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Public case library

See how messy work becomes a useful first step.

These are example cases, not real client stories. Real public cases should only appear after permission and anonymization.

Reporting

Weekly status chaos

Example case

Problem

Nobody knows which version is the latest. Managers spend too much time chasing updates and rewriting summaries.

Root cause

The input is not standardized before the reporting work starts.

Process sketch

  1. 1 Team members submit updates in one format
  2. 2 Missing fields are flagged
  3. 3 AI drafts summary
  4. 4 Manager reviews and edits
  5. 5 Final report is sent

AI opportunity

AI can summarize updates, identify missing information and draft the first version.

Human approval

The manager approves the final wording and decisions before anything is sent.

Risk

If input remains messy, AI will just create faster-looking mess.

First fix

Create a fixed weekly update template before introducing automation.

Inbox / email

Customer inbox ping-pong

Example case

Problem

Ownership is unclear, replies are inconsistent, and urgent requests can sit too long.

Root cause

No triage logic exists before the work is distributed.

Process sketch

  1. 1 Request enters shared inbox
  2. 2 Request type is classified
  3. 3 Urgency and owner are suggested
  4. 4 Human confirms assignment
  5. 5 Reply draft is prepared
  6. 6 Status is tracked

AI opportunity

AI can classify request types, extract key details and draft first replies.

Human approval

A person approves classification and sends final reply.

Risk

Fully automated replies can damage trust if the case is sensitive.

First fix

Define 5 to 8 common request categories and ownership rules.

Supplier/customer flow

Supplier follow-up black hole

Example case

Problem

Follow-ups depend on individuals remembering what is missing.

Root cause

There is no single status structure or exception list.

Process sketch

  1. 1 Supplier status is captured in one tracker
  2. 2 Missing confirmations are flagged
  3. 3 AI drafts follow-up emails
  4. 4 Responsible person approves
  5. 5 Escalations are visible

AI opportunity

AI can detect missing responses, draft nudges and create an exception summary.

Human approval

Procurement or operations approves follow-ups before sending.

Risk

Bad data in the tracker creates false urgency.

First fix

Define the minimum fields needed to know whether a supplier case is healthy.

AI idea

AI idea without owner

Example case

Problem

Ideas stay abstract because nobody defines pain, frequency, risk or owner.

Root cause

The company is evaluating tools before evaluating workflows.

Process sketch

  1. 1 Collect AI ideas
  2. 2 Map each idea to workflow pain
  3. 3 Score by frequency, value, risk and readiness
  4. 4 Pick one pilot
  5. 5 Measure before and after

AI opportunity

AI can help structure and compare ideas, but prioritization must be business-led.

Human approval

Leadership chooses the pilot based on business value and risk.

Risk

AI theatre: many demos, no changed workflow.

First fix

Create a one-page AI use case scorecard.

Meeting notes

Meeting actions drift

Example case

Problem

Follow-up depends on memory, and the next meeting starts by reconstructing the last one.

Root cause

The meeting has no standard output format or owner review step.

Process sketch

  1. 1 Meeting notes are captured
  2. 2 Actions, owners and decisions are extracted
  3. 3 AI drafts a follow-up summary
  4. 4 Meeting owner approves
  5. 5 Actions are added to the team tracker

AI opportunity

AI can extract actions, open questions and decision summaries from notes.

Human approval

The meeting owner confirms owners, deadlines and decision wording.

Risk

Badly captured notes can create confident but wrong action lists.

First fix

Agree one meeting output template before adding AI note processing.

Handover

Handover context loss

Example case

Problem

The receiving team has to ask for the same context again before they can move.

Root cause

There is no shared definition of a complete handover.

Process sketch

  1. 1 Source notes and CRM context are collected
  2. 2 Missing fields are flagged
  3. 3 AI drafts a handover brief
  4. 4 Sender approves sensitive context
  5. 5 Receiver gets a clean next-action view

AI opportunity

AI can compile scattered context into a structured handover brief.

Human approval

The sender reviews what is included and removes anything sensitive.

Risk

A long summary can look useful while hiding the missing decision.

First fix

Define the five fields every handover must contain.

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