New version of Superpowered Professional
A more precise version of the methodology that better captures how people really work with AI.
Published: May 22, 2026
AI has moved forward. And with it, the meaning of "being able to work with AI" has changed.
It is no longer enough to know whether someone uses ChatGPT, Copilot, or Claude. It is not enough to write a good prompt. Today, what matters more is how someone thinks with AI, how they give it context, how they iterate and verify, whether they save what works, and whether AI changes the way they do their work.
That is why we are introducing the third, updated version of the Superpowered Professional™ methodology.
This is not a cosmetic update. It is a more precise version of the methodology that better captures today's reality of work with AI: from first practical uses, through personal workflows, all the way to changing entire work agendas.
You can find the full methodology on the Superpowered Professional Methodology page.
#What is new in the third version
#1. The result is not only a score
SP Score remains important, but we no longer want to read it as the main answer.
The score is a baseline value. It helps track movement over time, compare groups, and see where there is room for growth. But by itself, it does not say enough.
The new result stands more clearly on four layers:
- Level - where a person is on their AI journey,
- Profile - how they naturally create value with AI,
- Growth Edge - what will help them most right now,
- SP Score - a numeric baseline for measuring movement.
In other words: less "you have 61 points", more "AI is already part of your work; the next step is to make it a repeatable workflow."
Read more about why the score is only one part of the result in How we score.
#2. We look more closely at what people really do with AI
Self-assessment is useful. But with AI, it often misleads.
Some people underestimate themselves because they do not know the right vocabulary. Some overestimate themselves because they use AI every day, but still mainly for one-off answers.
That is why the third version looks more at behavioral evidence: concrete outputs, repeated workflows, saved prompts and templates, assistants, work processes, verification, iteration, and changes in the way work is done.
We do not only ask: "How often do you use AI?"
We ask: "What changed in how you work because of AI?"
#3. We distinguish AI use from work change more precisely
Frequent AI use is a good signal. But it is not the same as high AI maturity.
The difference is in what remains after AI is used. A one-off answer helps with one task. A good prompt helps again. A template, assistant, or workflow helps repeatedly. A changed working system changes the whole agenda.
The new version therefore distinguishes more clearly whether someone is trying AI, using it as a normal part of work, building personal workflows and tools, or changing how important work is done because of AI.
Important: advanced work with AI is not only for managers. An individual contributor can be very advanced if AI changed their key agenda.
#What changes in recommendations
A good recommendation should not sound like generic advice from a course.
It should not say: "Use AI more."
It should show one concrete next step in a person's real work.
For example:
Take five emails that worked well. Let AI find the rules of your style. From those rules, create the first version of an assistant that drafts future emails for you.
Or:
Take your last three project meetings. Let AI extract repeated decisions, open questions, and bottlenecks. Turn that into a project context pack you use next time.
The new recommendations therefore follow a simple rule: they should connect to a person's real work, let AI do the first analysis, draft, or extraction, and leave the decision, quality control, and next action to the human.
We also expanded the development map. The methodology now works with 68 practical methods. It also covers AI adoption in teams more strongly: how to recognize whether people really use AI at work, how to run small experiments, how to build shared context, how to choose tools, and how to turn AI trends into decisions.
How coaching works explains how a result becomes concrete next steps.
#Why we are doing this
AI adoption is no longer just tool training.
If we give people a chatbot and ten prompts, we help them start. But we do not learn who has really changed their work with AI, who has good one-off results but lacks a system, who builds workflows others can reuse, where a team lacks context, tools, or rules, or which next step makes sense for a specific person.
The new version is not a harsher assessment. It is a more precise map.
It helps see the difference between someone who only occasionally tries AI, someone who uses it every day, someone who builds repeatable workflows from it, and someone who changes how work is done with it.
#What stays the same
The core belief does not change:
AI is not just another app next to Excel or Outlook. It is a new way of collaborating.
The biggest difference is not made by the tool itself. The difference is made by how a person thinks, how they give context, how they stay in the conversation, how they verify outputs, how they save what works, and how they turn a one-off win into a repeatable workflow.
The new version helps us name these differences more precisely and turn them into better recommendations.
#FAQ
#My score changed. What should I do with that?
Treat it as a new baseline according to a more precise methodology.
Old and new scores may not always match one to one. More important than the difference itself is what level the result shows, what evidence it is based on, which opportunities it recommends, and what your next step should be.
#My score is lower than before. Does that mean I got worse?
Not necessarily.
Often it means the new version more precisely distinguishes between frequent AI use and repeatable work change.
If you use AI a lot but mainly ad hoc, the new score may be more sober. It is not a punishment. It is a signal that the next shift is not "use AI more", but turn what works into a workflow, template, assistant, or repeatable process.
#Why can someone have a lower score even if they use AI every day?
Because frequency is not everything.
Daily AI use is a strong foundation. But the methodology also looks at whether someone iterates, verifies, saves workflows, creates repeatable processes, and changes how they work.
AI used every day for one-off answers is a different type of maturity than AI that helps manage a repeated agenda.
#Why can someone have a higher score even if they know fewer tools?
Because tools are not the main result.
A person who knows fewer tools but uses AI thoughtfully, gives it good context, verifies outputs, and changes their workflows can be further ahead than someone who knows ten apps but uses them superficially.
#My profile changed. Is that a mistake?
It does not have to be.
Profile is not a fixed identity. It describes how you most often create value with AI today.
If you move from writing texts to building workflows, the result can shift. If you start using AI more for decisions, your dominant style may change. The goal is not to keep the same label, but to better name the current way of working.
#What if I disagree with the result?
Look at the evidence.
The result depends on which concrete examples and signals were visible in the assessment. If important things were not mentioned, the result may be more cautious.
Concrete examples help: what you created with AI, which workflow you repeat, what you changed, what worked, what did not work, and how you adjusted it.
#What should I do if I want to move my score?
Start with the opportunities recommended in your report.
Choose one or two most important opportunities and start working on them practically. Not for points, but for real movement in work.
A good first step:
- Choose one recommended opportunity.
- Find a concrete task where it appears every week.
- Let AI propose a better workflow.
- Save what works as a template or repeatable process.
- Adjust the process after the first use.
Progress usually does not start with another tool. It starts when one good experience becomes something that also works next time.