Compare project velocity across months of meeting notes
Ask Copilot to scan a notebook's meeting history and identify recurring issues and pace changes over time.
Build a Copilot Studio agent that analyzes recurring incident tickets to identify the underlying cause and recommend a proactive fix instead of repeated firefighting.
Fewer repeat incidents of the same type because the underlying cause gets fixed instead of just the symptom.
The apps run in this order.
Export the ticket history into Excel as a Table and use a PivotTable to group by symptom, affected system, and time-of-day/week — you're looking for a cluster, not a single pattern-matching keyword.
Confirm there's an actual recurring shape (same symptom, similar timing) before handing it to the agent; feeding it a handful of unrelated tickets just because they share a vague symptom produces a hypothesis built on noise.
In Copilot Studio, feed the agent the grouped ticket history — descriptions, timestamps, affected systems, resolution notes — and have it propose a root-cause hypothesis with the specific evidence from the tickets that supports it.
Prompt idea:
These 14 tickets over the past 6 weeks all report the same VPN disconnect symptom, clustering around 9am each weekday. Based on the ticket details and resolution notes, what's the most likely root cause, and what evidence points to it?
Have the agent propose a fix (a config change, a capacity increase, a patch) for an engineer to evaluate and implement — this agent's job is diagnosis, not making infrastructure changes unsupervised.
Set a calendar reminder for 2-3 weeks after the fix ships (don't rely on remembering) and re-pull the same ticket grouping from step 1.
If the pattern is genuinely gone, log the fix and its evidence as a confirmed case; if it's still occurring at a lower rate, that's a different finding than "resolved" and should be logged as such.
A written root-cause hypothesis for each recurring incident pattern, with supporting evidence and a recommended fix, ready for engineering review.
Grouping tickets by symptom and timing (step 1) is a pivot table. Turning resolution notes and timestamps across 14 tickets into a plausible root-cause hypothesis is causal reasoning over text — exactly where a fixed rule falls short.
Ask Copilot to scan a notebook's meeting history and identify recurring issues and pace changes over time.
Stand up a Copilot Studio agent that opens and categorizes IT tickets, runs first-line diagnostics, and resets passwords through Entra without a technician.
You inherited a CRM export filled with duplicate entries, inconsistent formatting, and missing fields.
Workflows like this tend to raise real governance and licensing questions once more than one person is using them — that's exactly what we help with.