Free workflow reviews for teams drowning in manual work.
SiteDokAILab is a free process lab for companies stuck in recurring manual work, messy handovers, repeated status updates, spreadsheet chaos, email ping-pong or AI ideas that have not become useful yet.
Free while I am looking for my next role. No sales pitch. No AI hype. Just practical workflow thinking.
Live process lab
Messy inputs become a practical mini-review, not a 58-slide ritual.
Messy input
SiteDokAILab
Mini-review output
No magic AI dust. Start with the workflow.
No invented case claims. Examples stay labelled.
No blind automation. People approve sensitive output.
Workflow selector
This is a quick way to translate fuzzy process pain into a useful first review angle.
Reporting
Updates live across email, Teams, meetings and someone who remembers the latest number.
I would look for missing input standards, unclear owners and where the summary work really starts.
AI can draft summaries, compare updates and flag missing fields when the input is structured enough.
Create one weekly update template before adding automation.
Inbox
Requests arrive in a shared inbox and bounce between people until ownership becomes a mood.
I would map request types, urgency signals, routing rules and the approval point before any reply is sent.
AI can classify requests, extract context and draft replies for a person to approve.
Define 5 to 8 request categories and who owns each one.
Handover
Important context sits in calls, notes, memory and half-finished documents.
I would find the minimum context needed by the next person and where handovers currently lose it.
AI can turn notes into handover briefs, action lists and open-risk summaries.
Write the handover checklist the receiver actually needs.
CRM
Follow-ups disappear because CRM notes are inconsistent, stale or written for the person who already knows the story.
I would inspect ownership, next action quality, missing dates and repeated follow-up patterns.
AI can summarize account context, suggest next actions and flag stale opportunities.
Standardize what a good next action must contain.
Supplier flow
Supplier status is tracked through spreadsheets, email threads and heroic memory.
I would separate normal status from exceptions, missing confirmations and escalation rules.
AI can draft nudges, summarize exceptions and create a clean daily follow-up list.
Define the minimum fields that make a supplier case healthy.
AI idea
The company has many AI ideas, but nobody has tied them to pain, risk, frequency or ownership.
I would score ideas against real workflows before evaluating tools or shiny demos.
AI can help structure and compare ideas, but prioritization must stay business-led.
Create a one-page AI use case scorecard.
Updates live across emails, Teams and memory.
Why it matters: Managers spend time chasing the latest version instead of making decisions.
I would look for: Missing input format, owner gaps and whether AI can summarize or flag missing fields.
Shared inboxes create unclear ownership and uneven replies.
Why it matters: Urgent requests can wait too long, while simple ones get handled twice.
I would look for: Request categories, routing rules, tone standards and human approval before sending.
People rely on memory to know what is missing.
Why it matters: Late confirmations become fire drills instead of visible exceptions.
I would look for: A clean status structure, missing fields and safe follow-up drafts.
Actions are captured differently every time.
Why it matters: Follow-up depends on who took notes and how much context survived.
I would look for: Decision points, owner/date logic and reusable action summaries.
Numbers move from system to sheet to report by hand.
Why it matters: Copy-paste creates errors and burns attention on low-value work.
I would look for: Source of truth, validation points and where AI should only assist.
Next steps are vague, missing or written after the fact.
Why it matters: Sales and delivery lose momentum when the record does not guide action.
I would look for: Next-action quality, stale records and suggested follow-up structure.
The receiving team gets fragments instead of usable context.
Why it matters: Work slows down when every transfer requires another meeting.
I would look for: Minimum handover fields, risks, open decisions and ownership.
Ideas sound interesting, but nobody knows the first useful pilot.
Why it matters: Tool-first AI work becomes theatre when it is not tied to a repeated task.
I would look for: Frequency, value, risk, readiness and the human approval point.
What the real problem seems to be.
The simple version of the process before tools get involved.
Where AI could help, and where it should not.
What could break, confuse people or create bad automation.
One practical move the company can make without buying a tool.
Why this exists
I am looking for my next role in AI implementation, operations transformation or workflow automation. While I am doing that, I am using SiteDokAILab as a live process lab: companies send in real workflow problems, and I turn them into practical mini-reviews.
It keeps me sharp. It helps companies see what AI could actually do. And it gives hiring teams a much better view of how I think than a normal CV ever could.
No sales pitch. No magic AI dust. Just a practical look at where the process breaks, where AI might help, and what the first sensible step could be.
How it works
If your process contains sensitive data, remove it before submitting. I do not need customer names, employee names, prices or confidential documents to spot workflow friction.
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Case library
Reporting
Problem: Nobody knows which version is the latest. Managers spend too much time chasing updates and rewriting summaries.
AI opportunity: AI can summarize updates, identify missing information and draft the first version.
First fix: Create a fixed weekly update template before introducing automation.
Inbox / email
Problem: Ownership is unclear, replies are inconsistent, and urgent requests can sit too long.
AI opportunity: AI can classify request types, extract key details and draft first replies.
First fix: Define 5 to 8 common request categories and ownership rules.
Supplier/customer flow
Problem: Follow-ups depend on individuals remembering what is missing.
AI opportunity: AI can detect missing responses, draft nudges and create an exception summary.
First fix: Define the minimum fields needed to know whether a supplier case is healthy.
AI idea
Problem: Ideas stay abstract because nobody defines pain, frequency, risk or owner.
AI opportunity: AI can help structure and compare ideas, but prioritization must be business-led.
First fix: Create a one-page AI use case scorecard.
Quick heuristic
Not scientific. Useful as a first smell test before anyone buys another tool or calls it a strategy.
AI fit
Medium
Assist, do not automate
AI may help draft, classify or summarize, but the workflow needs human approval and cleaner input first.
First recommendation: map the workflow and define the approval point before building.
About / hire
I am Troels Østbjerg, an operations leader turned AI implementation builder. I have spent 10+ years inside real operational complexity, from FMCG scale-up work to global CMO coordination, and now I use AI to build faster, clearer workflows.