’ve defined, or to set out what black swan events are most likely to disrupt the plan, or the second and third order effects of a competitor entering the space.
Distilling and packaging outputs
Again, this one takes a bit of work to get good but you can easily generate summary slide decks, roadmaps or other outputs. I prefer to get it to suggest structure and essential flow before compiling the deck myself and, as I think I’ve said before, I always like to write myself rather than allowing the AI to do it as writing is how I think and learn. But it can provide a great starting point from which to work.
The key thing that I’ve found is that using Project Spaces makes it far easier to truly embed AI throughout the process, and to build a context-rich chain of thought. You don’t have to deliberately remember ‘Oh, this might be good for AI to input into’ because it’s right there. For me, it’s kind of like having a team of research assistants that are always up-to-date with the context for the project and understand the full journey, or a living notebook, or a strategy room that you go into to think.
The biggest lesson for me? Rather than thinking about AI just as a productivity tool, you need to view it as a thought partner and a thinking environment.
I’ve created a full template for how to use ChatGPT Project Space in the strategic process, which you are free to use and which you can access here.
A version of this post appeared on my weekly Substack of AI and digital trends, and transformation insights. To join our community of over ten thousand subscribers you can sign up to that here.
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