As Anaplan clients plan the next phase of their digital planning journey, one question is surfacing more often—Is now the time to move to Polaris?
Polaris, Anaplan’s next-generation calculation engine, is more than a technical evolution. It’s a shift in how enterprise planning can be designed—free from the traditional limitations of sparsity, dimensionality, and hierarchy complexity.
In this blog, we explore how clients are making the move to Polaris not just to gain performance, but to unlock clarity, flexibility, and adoption—and, ultimately, to spend more time on what to plan, and less time figuring out how. From their experiences, here are five key considerations to help you assess whether Polaris is the right next step for your business.
Many Classic Anaplan models reserve memory based on all possible intersections, even empty ones, creating inefficiencies in large, sparse models.
One client's proof-of-concept model, contained 4 billion cells and used 24GB in Classic. On Polaris? Just 9.5GB. While some migration tuning was required, the scalability benefits were immediate and significant.
But the real impact? Polaris eliminates the need for technical workarounds to handle sparsity. That means simpler models, faster deployment, and easier maintenance.
The simplest models are the most adaptable ones—faster to pivot, easier to debug, and better able to handle curveballs.
Classic Anaplan’s dimensional limits often force compromises, which require you to either simplify data or introduce complex workarounds that make models harder to use and maintain. Polaris removes those constraints, allowing models to reflect the full richness of your business, essentially creating a digital copy of how you operate.
A Swedish manufacturer introduced Polaris to restructure their planning environment by separating reporting logic from user-facing experiences. This streamlined the overall design and enabled richer, multi-dimensional insights to flow directly into reports, without the performance drag or model bloat that typically comes with added complexity.
This approach didn’t just improve technical performance—it had a major impact on user adoption. Because the model structure closely mirrored the business itself, users were better able to understand and relate to the outputs. There was no need to "learn how the model works"—it worked the way they did, reducing friction and increasing trust in the system.
At OpenAI, Polaris supported a highly dimensional planning cube across vendor spend, compute costs, and revenue drivers. See the full story in this fireside chat. With eight or nine dimensions and trillions of cells, Polaris handled it all natively—delivering high performance without sacrificing depth.
Why it matters: Polaris gives teams the freedom to explore data in the ways that make sense to them, without needing to force-fit analysis into technical limits.
More dimensions, fewer compromises—and because the model output is intuitive and aligned to the real world, adoption rises, and decision-making speeds up.
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As your business adapts and grows, consolidations can become complex, across teams, geographies, or product lines. This often leads to nested logic, hierarchy-heavy structures, and brittle formulas that are difficult to manage and even harder to change.
In the Swedish case, Polaris helped streamline consolidation logic by removing the need for nested IFs and layered hierarchies, making the model more stable, more maintainable, and easier to evolve.
But there’s another critical benefit: greater transparency. Without the need for dense transformation logic or hidden mappings, users can see and trace data in its raw form. That reduces non-value admin effort and makes inevitable debugging or change requests far easier to manage.
At OpenAI, Polaris removed the need for custom hierarchies enabling a flatter, cleaner design, that supports better governance and reduces the risk of errors.
When your model mirrors your real-world structure, your team doesn’t have to “learn how the model works.” It just works—because it works like they do.
OpenAI’s move to Polaris was part of a broader “AI-first” strategy. With Polaris in place, they were able to integrate ChatGPT into Anaplan workflows to automate documentation, retrieve insights instantly, and analyse planning outputs in real time.
But the foundation for this level of automation and responsiveness lies in data accuracy, granularity, and consistency—all of which Polaris delivers by design.
Because Polaris supports highly dimensional and precise models, businesses gain a more accurate representation of operations. This means insights are not only faster—they’re more reliable. And when models reflect the real-world business structure in detail, it becomes significantly easier to run scenarios, test responses to “what if” situations, and adapt to unanticipated events with confidence.
The more accurately your model mirrors the business, the better prepared you are to respond to uncertainty—with clarity, speed, and confidence.
Companies like OpenAI and the Swedish manufacturer aren’t moving to Polaris just to fix performance problems. They’re using it to build a long-term foundation for integrated business planning.
Polaris allows them to:
When you’re not busy wrangling the model, you can focus on what matters: making better decisions, faster.
Across the industries that Profit& supports, Polaris is already reshaping how businesses plan, model, and make decisions. Here's a flavour of how Polaris can impact these sectors.
For Transport and Logistics organisations, Polaris can model operations at the site level—tracking volumes, routes, and delivery performance with precision. This unlocks the ability to scenario-plan customer contracts, fleet investments, and new sites in real-time, enabling strategic decisions based on real operational data.
Polaris supports the high complexity found in manufacturing—from multi-stage production processes to intricate Bill of Materials. This helps teams simulate the impacts of recipe changes, equipment upgrades, or supply chain shifts with more confidence and less technical effort. The result is better-optimised production and investment decisions.
In fast-moving SaaS and software businesses, Polaris enables finance and GTM teams to model AAR (Annual Recurring Revenue), usage-based pricing, renewals, and customer segmentation dynamically. This supports more adaptive planning, faster scenario modelling, and greater alignment between commercial and financial performance—all in a single, integrated platform.
Polaris isn’t just a faster engine, it’s a different way of modelling.
If your models are strained under the weight of complexity, if your teams are spending more time explaining how things work than acting on insights, it might be time to ask:
What are we missing by staying where we are?
We’d love to hear from you.
Have you already made the move to Polaris? What difference has it made in how your teams work, model, and plan?
Are you currently evaluating the shift? Does your list of decision criteria align with what we’ve outlined, or are other factors shaping your thinking?
Have you considered the move but aren’t sure how to assess the benefits, or need help building the business case?
Whether you’re well into the journey or just starting to explore the possibilities, we’re keen to learn from your experience and share what we’re seeing across the Anaplan community.
If you're facing challenges with complexity, scalability, or model adoption, or simply want to sense-check your current planning architecture, we're here to help.
Let’s start a conversation. Drop us a message, connect with the team at Profit&, and let’s explore together whether Polaris is the right next step for your business.