The Anaplan customer base should adopt AI-powered forecast modeling, model construction automation, insights for planning through conversations, and agentic enterprise planning to remain competitive in 2026 & beyond.

This is a figure that all Anaplan CFOs should fear: 84% of finance teams have adopted or are going to adopt AI technology, yet only 7% claim a high or very high impact.

That gap between deployment and value is not a technology problem. It is a use case selection problem. Most finance teams chase isolated AI pilots instead of embedding AI where it compounds inside their planning platform.

Anaplan has shipped significant AI capabilities through 2026. The question is which ones to prioritize so you land in that 7%, not the 77% burning budget on pilots that go nowhere.

AI in Financial Planning: From Static Budgets to Adaptive Forecasts

The rhythm of traditional financial planning in Anaplan is very rigid: annual budgeting, quarterly reforecasting, and monthly variance analysis. Anaplan Forecaster, which is the next-generation product succeeding PlanIQ, disrupts the traditional rhythm with the help of 8 ML algorithms (DeepAR+, LightGBM, TimesFM), automatic algorithm selection, and explainability.

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The business case is backed by hard numbers. Gartner’s 2026 data shows 60% of CFOs are increasing finance AI investments by 10% or more this year, with 88% ranking finance staff productivity among their top three priorities. Among organizations that have already adopted AI in finance, 66% report efficiency and productivity as the top realized benefit.

Forecaster delivers on this by enabling:

• Real-time forecasting that is continuously revised using real-time revenue data and macroeconomic changes
• Up to 10x faster processing via Anaplan-native functionality versus the previous PlanIQ engine
• No-code explainability that removes the “black box” barrier for business users

Automated Model Building with Anaplan Co-Modeler

Anaplan’s Co-Modeler, generally available since Q1 2026 as part of a $500M multi-year AI investment, uses AI to interpret natural language instructions and build model components: lists, modules, line items, formulas, and hierarchy logic. It works across both Classic and Polaris workspaces.

For organizations running lean Centers of Excellence, automated model building solves a real structural bottleneck. It does not replace architects. It stops every new planning initiative from queuing behind every other.

One important caveat: Co-Modeler follows DISCO methodology by default. Edge cases and cross-module dependencies still need human judgment. Organizations getting results pair it with structured review protocols, not hands-off automation.

Conversational Planning Insights with Anaplan Analyst

This is the most overlooked use of Anaplan’s AI in 2026. Launched in March 2026, the Anaplan Analyst lets you ask natural language questions about your planning models and get insights in real time along with dashboards.

For a CFO, this changes the daily workflow:

• Ask “What is driving the margin variance in Q2?” and get a context-aware answer grounded in your actual model data
• Skip the two-day wait for analysts to pull reports or navigate dashboard hierarchies
• Role-based access governance ensures users only see insights relevant to their function

Custom Analyst, built through Agent Studio, lets organizations create specialized AI analysts trained on their specific business logic. This is not a generic chatbot. It understands how your business plans.

Agentic Enterprise Planning: AI That Acts, Not Just Recommends

According to Gartner, 40% of enterprise software solutions will include AI agents for tasks by the end of 2026, rising from only 5% in 2025. Anaplan is already selling specialized agents for roles:

• Finance Analyst tracks performance, completes month-end activities automatically, and identifies any variances
• Supply Chain Analyst identifies risk, highlights inefficiencies, and rebalances supply and demand
• Sales Analyst warns about risks in the sales pipeline, evaluates scenarios, and enhances revenue performance

The prerequisite is connected data. If your Anaplan instance operates in functional silos, agentic capabilities underperform. Getting data architecture right is where working with an experienced Anaplan implementation partner becomes critical.

How Should Anaplan Customers Prioritize AI Use Cases?

Here’s Gartner’s advice to CFOs – pick 3-5 use cases to trial at a time, never more. This will require gaining a complete view of all the capabilities Anaplan provides, and only then applying AI where the benefits multiply.

For most teams, that means Forecaster or Co-Modeler first, then Analyst and agentic planning once your data foundation is solid.

Polestar Analytics is an Anaplan implementation partner that builds AI-enabled connected planning solutions for finance and supply chain teams. If you need help identifying where AI fits into your Anaplan environment, that is a good place to start.

Frequently Asked Questions About Anaplan AI   

Can Anaplan Co-Modeler fully replace certified model builders in a planning CoE?

No. Co-Modeler accelerates scaffolding by translating natural language into Anaplan-native structures across Classic and Polaris workspaces. However, complex business logic and governance still demand certified human oversight. The real value is reducing builder backlog, not eliminating builders.

What data infrastructure is needed before implementing agentic AI within Anaplan?

Agentic enterprise planning requires connected, clean data flowing across functional workspaces. You need a unified data layer (via ADO, Wormhole, or cloud warehouse integration), consistent dimension hierarchies, and role-based access governance. Without this, agents act on incomplete information.

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How does Anaplan Analyst differ from standard dashboard reporting?

Anaplan Analyst is a conversational AI agent, not a static dashboard. Users ask natural language questions and receive context-aware answers grounded in their specific planning logic and data. Dashboards require users to know where to look. Analyst surfaces the insight directly, with built-in data lineage for trust and verification.