Use cases

Measured examples. Not perfect proof.

These are example cases to make the measurement method concrete. They are written like the kind of messy pilot notes I would actually want to see before scaling.

Copilot

Customer renewal meeting briefs

Account managers use Microsoft 365 Copilot to prepare renewal meeting briefs from Teams, Outlook, CRM notes, support tickets, and previous proposal documents.

Baseline55 min

With AI32 min

Volume180/mo

DecisionRetest

What improved

Briefs were faster to prepare and usually included more recent context from Teams and email. That is useful. I would not dismiss it.

What did not improve yet

Quality was not scored properly. Two briefs included outdated account information. So yes, the time case is promising, but the proof is not finished.

What I would measure next

Reviewer score, outdated-info incidents, follow-up quality after the meeting, and whether freed prep time turns into better customer actions.

Screenshot placeholder Meeting brief

Copilot Studio

HR policy question agent

An internal agent answers common HR questions about leave, remote work, expenses, and onboarding. Simple enough on paper. In practice, policy ownership matters a lot.

Eligible chats1,200/mo

Resolved43%

Escalation31%

DecisionChange

What improved

The agent reduced repeated HR questions and helped people find basic policy answers outside office hours.

What got exposed

Some policies were unclear, duplicated, or stored in the wrong place. That is not the agent failing exactly. It is the agent showing the mess.

What I would measure next

Deflection with quality checks, repeat contact rate, missing knowledge articles, and how often HR has to correct the answer.

Analytics placeholder 43% resolved

Power Platform

Invoice triage with AI Builder

A finance team uses AI Builder and Power Automate to classify incoming invoices, extract key fields, and route exceptions to the right person.

Baseline9 min

With AI4 min

Accuracy88%

DecisionScale small

What improved

Routine invoices moved faster, and fewer people had to touch the easy cases. Good. This is exactly where AI plus automation can be boring and valuable.

Where I would be careful

Exception handling matters. Vendor changes, missing PO numbers, and low-confidence extraction can quickly move work back to humans.

What I would measure next

Cost per accepted invoice, exception rate, correction time, payment delay reduction, and whether the model performs differently by vendor type.

Workflow placeholder Invoice route

Microsoft 365 Copilot

Project status updates from meeting notes

Project leads use Copilot to turn Teams meetings, planner notes, and open action lists into a weekly status update for stakeholders.

Baseline70 min

With AI38 min

Review tax14 min

DecisionScale with rules

What improved

The first draft became much faster. It also caught action items that were easy to miss when people wrote updates from memory.

Where I would not overclaim

The update was only useful when the meeting notes were decent and the project had clear milestones. Weak project hygiene made the AI output look vague.

What I would measure next

Stakeholder correction rate, missed action items, time spent reviewing the draft, and whether decisions happen faster because status is clearer.

Status placeholder Project update

Case study standards

Real evidence needs permission first.

These examples are public teaching material. Real case studies should only use approved, sanitized, permission-cleared evidence. No customer data, internal screenshots, personal data, or sensitive operational details belong here without a clear yes.

Safe evidence

Aggregated numbers, recreated screenshots, synthetic examples, public references, or written summaries that have been checked before publishing.

Useful numbers

Task volume, baseline time, AI-assisted time including review, quality score, escalation rate, exception rate, monthly cost, and scale decision.

Still honest

Where the method was weak, what was uncertain, what people disagreed about, and what should be measured differently next time.