A new COO inherits two problems at once: the visible operating mess, and the invisible mess hiding inside everyone's AI tabs.
By the time you arrive, the company usually already "uses AI." Someone has prompt docs. Someone built a clever workflow. Someone else has a private ChatGPT ritual they swear by. None of that means the business has a dependable operating layer.
The first 90 days are when that either gets cleaned up or calcifies. This audit is meant to help you spot the workflows where scattered AI usage is quietly turning into coordination debt.
What this audit covers
Why this matters in the first 90 days
A lot of AI drift looks harmless at first. It feels like initiative. People are experimenting. A few outputs are faster. But early operator drift becomes a systems problem fast.
- One team moves fast with AI, another stalls
- Reporting quality depends on who touched it last
- Follow-up and task hygiene stay inconsistent
- Context is trapped in prompts, not systems
- Useful workflows are not documented or shared
- Execution still depends on a few careful humans
Your job is not to ban AI improvisation. Your job is to decide where improvisation is fine and where the business needs a stable, operator-owned workflow instead.
The 6 checks to run before prompt chaos spreads
1. Executive brief production
Question: When the CEO or leadership team needs a memo, update, or recommendation, does the team start from a blank page every time?
- Source notes live across Slack, docs, and inboxes
- Format depends on the operator writing it
- Context gets rebuilt manually each cycle
- Inputs are clear
- Risks and next steps are structured the same way every time
- AI accelerates drafting without removing judgment
- Better leadership prep
- Less night-before cleanup work
- More reusable institutional thinking
2. Meeting follow-through
Question: After leadership meetings, does the business reliably leave with tasks, owners, context, and due dates?
If action items still depend on someone translating notes into work later, you have a follow-through gap, not a meeting-notes gap.
- Weak pattern: summaries exist, but the real work of assigning and clarifying still happens afterward
- Stronger pattern: decisions become task packets with enough context for downstream execution
- Best pattern: the handoff from meeting to work is boringly reliable
3. Intake and triage
Question: When requests arrive through inboxes, Slack, forms, meetings, or client channels, who cleans them up and decides what they mean?
A lot of operator drag comes from turning messy asks into structured work. AI can help here, but only if the categories, routing logic, and ownership rules are explicit.
- Weak pattern: requests disappear into channel noise
- Stronger pattern: common request types are tagged and routed consistently
- Best pattern: nothing important depends on heroic inbox vigilance
4. Recurring reporting
Question: Does the company rebuild status updates, dashboards, client summaries, or board materials from scratch every week?
Recurring reporting is one of the cleanest first workflows to fix because the output format is known, the pain is frequent, and the wasted time is obvious.
- Weak pattern: numbers get recopied and commentary gets rewritten every cycle
- Stronger pattern: data collection is stable, but narrative synthesis is still human-heavy
- Best pattern: the first draft arrives already shaped for review
5. Knowledge retrieval
Question: Can people find the current answer without waiting for the one operator who knows where everything lives?
If every recurring question starts a scavenger hunt, the company is paying a retrieval tax. AI only helps if the underlying knowledge is current, reachable, and tied to real operating use.
- Weak pattern: tribal memory wins
- Stronger pattern: docs exist, but people do not trust them enough to act
- Best pattern: current knowledge is easy to retrieve and easy to use
6. Ownership and governance
Question: Does anyone actually own the company's practical AI operating layer?
Not the policy deck. Not the innovation committee. The real thing: approved workflows, shared context, tool boundaries, and what counts as acceptable use.
- Weak pattern: every team experiments in isolation
- Stronger pattern: informal norms exist, but nobody maintains them
- Best pattern: the company knows which workflows are standardized, who owns them, and where the handoff lives
How to score what you find
Score each of the six checks from 1 to 5.
- 1 = manual, inconsistent, or trapped in people's heads
- 3 = partly standardized, but still operator-heavy and fragile
- 5 = dependable, visible, and usable by more than one person
You do not need more experimentation. You need one stable operating workflow installed fast.
There is enough proof that AI helps. The next move is standardizing one painful recurring workflow end to end.
The business is not starting from zero. Now the leverage is in cleaner handoffs, shared context, and ownership.
What to install first
Do not chase total transformation in your first quarter. Choose one workflow with all three traits:
- it happens every week
- it burns operator time
- the output shape is easy to define
- Pick one workflow. Good first candidates: recurring reporting, executive briefs, meeting follow-through, or intake triage.
- Map the inputs. Name exactly where source material comes from and what currently gets lost.
- Define the output. What should exist at the end: a memo, a task packet, a report draft, a routed request?
- Set ownership. Decide who maintains the workflow after the initial build.
- Install it on your stack. The goal is not novelty. The goal is a system the company can keep using without vendor dependency.
That is usually the first real win for a new COO: not proving the company is innovative, but making one recurring process calmer, faster, and easier to trust.