Here’s the situation I was trying to fix: a capable coworker was exhausted by tasks that looked easy on paper. They were not failing. They were spending too much time in work that required a kind of effort they did not naturally sustain. The working genius framework offers a useful way to examine that mismatch. It describes six forms of contribution: wonder, invention, discernment, galvanizing, enablement, and tenacity. I do not treat working genius as a permanent label or a scientific diagnosis. I treat it as a practical language for discussing energy, friction, and team roles.
That distinction matters. A framework can help you ask better questions, but it cannot excuse poor communication or replace performance evidence. The useful question is not, “Which box am I?” It is, “Which parts of this workflow give me energy, and where does the work repeatedly stall?”
What working genius is designed to explain
The model, associated with Patrick Lencioni, focuses on the types of work a person finds naturally energizing. Wonder involves asking what could be improved or what problem deserves attention. Invention generates possible solutions. Discernment evaluates ideas using judgment and experience. Galvanizing creates movement and commitment. Enablement supports people as they execute. Tenacity pushes important work across the finish line.
Most people can perform all six activities. The difference is often how much energy each activity requires and how consistently someone produces strong results there. A data analyst might enjoy investigating an unclear business question but dislike persuading five departments to adopt the answer. A project manager might be excellent at creating momentum yet need a partner to challenge assumptions before launch.
This is where working genius becomes more useful than a generic personality quiz. It connects a preference to a real work sequence. A weekly report, for example, may require wonder to identify the meaningful question, invention to design an analysis, discernment to check the result, galvanizing to explain the decision, enablement to help others act, and tenacity to track completion.
Use the framework on a real workflow
Do not begin by taking a quiz and reorganizing your career around the result. Start with one recurring task that occupies at least two hours each week. Meeting follow-up is a good example because it includes interpretation, writing, coordination, and persistence.
First, capture the actual workflow. After a meeting, someone must identify decisions, separate commitments from discussion, assign owners, write a clear update, and follow up when deadlines slip. Then mark the moments that feel energizing, neutral, or draining. A person who enjoys finding the central decision may have strength in discernment. Someone who likes getting three owners to respond may lean toward galvanizing or tenacity.
Next, compare your experience with the quality of the outcome. Energy alone is not enough. If you enjoy generating ideas but routinely miss constraints, invention needs a discernment checkpoint. If you are strong at support but never clarify ownership, enablement needs a stronger handoff. The point is not to protect a preferred activity. It is to design a workflow where judgment and accountability remain visible.
I have found that this small exercise takes about 20 minutes and produces more useful insight than broad career speculation. It also creates a concrete starting point for an AI experiment. An assistant can extract decisions from a transcript, but a human still decides whether the decision is accurate, politically realistic, and ready to share.
Working genius and AI-assisted work
AI can change the balance of a workflow, but it does not remove the need to understand your contribution. For example, a language model can produce five possible structures for a customer update. That supports invention. It can also compare a draft against a supplied rubric, which may support discernment. Yet the model does not own the business context, know which stakeholder will resist, or decide whether a promised date is credible.
A practical test is to choose one task and separate assistance from judgment. Use AI to summarize a 45-minute meeting, propose action-item wording, or turn rough notes into a first draft. Spend your saved time checking names, commitments, dependencies, and tone. Measure three things: minutes saved, corrections required, and whether the final work is clearer to the recipient.
This is a better use of working genius than asking AI to automate everything. If you are energized by discernment, put your attention into the review standard. If you are energized by galvanizing, use the draft to make the next action obvious. If tenacity is difficult for you, create a reminder and ownership system rather than pretending a polished summary equals completed work.
Privacy matters too. Do not paste confidential customer information, employee records, unreleased financial details, or proprietary strategy into a consumer tool without approval. A fast workflow that creates a data problem has not earned a permanent place in your system.
Turn insight into a stronger role
Once you see a repeated pattern, discuss the work rather than announcing a label. You might tell a manager, “I do my best work when I clarify ambiguous reporting questions and test the analysis. I would like to own that part of the monthly process, while partnering with someone who enjoys stakeholder rollout.” That statement is specific, testable, and less awkward than declaring a fixed type.
Working genius can also reveal a team gap. A group full of idea generators may produce impressive planning documents without a launch plan. A team with strong execution may keep improving an outdated process because nobody creates space to question it. The solution is not always hiring. It may be a 30-minute review that assigns a question owner, a decision owner, a communication owner, and a follow-up owner.
Keep a simple record for four weeks. Write down the task, your energy before and after, the quality of the result, and the help you needed. Look for repeated evidence, not one difficult Tuesday. Family obligations, deadlines, unclear leadership, and poor tooling can make any work feel draining. Context belongs in the analysis.
Build a side-business option carefully
This framework can inform an offer, but it should not become a shortcut to calling yourself a consultant. Start with a capability you have used successfully in a real job. If you repeatedly turn messy meeting notes into decisions and accountable follow-up, a small service might help a team improve its operating rhythm. If you evaluate dashboards and explain what leaders should do next, a reporting review could be more credible than a vague “AI productivity” package.
Document the workflow first. Record the starting problem, setup time, review steps, maintenance burden, and visible result. A productized service might include one process audit, a revised template, a 60-minute handoff, and two weeks of follow-up. Prices depend on scope and experience, but a tightly defined pilot is easier to assess than an open-ended promise. Do not claim guaranteed savings or immediate income. One useful client result is evidence; it is not a business model yet.
Working genius is helpful here because it can show which part of delivery you want to own and which part needs a collaborator. An inventive offer still needs discernment, communication, and tenacity. Better work first. More options next.
A one-week experiment
On Monday, choose one recurring workflow and write its six stages in plain language. On Tuesday, mark where you feel focused and where you procrastinate. On Wednesday, test AI on one low-risk step, such as organizing notes or suggesting a draft. On Thursday, review every output against a short quality checklist. On Friday, record time saved, errors found, and the next improvement.
Then decide whether the workflow deserves another week. If it created more maintenance than value, remove it. If it improved clarity and left human judgment in the right place, refine it. That is the practical promise of working genius: not a fixed identity, but a better conversation about how useful work gets done. Make the workflow earn its place.
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