Author: Miki Sato is a Product Manager, Product Management Team (Data Management) at Anaplan.
Earlier this week, our CPTO Adam Thier announced a set of AI-driven innovations designed to advance enterprise decision-making. Three products — Custom Analyst, Agent Studio, and CoModeler — are now generally available, and together they represent a meaningful step forward in how organizations can use AI to make better, faster decisions.
But this isn’t just a product announcement. It reflects a deliberate point of view on what it actually takes for AI to deliver value in enterprise planning — and that comes down to one word: structure.
Why AI adoption stalls in most organizations
Despite significant investment in AI, many organizations still hesitate to act on what AI tells them. The outputs may look reasonable, but there’s rarely enough confidence to base real business decisions on them.
The underlying issue is architectural. In most companies, data sits in lakes, planning logic is scattered across tools, and AI is layered on top as a separate system with no real connection to either. The result is a gap between what AI produces and what the business actually needs to trust and act on.
Decisions aren’t made on data alone. They’re made on the logic that defines how a business operates, its assumptions, rules, and definitions. When AI is disconnected from that logic, its outputs will always fall short of being truly actionable.
Structure is the missing link
The solution isn’t to build a better AI layer. It’s to ground AI in the planning models where business logic already lives. In Anaplan, those models aren’t just data stores, they encode how the business thinks and plans. When AI operates within that structure, rather than alongside it, the outputs are inherently aligned with how the organization defines its own decisions.
Figure 1: AI is embedded directly into the planning model; every insight is grounded in the same structure that drives business decisions.
This is what makes AI explainable, controllable, and trustworthy. Not because the AI itself has changed, but because it’s working within a framework the business already owns and understands. The boundaries aren’t limitations, they’re what make the outputs credible.
The platform design that makes this possible
Trustworthy, structure-grounded AI doesn’t happen by default. It requires a platform built around three core principles:
- Collaborative experience: The structure AI depends on is only as good as the models it’s built on. Tools like CoModeler make it easier for the people who know the business best to build and maintain those models collaboratively, keeping the structure accurate as the business evolves.
- Data and connectivity: The quality of AI outputs depends entirely on the quality of its inputs. The Anaplan Data Orchestrator (ADO) transforms and catalogs data from across the organization, ensuring AI always has a clean, consistent, and governed foundation to work from.
- Intelligent scale and performance: AI needs to work with live data, not snapshots. Anaplan’s Polaris™ calculation engine makes it possible for AI to query the full, real-time state of a model interactively, so every answer reflects what’s true right now, not what was true when the data was last loaded.
Figure 2: AI agents operate across applications, grounded in a unified data ontology and powered by enterprise-scale calculation.
What this unlocks going forward
With this foundation in place, AI can move beyond answering questions to actively surfacing the right ones. That means proactively flagging risks, identifying signals the business might otherwise miss, and connecting plans across departments into a single, coherent enterprise view.
The longer-term vision is a decision-making ecosystem where planning and execution are continuously connected, with Anaplan as the system of record for how the business thinks, adapts, and acts.
What’s changed isn’t AI itself. What’s changed is the structure it runs on, and that makes all the difference.
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