Summarize this blog post with:
| The Anaplan CoModeler platform acts inside your model. That single property is both its leverage and its risk. Used well, it accelerates implementation work. Used without context, approval, and expert judgment, it can amplify the wrong change just as quickly. |
TL;DR
This article looks past the hype and focuses on mechanics: how Anaplan CoModeler reads an existing model, proposes changes, keeps humans in the approval loop, and supports expert judgment. That distinction matters because an AI agent in a planning model is not inherently good or bad. It becomes an accelerant or a liability depending on the practice around it.
Market signals point to the same tension. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Finance adoption has also levelled off after early momentum, moving from 37% in 2023 to 58% in 2024, then holding at 59% in 2025. The issue is not simply capability. It is governance, data quality, and the ability to explain the model after the change.
Anaplan is investing directly into this space through a $500 million multi-year AI innovation roadmap spanning platform, applications, and AI. CoModeler is one of the flagship capabilities in that roadmap. For implementation teams, the practical question is no longer whether CoModeler can generate formulas, modules, or even models from plain language. The sharper question is whether it can operate inside a model you already own without making that model harder to govern.
That is especially important for experts, who remain accountable for the model after the change. Speed does not remove implementation risk. It multiplies whatever direction you point it in. The useful frame, then, is not simply "what can CoModeler do?" It is: under what conditions does CoModeler make an implementation practice stronger?
Drawing from insights shared by Steven Gnatovich and Vijay Vyakaranam of Polestar Analytics in the ACE session on CoModeler for clear, correct, continuously improved models, the answer comes down to three requirements: context, approval, and judgment.
Short on time? Watch the 60-second teaser on what CoModeler does and where it differs.
Most AI-in-Anaplan coverage treats generation as the hard part. In real implementations, it rarely is. Teams usually inherit live models with thin documentation, established naming conventions, hidden dependencies, and hierarchies they did not build. The first job is understanding what already exists before deciding what should happen next.
The most useful behaviour was not generation. CoModeler worked from the model outward instead of from the prompt inward. When handed an inherited model with an empty Notes section and a one-sentence prompt to document all 14 modules in a single pass, it inferred purpose from the model's own conventions without a supplied schema or data dictionary.
It can interpret all that on its own. And that's because it has been trained on a decade or more of Anaplan's implementation knowledge and learnings from millions of deployed customer models. It brings all that expertise into every interaction we have with it.
~ Vijay Vyakaranam
That training is why the output reads like a builder interpreting architecture, not a language model guessing at intent. The same pattern appeared in a higher-stakes example: adding multi-dimensional currency translation to a live model. Here is what the Anaplan AI model builder reasoned through without being explicitly told:
- Object validation first. Before proposing anything, it confirmed the currency list, the G2 country and G3 city hierarchy, the product and product-family lists, the margin CAL module, the SYS mapping module, and the FX input module all existed. It refused to build against objects it could not find.
- Formats it was never told. It distinguished number-format from non-number-format line items on its own. Source currency is text, and it flagged it as such without instruction.
- Aggregation logic. Revenue rolls up across the product hierarchy; margin is revenue minus cost of sales and needs no further roll-up. It reasoned both and set summary methods accordingly.
- Dimensional translation. City to country, product to product family, mapped through the hierarchies already present.
- The FX lookup. It wired the rate lookup and the multiply into the revenue formula itself.
The sequence matters: validate, infer, then build. That is the pattern of a model builder, not a formula generator. These are the Anaplan CoModeler capabilities that distinguish the tool in real implementation work.
Think of CoModeler as a junior architect. In a matter of minutes, it came back and said, 'I think these are all the modules in the model, and this is what you want me to analyze.
Context is what makes the Anaplan AI CoModeler credible on an existing model. Without it, you have a fast way to bolt unrelated logic onto architecture it never read. With it, you have something that respects the model it is working inside.
The currency-extension implementation plan showing object-existence checks before any line item is proposed. Context before construction: the agent validates what exists before it builds. Watch the clip (23:44 to 25:10).
The discussion shifts from productivity to governance at this point. That shift matters because an AI agent earns or loses its place in a finance model based on one simple test: can it change anything without a named person signing off?
The most important thing the Anaplan CoModeler platform does is something it refuses to do: execute silently. The workflow keeps a named human on the decision at every step.
- Plan before action. CoModeler proposes an implementation plan and does nothing until you respond.
- Step-level control. Approve, skip, or revise individual steps. Approve step three and scrap the rest, reject four and five and run the other twelve. It revises and reconfirms before touching anything.
- Named attribution. Every change lands in Anaplan's audit history under your username, not the agent's. Who changed what, when, fully traceable.
- No silent execution. Nothing commits without your explicit confirmation.
For a planning model connected to financial decisions, a gated workflow with named attribution is not just a product feature. It is one of the Anaplan CoModeler benefits that makes adoption credible. The real question is not whether an AI system can make a change. It is whether you can explain who approved the change, why it was approved, and how it was reviewed.
Nothing is ever executed without your explicit confirmation. You're always the decision maker, and basically CoModeler is the accelerant for you.
Accelerant is the precise word. This Anaplan planning AI makes a process you control go faster; it does not decide where the process goes.
The teams that scale AI agents well will not be the teams that generate the most modules. They will be the teams that move faster while keeping this approval line intact.
It's time to run Anaplan CoModeler on a model you already own
Polestar Analytics is an Anaplan implementation partner that builds AI-enabled connected planning solutions for finance and supply chain teams.
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If context makes Anaplan CoModeler credible and approval makes it governable, judgment defines its role. The tool does not replace the expert. It changes what the expert spends time on.
The DISCO review is the sharpest of the Anaplan CoModeler use cases. For readers outside the Anaplan world: DISCO (Data, Input, System, Calculation, Output) is the canonical methodology every serious implementation is graded against, module by module. Pointed at an inherited model and asked to check every line item formula against it, CoModeler did not return a flag list. It returned a diagnosis.
- Scope: Scanned 13 modules against the full DISCO ruleset in a single pass.
- Finding: There are 6 total violations, of which 2 flagged structural driver line items for country and product family sitting in the CAL R1 module.
- The reason, not just the flag: It explained why: structural drivers belong in a SYS module, not a CAL module, especially where the same line items feed a downstream calculation.
- The remediation: It returned the corrected formula, already repointed at the SYS module, ready to review.
That is the difference between a linter and a review, and it is one of the clearest Anaplan CoModeler examples of judgment support in action. What CoModeler produced in minutes would have taken a senior architect anywhere from a couple of hours to a couple of days to produce manually, depending on model size and module count. Vijay described the agent as a junior architect. On breadth and consistency, he would rate it higher because it does not get tired, miss modules, or apply standards unevenly. That is exactly where human review can degrade across a large model.
But notice what Anaplan CoModeler did not do. It did not decide which of the 206 findings mattered most. A model with 206 flags does not simply need 206 fixes. It needs someone who knows which findings are load-bearing for the roadmap, which are cosmetic, and which forms of technical debt are acceptable for now. That is judgment. The Anaplan AI model builder produced the input for that judgment, not the final decision.
This is the honest version of Vijay's "democratization of expertise" point. CoModeler does not turn every administrator into an architect. It makes consistent review available to more workspace administrators, so senior expertise can move away from mechanical inspection and toward decisions that require deeper architectural judgment. The scarce resource was never module-building alone. It was judgment. The Anaplan CoModeler shifts the bottleneck toward that scarce resource.
CoModeler does not remove the architect's job. It changes the architect's calendar. Documentation gets done instead of deferred. Extensions begin from the model's existing structure. Reviews run across the whole model consistently instead of only where the senior person had time to look.
The through-line across context, approval, and judgment is one idea: the Anaplan CoModeler platform's power and its danger are the same property, because it acts inside your model. Point it with context, gate it with approval, steer it with judgment, and it is an accelerant. Skip any one and you have bought a fast way to make a mess. The tooling does not decide which you get. The practice around it does.
That is why this matters for teams evaluating Anaplan planning AI. The demo answers "can it?" The implementation question is "should we, and how?" Choosing the right Anaplan implementation partner is what turns that answer into a repeatable practice.
Start small and treat the first use cases as controlled learning opportunities:
- Pick one inherited model. Run CoModeler in documentation-only mode first. Zero write risk, and you find out fast whether its inferences match your conventions.
- Draw the approval line before the first build request, not after. Named approver, which change classes need a second reviewer, where the audit trail gets reviewed.
- Treat the first DISCO review as triage, not a fix list. Sort the flags into load-bearing, cosmetic, and debt you accept on purpose.
No. This Anaplan AI assistant handles Anaplan's out-of-the-box solutions for finance, supply chain, and workforce, and equally handles fully custom models. Everything in the demo was custom.
Either approach can work, but the choice is a control decision. A spec file is efficient when the model is simple and the requirements are clean. For a complex model, or for an extension to an existing one, the conversational approach supports iteration and keeps control visible at each step. A full spec may increase speed, but it can reduce visibility into the build; if the requirement is unclear, the rework still returns to the implementation team. As Vijay put it: "I can give it 15 lists, 35 modules, summary methods, everything, and then I'll go take a coffee break, lunch break and come back, and the model is built."
Fully. This Anaplan planning AI lets you approve specific steps and skip others, implement step three and scrap the rest, reject four and five and run the other twelve. CoModeler revises the plan, reconfirms, and only then executes. You are always the decision maker.