Fithian AI Proficiency Self-Assessment

I've heard about AI tools but haven't really used them, or I tried once and didn't stick with it.

I use AI occasionally — mostly ChatGPT or Copilot for simple tasks like drafting emails or summarizing text.

I use AI regularly across multiple tasks and have a sense of which tools work best for different situations.

AI is part of my daily workflow. I use multiple tools, understand their strengths and limitations, and I'm often the person others come to for help.

I'd want to see the data it used, but I'd take the recommendation seriously since AI can process more information than we can.

I'd be skeptical. AI can identify patterns but "cut marketing by 20%" involves business judgment AI doesn't have. I'd want to understand what it actually found.

I'd ask what prompt and data were used, whether the AI had context like seasonality or strategy shifts, and whether the "20%" is a correlation or a recommendation.

All of the above, plus I'd check whether the AI had our full picture — customer acquisition costs, brand timelines, competitive positioning. A model trained on cost data alone will always recommend cuts.

They search the internet for the best answer to your question.

They retrieve information from a database of verified facts.

They predict the most likely next words based on patterns learned from training data — they generate plausible text, not verified truth.

They understand your question the way a human would and reason through the answer logically.

"Write a project status update for Project Atlas. We're on track for the May deadline but we have a risk with the vendor timeline."

Adds context: VP cares about hitting May date and whether to escalate. Tone: direct, no fluff, 150 words max.

Same plus: "Do NOT downplay the vendor risk or offer generic reassurance — if it's a real risk, say so clearly."

All above plus: "If unsure whether to flag risk as high or medium, ask me rather than guessing. Structure so first sentence answers her two questions."

I'll try rephrasing my request, sometimes a few times, until I get something closer to what I want.

I tell the AI specifically what's wrong and what to change — "the tone is too formal" or "you missed the budget constraint."

I diagnose why the output missed: was the context insufficient? Was my goal ambiguous? Then I fix the input, not just retry.

I treat it as a signal about my instruction. I ask what I assumed the AI would know that I didn't say. Over time I've built a mental checklist so I get fewer misses upfront.

"I'm meeting with a manufacturing client about our AI consulting proposal. What tough questions might they ask?"

Adds: CFO at $30M company, skeptical about ROI, IT team pushback. "Generate 5 toughest questions and what concern is really behind each one."

Same plus: "Tell me what a bad answer sounds like. Do NOT generate softballs — I need the questions that would make me uncomfortable."

All above plus: "Give me the one question she won't ask out loud. Then roleplay as her — I'll practice responses and you push back in character."

I'd scan it for anything obviously wrong, but if the numbers look reasonable, I'd use it.

I'd check the specific numbers against known sources. AI can generate convincing but fabricated statistics.

I'd verify claims but also check what's missing. AI tends to omit inconvenient data or context. I'd ask: "What did you leave out?"

All above plus I'd run the output through a second model in a clean context window to audit logic independently.

It looked right and covered what I asked for, so I used it.

I read through it carefully and edited the parts that didn't sound right.

I checked key claims and reasoning — well-written and well-reasoned are two different things.

I evaluated on multiple levels: facts verifiable? Reasoning sound? What's missing? Would this hold up if someone pushed back?

Whether the summary is long enough to capture everything important.

Whether the main points are accurately represented.

Whether the AI omitted nuances, tensions, or minority viewpoints that matter for decisions.

What was omitted AND whether the framing introduces bias — presenting one interpretation as "the" summary when the document supports multiple readings.

Look at what other companies in our industry are doing and suggest similar things.

Map our workflows — where do we spend the most time, where do errors happen — then match bottlenecks to AI solutions.

Map workflows but also look deeper: data we're not using, knowledge locked in one person's head, quality gaps from inconsistent expertise.

Start by having AI interview me and my team to uncover tasks we might not think to mention. Then prioritize opportunities that create new capabilities, not just faster versions of what we do.

Occasionally, usually when doing something tedious and repetitive.

Regularly. I've identified several tasks where AI handles part of the work and I focus on what requires my judgment.

It's a constant lens — not just my tasks but team workflows, department handoffs, and client delivery. I've also identified things AI could do that we don't currently do at all.

What's the accuracy rate and what happens when the chatbot can't answer?

What problem are we actually solving? Does this address the real issue or just the cheapest-to-solve one?

Plus: are we optimizing for cost or customer experience? What happens to the team's institutional knowledge? How does it handle edge cases?

All above plus: what data does our team have in their heads that isn't in any system? What actually happened at similar companies after 6 months? If we cut staff and it doesn't work, can we recover?

Whether quality stays consistent and the team uses extra time productively.

Does the team still understand what's in the reports? Are juniors still learning the skills report-writing taught? Has the team's value proposition changed?

All above plus: skill atrophy, identity shift ("produce analysis" vs "review AI's analysis"), and Jevons Paradox — leadership will expect more reports, not fewer hours.

I focus mainly on whether it solves the immediate problem.

I think about obvious impacts — who it affects directly and clear risks.

I actively consider second-order effects: adjacent processes, changed incentives, skill atrophy, non-obvious stakeholders.

I think in feedback loops: what does this incentivize? What becomes invisible? How does it interact with other changes? I default to asking "and then what?" at least twice.

Cost — three tools instead of one coordinated purchase.

Data security and compliance — three different tools with potentially different policies.

Fragmentation. Each department optimizes locally while the organization gets harder to coordinate. Different tools, different assumptions, different definitions of "good."

All above plus: implicit dependencies nobody tracks, institutional knowledge split between humans and three AI configs, and "great results" probably measured differently across departments.

Save the prompt or conversation for reference.

Fithian AI Proficiency Self-Assessment

Where are you on your AI journey?