Consider a familiar month-end question: why is freight over budget?
Anaplan shows the variance. The analyst drills into the movement by entity and cost centre. Then the investigation moves elsewhere: an ERP extract, a supplier invoice, an email explaining an emergency shipment.
Someone finds the evidence, decides what it means and writes three sentences for the pack.
A seasoned analyst would have seen this one coming. Freight has been running hot since the second week, they already know which shipment did it, and the three sentences were half-written before anyone asked. Experience buys that, for the questions experience has met before. Nobody predicts every question a board pack provokes, and the ones that arrive unpredicted follow the same path: model, extract, invoice, email, morning gone.
The model did its job. Those three sentences still took the morning.
After eight years of Anaplan builds, the work around the model is where I would look for the next round of automation: getting data ready, explaining what comes out and preserving the knowledge needed to run the implementation.
Anaplan’s own AI belongs in that picture. Finance Analyst supports variance analysis; CoModeler helps builders create, refine and explain models. Anaplan Intelligence also describes governed connections to outside AI.
The gap is often the connection to your particular implementation: the invoice behind the movement, the change notice behind the failed load, the decision behind the entity exclusion. Those connections still need to be built and maintained.
The practical question is how much of the surrounding work your implementation can actually complete. Can it trace the freight movement to supporting transactions? Find the relevant invoice? Identify whether the explanation is documented or merely plausible? Put the evidence in front of the person responsible for signing off?
That’s a larger job than producing a convincing paragraph.
Start with the paragraph
Variance commentary is a useful first example because the finished output looks so small.
“Freight costs increased due to higher shipment volumes and supplier rate changes.”
It reads well. It could also have been written without looking at a single invoice.
Automation worth having has to do the investigation behind the sentence. It needs the correct model version and period, the transactions behind the movement, and evidence supporting the proposed explanation.
An invoice can establish a charge. It may say nothing about why the business incurred it. That explanation might be in an operational note, or it might still require a conversation.
A good workflow makes that distinction visible. It could prepare the variance bridge, retrieve supporting documents and draft the explanation, while flagging the part nobody has substantiated.
The reviewer then has a specific question to resolve, which is a meaningful improvement over receiving polished prose and having to repeat the investigation to trust it.
Go upstream to the failed load
The same opportunity appears before data reaches Anaplan. An overnight headcount import fails because a column in the HR extract has changed. Someone arrives, reads the error, compares files, checks the mapping and works out whether the change is harmless.
Some of that should already be ordinary automation. Required columns, valid codes, duplicate records and control totals can be checked with explicit rules. If someone still has to export a file, email it and upload it each month, start by automating that handoff and validating the load.
AI becomes useful when the exception needs interpretation. Suppose “Department” has become “Supervisory Organisation”. The columns might contain equivalent values. They might represent different organisational structures. A confident rename could load successfully and still produce the wrong plan.
A good assistant would compare the files, inspect the existing mapping and retrieve any relevant change notice. It would present the evidence for a proposed fix and identify what remains uncertain.
The integration owner would review a prepared case, with the checks needed before a rerun. Measure the benefit in time spent resolving the exception, and in whether the fix survives the next load.
Then ask why UK03 is excluded
A different problem appears when someone inherits the model. Why does this import exclude one entity? Why are these two lists almost identical? Which downstream process depends on that export?
Tools that explain model structure help. But structure alone may not reveal the business decision behind it. An exclusion could reflect an acquisition, a workaround that was meant to last a quarter, an agreement that expired two years ago, or a builder who left.
The reason may survive in a design note or an old ticket. Sometimes it survives only in someone’s memory (more often than any handover admits).
This is where a searchable body of implementation knowledge starts to matter: model documentation connected to decisions, tickets, operating procedures and handover notes.
It also needs to preserve uncertainty. If a ticket describes an exclusion as temporary and no later decision can be found, the answer should say so. Turning an old workaround into an authoritative explanation would make the implementation harder to maintain.
And if the reason was never recorded, someone still has to supply it. AI is one more reason to capture decisions while the people who made them are still around.
Feed, prove, remember
These examples suggest three places to look around an Anaplan implementation.
Feed: prepare and validate incoming data, and help resolve exceptions before they reach the model.
Prove: reconcile outputs and assemble the evidence needed to explain and approve them.
Remember: preserve the decisions and operating knowledge needed to maintain the implementation.
Each has a different test of success. Did the load arrive correctly? Could the reviewer substantiate the commentary? Could a new builder understand the decision without tracking down their predecessor?
A citation helps, but it doesn’t establish that an explanation is correct. An approval helps when the reviewer can see what changed, what was checked and what remains unresolved.
Those details determine whether the automation saves work.
Pick one piece of the month
Start with one recurring task around your model: a load that regularly needs intervention, a reconciliation that consumes half a day, or a commentary process that sends analysts searching through several systems.
Follow one instance from beginning to end. Record the inputs, the checks, the judgement calls, the handoffs and the person accountable for the result. Then decide which steps can run on fixed rules, which need help interpreting evidence and which require a person’s decision.
You may discover that the immediate fix is a better mapping table or a missing validation check. You may find a well-bounded job for an agent. Either result is useful.
The measure is how much work remains before someone can use the number with confidence.
Next month, when freight is over budget again, how much of that morning will finance get back?
This is part of a strand about Anaplan estates: the automation, data pipelines and AI that wrap around a planning model. I’m a Master Anaplanner with eight years of builds behind me; the strand is what they taught me.