Flowcharts show the happy path. The detours, escalations and “wait, that’s not right” moments show how real work gets done.
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Flowcharts show the happy path. The detours, escalations and “wait, that’s not right” moments show how real work gets done.
Draw your company’s order-to-delivery process on a whiteboard and it fits in five tidy boxes. Request comes in, gets checked, gets approved, gets fulfilled, gets closed.
Now ask the person who does the work how many of yesterday’s cases followed that line. The answer usually involves a laugh.
Evidence was missing. The customer changed the order after approval. Two policies disagreed. A refund needed a manager’s sign-off. Those detours are where experienced people earn their salaries, and they are the moments current AI systems often struggle with.
THE SHORT VERSION
- Useful business process data for AI can capture branches: the condition, the decision and the outcome.
- A completion flag hides the difference between a good stop and a shortcut.
- Map the reason behind each decision first. Licensing, if it ever happens, comes after authority and rights are clear.
Why do the exceptions matter most?
A list of completed steps tells a system what happened. It rarely tells it why.
A collection limited to successful, routine completions can leave important exceptions underrepresented. Records of missing information, conflicting rules and appropriate escalation can help explain how the task changes when the routine path breaks. Their usefulness still depends on the task and how the examples are selected.
The straight line describes the intended process. The branches show how your team handles what actually happens. That is the shift worth making: from logging steps to recording decisions.
Name the decision behind each fork
Take an illustrative service workflow. A standard request goes straight through. An unusual one triggers a check against a different policy, or a request for a manager to approve an alternative.
A useful record connects four things: the condition that made the case unusual, the information available at that moment, the decision taken and the result.
Two practical rules help:
- If the approval happened in another system, such as email or a chat thread, note that dependency rather than pretending the record is complete.
- If the reasoning simply wasn’t written down, say so. Don’t reverse-engineer a confident story from a final status field.
Capture a branch, not a step: Condition, Decision, Outcome. Concept illustration, not measured performance or a completed transaction.
Keep routine cases as well. An archive containing only unusual incidents can misrepresent how often those incidents occur. Record how cases were selected, and match the mix to the capability being trained or evaluated.
A good stop is not a failure
Here is a subtle trap. A worker who pauses a case to request more information may have done the right thing. Another who closed a case in record time may have skipped a required check.
A simple “completed: yes or no” label scores those two backward. Outcome labels have to reflect what the task required.
Research benchmarks reflect this. OSWorld, for example, evaluates AI agents on tasks in real computer environments and checks what their actions accomplish, not just the text they produce. It’s a helpful reference for thinking about outcomes. Your company’s own workflow history would still need its own validation and rights review before any proposed AI use.
Keep the timeline straight
Time distorts process records in two ways.
First, a summary written after a case closes can reveal the answer to a decision that was made earlier, without that information. Second, a policy update can make a perfectly correct historical action look wrong, because the rule changed later.
Record which information was available and which guidance applied at the time. When you can’t reconstruct that context, narrow your claims. And when related cases share templates or answers, group them together before building any held-out test set, so the test can’t peek at the answers.
Who can authorize the use?
Workflow systems often hold customer content, confidential terms and third-party material. Running the system every day does not automatically give a company the right to grant outsiders AI training rights over it.
Start with a metadata inventory, meaning systems, categories and dates, with an authorized company contact. Define proposed categories and exclusions before anything is extracted. De-identification can reduce exposure, but distinctive details can still point to real people, so some material belongs outside the collection altogether.
Mapping a workflow branch. Four takeaways from the article; the text supplies the full context and conditions.
Turning a mapped process into a commercial option
For established companies with long, well-recorded operating histories, this kind of data may be worth assessing commercially. SourceX licenses approved business records directly from companies for AI training and evaluation. You sign with SourceX, not an AI lab, and SourceX manages the agreement, preparation and payout under signed terms.
SourceX advertises payouts of up to US$4 million. Treat that as an upper bound for the largest enterprise datasets, not a typical or guaranteed result. The core fit is a company that reached at least 50 full-time employees at its peak and has several years of operating history, and qualifying companies identify at least five systems. The first fit check is a few questions with no uploads, and nothing is delivered until the company signs.
Map one recurring exception. Pick one process with a common exception. Find where the reason for the decision is recorded, then trace the action and the outcome.
What should you map first?
Choose one process with a recurring exception. Find where the reason for the decision is written down, then follow the action through to its outcome.
You’ll come away with an inspectable candidate dataset, or with a clear view of where your records fall short. Either result pays off. Better decision records can support staff training, audits and handovers even if the company never licenses them.
So the next time someone uncaps a marker and draws those five tidy boxes, hand it back and ask for the branches too.
Once the workflow and its limits are clear, ask SourceX about company fit. A metadata description can start the conversation while the records remain with the business.
About the founder
Noah Loul is a four-time founder and the founder and CEO of SourceX, which licenses business data directly from companies for AI training and evaluation. He previously founded B2B Rocket, acquired by Blackpearl Group. His work focuses on building permission-based data infrastructure for AI.
