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7 Places Your Agency's AI Implementation Is Broken
How to use this diagnostic:
Work through each section in order. Check the box next to every item that is currently true for your agency. Sections where you can't check most items are your priority to fix before you buy anything new or scale anything that's already running.
Section 1 of 7
The Process Documentation Test
Can your AI actually follow your workflow, or is the map still in your head?
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You have written, step-by-step documentation for every workflow you are trying to automate, not just a description of the outcome you want.
Any team member, not just the person who built the workflow, could follow your documentation and produce the same output.
Your documentation defines what good looks like at each step, including what to do and how to know when it's been done correctly.
Your workflow documentation has been reviewed or updated in the last 90 days.
Section 2 of 7
The Handoff Audit
Where ownership changes hands is where work falls apart. AI amplifies bad handoffs. It doesn't fix them.
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You've mapped every point where ownership transfers in your top three workflows, including who hands off to whom and when.
Each handoff has a defined trigger, which is a clear signal that tells the next person or tool it's their turn to act.
The output format at each handoff is standardized so the receiving person or tool knows exactly what they're getting every time.
Your workflows survive a team member being out for a week. The handoffs are documented, not just understood by the people currently in those seats.
Section 3 of 7
The Adoption Gap Check
The tools you think your team is using and the tools they're actually using are not the same list.
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You can name without guessing which AI tools each member uses this week, and it matches what you expect.
When you ask, "what's slowing you down with [tool]?" you get specific, honest answers, not silence, vague complaints, or "it's fine."
No tool in your current stack has gone more than two weeks without intentional use by the person it was assigned to.
Section 4 of 7
The Output Quality Baseline
If you don't know what "good" looks like before the AI produces it, you're editing on instinct, not standards.
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You have a written definition of what a high-quality output looks like for each AI-assisted task established before you see the result, not after.
You've measured how long it actually takes to review and edit AI output versus completing the same task from scratch.
Your tracking revisions on rounds of AI-assisted work, and the numbers trending down over time, not holding steady,
When you do the math on AI output time plus editing time compared to manual time, the AI-assisted version is actually faster.
Section 5 of 7
The "Is This Really an AI Problem?" Test
Most AI failures aren't AI problems. They're process problems with a new label.
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If you remove the AI tool from this workflow tomorrow, the workflow would still run, just more slowly.
The person responsible for this task has a documented, clear definition of success before they start, not just a rough sense of what good looks like.
This task is executed the same way by every team member who handles it, not "whatever works for them" or "however they learned it".
There is one person who owns the outcome of this workflow, not a team, not leadership. One person.
Section 6 of 7
The Prompt Governance Check
If every person on your team writes their own prompts from scratch, you don't have an AI system. You have parallel experiments running without oversight.
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Your team uses shared documents and prompts stored somewhere everyone can access and update them, not buried in individual chat histories.
Your prompts include your brand voice guidelines and quality standards, not just the task instruction.
You can tell from the output alone whether the correct prompt was used to produce it.
When a prompt produces a bad result, there is a process for identifying why and updating the prompt, not just re-running it and hoping for something different.
Section 7 of 7
The Ownership Gap
"We all own AI" is not an accountability structure. It's how implementations quietly erode until someone notices the tool hasn't been used in two months.
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There is one named person accountable for the performance of your AI implementation. Not a committee, not the whole team. One person who is responsible.
The person has a defined, measurable metric they're responsible for, not just "make our AI work better".
Someone on your team has a scheduled reoccurring time block to review, tune, and improve your AI workflows. It's on the calendar, not just on the to-do list.
You have a documented escalation path for when an AI output misses the mark. Not just "tell someone on Slack and figure it out".
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