AI-Powered Planning with Anaplan Intelligence

Anaplan's most significant releases of the past two years are AI releases. Anaplan Intelligence is the umbrella for the platform's AI capabilities. Anaplan Forecaster, launched in October 2025 as the next generation of PlanIQ, brings an expanded library of machine-learning algorithms, faster model training, and explainability built for business users rather than data scientists. In December 2025 Anaplan announced a suite of role-based AI agents that surface insights, draft scenarios, and recommend actions inside the planning process.

For most customers the question is no longer whether to use these capabilities, but how to use them without creating forecasts nobody trusts and agents nobody governs. That is where we come in.

What we do

AI-readiness assessment. Machine-learning forecasts are only as good as the history and drivers behind them. We review your data quality, history depth, granularity, and model structure, then tell you plainly which series are ready for Forecaster, which need data work first, and which should stay on a statistical or judgmental forecast.

Anaplan Forecaster enablement. We configure forecast actions, select and tune algorithms, set up backtesting so you can see how Forecaster would have performed against actuals, and run an explainability review with your planners so they understand which drivers move the forecast. Forecasts land in your demand, revenue, workforce, or FP&A models through the standard Anaplan actions, so they are part of the plan, not a side spreadsheet.

PlanIQ to Forecaster migration. If you have PlanIQ forecast actions in production, we recreate them in Forecaster, run both in parallel, compare accuracy, and retire PlanIQ cleanly.

Agent rollout governance. Role-based agents act on your data and propose changes to your plan. Before turning them on we define who can see recommendations, who can act on them, how actions are logged, and how Anaplan's role and selective-access security maps to agent permissions. A governed pilot beats an ungoverned rollout every time.

Measuring forecast value. We set up forecast value added (FVA) tracking that compares Forecaster output against your incumbent statistical or spreadsheet forecast and against naive baselines, so the business can see, in its own numbers, whether the AI is earning its place.

Where AI forecasting helps, and where it does not

We would rather tell you this before the project than after it.

• It helps with stable or seasonal demand series, products with two or more years of clean history, and planning areas rich in explanatory drivers (price, promotions, weather, calendar effects, leading indicators).
• It struggles with sparse or intermittent history, new products, structural breaks (a reorganization, a pricing model change, a pandemic-scale shock), and anything where the people in the room know something the data does not yet.

In the second group the right answer is usually a hybrid: a machine-learning baseline with a governed override process and FVA tracking that shows whether the overrides are adding value. Anaplan is very good at exactly that kind of workflow.

Why QuanticPlanning

Our consultants are Anaplan model builders first. We understand how a Forecaster output flows into a line item, how it interacts with your time settings and versions, and what happens to it in a Workflow approval. AI features that ignore the model they live in fail quietly; ours are designed into the model from the start. All of our consultants are US-based and available for onsite or remote engagements.

Read our tutorial on getting real value from Anaplan Forecaster, or see how we frame the broader platform roadmap in Anaplan Platform Migrations and Modernization.

Ready to put Anaplan Intelligence to work? Contact us for an AI-readiness assessment.

Back to services