SuperpositionSuperposition7
Superposition7: seven areas for making AI work
Superposition7 is our framework for looking at how a consumer brand uses AI. It covers people's skills, leadership, rules, the way work is designed, team culture, results and company knowledge.
The seven areas help you discuss what is working and where to focus. The free Superposition7 scorecard gives you a profile across them, based on your answers about what your team does today.
Take the free scorecardWhy AI is hard to get right
When businesses start with AI, they often focus on one thing, like writing an AI policy or setting up some training. It's a sensible first step, but on its own it rarely changes how the business works.
In our work with consumer brands, we've seen what separates the companies that get real results from AI from those that don't - and it comes down to these seven things.
Fluency
Build skills, judgement and confidence.
Help people use AI well through practice on real work. They need to know how to frame a task, provide useful context and judge the result.
What good looks like
People can use AI on relevant tasks, explain how they checked the work and adapt when the first attempt falls short. Their confidence grows through experience.
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What it covers
- Practising on tasks from your own role, with feedback and time to improve.
- Giving clear instructions, choosing useful context and adapting your approach.
- Checking outputs, recognising limitations and knowing when to ask for help.
Ask your team
- Can people show how AI helps with a real task in their role?
- How do they decide whether an output is good enough to use?
Ownership & Leadership
Set direction and make room for the work.
Give AI work a clear purpose, someone responsible for moving it forward and the backing to make progress. Leaders make time for the work and agree what can give way.
What good looks like
People know what the business wants to achieve and who is responsible. Leaders make time available, resolve obstacles and support the work through to completion.
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What it covers
- Connecting AI work to business goals and agreeing clear priorities.
- Giving people clear responsibility, access to decisions and suitable tools and resources.
- Resolving competing demands and deciding what can pause, change or stop.
Ask your team
- Who is responsible for moving this work forward?
- What will you make room for, and what will give way?
Rules
Make safe, confident use possible.
Agree practical boundaries for using AI: which tools people can use, what information it can access and where human review or approval is needed.
What good looks like
People understand what is allowed in their work and can apply the guidance. Permissions and safeguards keep pace as tools and tasks change.
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What it covers
- Choosing suitable tools and accounts, with appropriate privacy and security settings.
- Agreeing what AI may access, change or share, and what stays restricted.
- Making review requirements clear, with a route for questions, exceptions and problems.
Ask your team
- Do people know what information and systems they may use with AI?
- Where is human checking or approval required?
Workflow Design
Choose worthwhile work and make it dependable.
Choose where AI could make the greatest difference to the business. Then design, test and maintain dependable ways of doing that work.
What good looks like
The business can explain why it chose a particular opportunity. The process works in practice, has clear ownership and can be maintained when things change.
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What it covers
- Comparing opportunities by potential benefit, effort, what is practical and what else needs to be in place.
- Reimagining whole processes, including work to simplify, remove or keep human.
- Testing the approach and agreeing responsibilities, quality checks, maintenance and fallbacks.
Ask your team
- Where could a change make the biggest difference for your business or customers?
- What would it take to make that change dependable in everyday use?
Adoption & Culture
Learn together and protect meaningful work.
Create an environment where people can explore AI, challenge it constructively and share what works. Make deliberate choices about the human contributions you want to preserve.
What good looks like
People build on each other’s learning and feel able to challenge poor results. The team agrees where AI helps and which meaningful work it wants to keep human.
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What it covers
- Sharing useful methods, experiments and lessons across the team.
- Making it comfortable to question outputs, raise concerns and discuss mistakes.
- Protecting creative work, relationships, expertise and professional judgement.
Ask your team
- Can people share both successes and concerns openly?
- Which human contributions do you want to protect or make more time for?
Results
Measure results and act on what you learn.
Find out whether AI is making the business better. Use evidence about benefits, costs and trade-offs to decide what to improve, expand or stop.
What good looks like
The business can explain what changed and what it cost. Evidence informs decisions, including stopping an experiment that has not proved worthwhile.
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What it covers
- Defining success and choosing a starting point or comparison.
- Checking quality, time, cost or customer experience, including the effort of checking and rework.
- Acting on what you learn and checking whether the adjustments helped.
Ask your team
- What would count as a worthwhile result, and how would you recognise it?
- What have you changed because of what you learned?
Data & Context
Make data and company knowledge usable.
Give AI the raw data and company knowledge it needs to do useful work. Include the context held in people’s heads, as well as what is already written down.
What good looks like
People and AI can use reliable information for the task. Useful knowledge and corrections are captured, and someone keeps the sources up to date.
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What it covers
- Identifying relevant records, brand guidance, customer knowledge and task context.
- Drawing out unwritten knowledge through examples, explanations and feedback.
- Keeping trusted sources current, resolving inconsistencies and making them accessible.
Ask your team
- What does someone need to know to do this work well?
- Where does that knowledge live, and who keeps it current?
Where should you start?
Start with what your business wants to achieve and what is getting in the way. The lowest-scoring area may need attention, but your priorities also depend on the work, the risks and the support available.
A useful change might be training, clearer rules, a better document or a different process. Keeping work human or stopping an unhelpful experiment can also be the right decision.
See your profile across the seven areas
The free Superposition7 scorecard asks 21 questions about what your team does today. It takes about five minutes. You'll see your results on screen and receive a PDF report by email.
Your profile reflects your answers. Use it to start a discussion with your team about where to focus.
Take the free scorecard