Verdigris • sep - dec 2025 • in alpha
from chatbot to proactive ai assistant
i was hired to usability test an ai chatbot for data center technicians. i recommended not shipping it — and led the pivot to arc, a proactive assistant now in alpha.
sole designer (research → strategy → design) · 9 usability sessions + 2 contextual inquiries · team: head of product, engineering, 3 research collaborators
02 — context
verdigris catches equipment failure in data centers before it happens.
they built a chatbot to help technicians understand the data, and asked us to test one thing: is this the right product?
"a lot of data center technicians don't know what to do with the data itself. they don't know how to analyze it. they just want the building to run efficiently."
jimit shah, head of product @ verdigris
03 — the test
i set five ship criteria and tested against them.
the chatbot assumed technicians would notice an issue, open a tool, ask the right question, and judge the answer — all on them.
04 — what i found
0/5
criteria met — recommendation: do not ship
glanceable
scan severity in under 10 seconds
✕ failed
8/9 had to read first
actionable
leads to a clear next step
✕ failed
8/9 wanted what to do
trustworthy
shows source and confidence
✕ failed
9/9 asked where it came from
field-ready
works on mobile and tablet in the field
✕ failed
desktop-bound
recoverable
verify, escalate, or hand off when unsure
✕ failed
no path
the root cause: the chatbot put the work on the technician.
the ai should do the opposite — detect the issue, surface the context, explain the risk, recommend the next step, and show its reasoning. that reframed the question from "how do we make the chatbot easier to use?" to "how do we bring ai into the workflow at the moment it's needed?"
05 — making the case
i grounded the pivot in evidence, not preference.
i showed leadership that a passive chatbot was a business risk — alert fatigue and slower resolution — and used the ship criteria to keep the call objective, not a matter of taste. in the final review, i walked the cto through the mismatch between a blank prompt and a technician's real-time needs.
what changed in the room
from
"can we build a bot?"
to
"how do we design trust?"
06 — the pivot: arc
same ai. new surface. arc starts with the problem, not a prompt.
old — chatbot (all on the user)
notice the issue → know which data matters → open a separate tool → ask the right question → judge the answer → decide what to do
new — arc (system-initiated)
system detects anomaly → push notification → tap → alert view (cause · severity · source · confidence · next step) → act, escalate, or monitor
push notification entry
fixes: blank prompt
alert-first detail view
fixes: not glanceable / no next step
confidence + source data
fixes: no trust
recommended next steps
fixes: no action
persistent handoff context
fixes: outside the workflow
safe by design: severity thresholds control alert fatigue; the dashboard stays as a manual fallback; language is "recommended," never "do this." the technician always decides.
07 — impact
verdigris accepted the recommendation and moved arc into alpha.
my most valuable contribution was the call not to ship — turning usability findings into product criteria changed the product's direction, not just its interface.
08 — reflection
not every ai product should be a chatbot. chat works when users know what to ask. here they didn't — so the answer wasn't a smarter chatbot, but an assistant that notices, contextualizes, and guides.