The copilot went in. The team feels heavier.
You can see it in your own numbers: the AI went in, the dashboards barely moved, and somehow everyone is more tired. That's not resistance. The work didn't shrink. It moved into checking, correcting, re-prompting, and coordinating around the machine, and your metrics can't separate "doing the work" from "supervising the AI." We call this the old container problem: new capability poured into old workflows, old approval chains, old assumptions about what people can absorb. The tools are fine. The container is wrong.
The feelings that carry over
Hard conversations compound. People bring yesterday into today, and no dashboard shows it.
The cost of the stack
Tool sprawl, pings, twelve open tabs. The mental cost of the tooling is real. It's just not on any report.
The full-head problem
When working memory maxes out, decision quality drops and errors climb. Most organizations only notice the output.
The AI Capacity Audit
A few weeks. Your data, decomposed. A map of where capacity leaks and what to redesign first.
We start from the operational data you already have: handle time, after-call work, quality scores, adoption numbers. Then we add the part your dashboards can't do: confidential interviews with your team, a short survey, and watching how the work actually flows on masked historical cases. Never live customer screens. Never individual scoring.
What you get back
A written report of about ten pages plus a 90-minute readout with your leadership team. Inside it:
Where the capacity drains
Every finding graded by how confident we are in it, and every number carrying its source. If a claim doesn't reconcile with your own systems, we flag it, and that mismatch is itself a finding.
Where AI helps vs. where it burdens
Which workflows the AI genuinely lightens, and which ones it quietly turned into supervision jobs.
What to fix first
The two or three highest-leverage changes, ranked. At least one you can act on without spending another dollar with us.
A plan your team can run
A 30/60/90-day roadmap where every item has a named owner and an honest hours estimate.
The investment: $7,500 to $15,000 depending on team size and scope, set on the free call. Your team's time, counted honestly: roughly 16 to 24 person-hours across four weeks. We schedule interviews off-peak and count your people's time because that's the kind of company this is.
Three things that make this different
We start from your numbers
Not our slideware. Every quantitative claim in the report reconciles against your own systems or gets flagged as inconsistent with them.
The report can say "don't hire us"
The proposal states our working theory and what the data would have to show to prove it wrong. If the strain isn't coming from the AI rollout, the readout says so and points you at the right next move, which may not be us.
We don't score people. Structurally.
Aggregate only, opt-in, names stripped. No theme gets reported that could identify fewer than three people or a single shift. The audit reads the work, not the workers.