Enterprise finance teams are being pushed to forecast faster, connect fragmented data and make decisions with less manual analysis. Jedox and Polestar Analytics are responding with a strategic partnership aimed at combining AI-powered financial planning, enterprise data engineering and domain-specific consulting to modernize how CFO organizations plan and manage performance.
For CFOs, the problem with traditional planning is increasingly obvious: the business can change faster than the spreadsheet can be updated.
Forecasts still depend heavily on manually assembled data, disconnected systems and planning cycles that can take weeks. At the same time, enterprises are investing in artificial intelligence to automate analysis and accelerate decision-making.
Jedox and Polestar Analytics are betting that those two trends can be addressed together.
The companies have announced a strategic partnership that will combine Jedox’s financial planning and performance management platform with Polestar Analytics’ expertise in AI, data engineering and enterprise planning. The initial focus will be on Consumer Goods and Retail, Manufacturing, Pharmaceuticals and Financial Services.
The goal is broader than deploying another finance application. The companies want to help enterprises move from conventional budgeting and reporting toward more connected, AI-assisted planning across finance and other business functions.
From financial planning to enterprise planning
Jedox provides software for financial planning, performance management, forecasting and reporting. The platform is designed to bring financial and operational data into a common planning environment.
Polestar Analytics adds a different layer: implementation expertise, data orchestration, enterprise AI and industry-specific transformation capabilities.
That combination matters because AI-powered planning is only as useful as the data and processes supporting it.
An AI system can generate a forecast quickly, but a finance organization still needs reliable source data, appropriate business rules, governance and integration with existing enterprise systems. Those requirements become more complicated when planning extends beyond finance into supply chain, sales and workforce operations.
The partnership is therefore targeting the broader enterprise planning stack, rather than treating AI as an isolated feature.
Gareth Morris, chief revenue officer at Jedox, said the companies intend to help finance leaders generate faster insights, improve forecasting and increase business agility.
Polestar Analytics CEO Chetan Alsisaria described the next generation of enterprise performance management as increasingly defined by AI, automation and faster decision-making.
Why the data layer matters
The most interesting part of the partnership may be its emphasis on data engineering.
Large companies rarely operate from a single source of truth. Financial information can sit in enterprise resource planning systems, customer platforms, data warehouses, spreadsheets and specialized operational applications.
Polestar Analytics says its experience spans platforms including Anaplan, Microsoft, AWS, Snowflake and Databricks. That multi-platform exposure could become an important differentiator as enterprises attempt to connect planning software to increasingly complex data architectures.
For CFO teams, the practical question is not simply whether an AI planning application can produce a forecast. It is whether the forecast can be traced back to trusted data and updated as underlying assumptions change.
That requires integration.
It also explains why enterprise planning is becoming increasingly adjacent to data engineering and AI infrastructure. A planning platform can provide the interface and modeling environment, but the quality of its outputs depends heavily on what sits underneath it.
Agentic AI enters the planning conversation
The partnership also places agentic AI into the enterprise planning discussion.
Agentic systems are designed to perform multi-step tasks with some degree of autonomy rather than simply responding to individual prompts. In financial planning, that could eventually mean AI systems monitoring business data, identifying deviations from forecasts, investigating drivers and preparing scenarios for human review.
The distinction is important.
Traditional business intelligence primarily tells finance teams what happened. Predictive analytics can estimate what might happen. Agentic planning aims to take the next step by helping determine what should be investigated or changed.
That does not remove the CFO from the process. In highly regulated or financially consequential environments, human oversight, auditability and governance remain essential.
Instead, the opportunity is to reduce the manual work between identifying a problem and deciding how to respond.
Competition is already crowded
Jedox and Polestar Analytics are entering a market with established enterprise planning vendors and major cloud platforms.
Anaplan has built a substantial position around connected planning, while Microsoft combines financial and operational analytics through its broader enterprise ecosystem. Oracle, SAP and Workday also have significant relationships with finance organizations and enterprise data environments.
Cloud data platforms such as Snowflake and Databricks, meanwhile, increasingly sit underneath enterprise AI strategies.
The competitive challenge for Jedox is therefore not simply to offer AI features. It needs to demonstrate that customers can implement those capabilities quickly and connect them to the systems already running their businesses.
That is where Polestar’s consulting and implementation capabilities could be strategically useful.
Rather than competing only at the software layer, the partnership gives Jedox access to an implementation organization with experience across multiple enterprise technology ecosystems.
CFO technology is expanding beyond the finance department
One of the broader implications is that financial planning is becoming less isolated from operational planning.
A finance forecast depends on sales assumptions. Sales forecasts depend on demand. Demand affects supply chains and inventory. Workforce requirements influence costs and delivery capacity.
That interconnectedness is driving interest in integrated business planning, where financial and operational models are connected rather than maintained as separate exercises.
For enterprise teams, the potential benefit is a shorter path from operational change to financial insight.
If demand falls in one market, for example, a connected planning environment could allow finance, supply chain and sales teams to evaluate the consequences using shared assumptions rather than rebuilding separate models.
The technology challenge is substantial, but so is the potential payoff.
The real test will be adoption
The Jedox-Polestar partnership is ultimately a bet on execution.
AI-powered planning has attracted significant enterprise attention, but finance departments are not likely to abandon established processes simply because a platform has generative or agentic capabilities. CFO organizations need measurable improvements in forecast accuracy, planning speed, data quality and operational decision-making.
That puts implementation at the center of the value proposition.
The companies plan to develop solution accelerators, conduct joint go-to-market activities and collaborate on complex planning transformations across the four initial industries.
If successful, the partnership could position Jedox to compete more effectively for enterprises looking to modernize financial planning without rebuilding their entire technology environment.
For CFOs, the more important trend is larger: financial planning is becoming an AI and data-engineering problem as much as a finance software problem.
The vendors that can connect those three layers—planning, data and intelligent automation—may have the strongest opportunity to define the next generation of enterprise performance management.
Market Landscape
Enterprise performance management is moving away from periodic budgeting toward continuous, data-driven planning.
The market includes dedicated planning vendors such as Jedox and Anaplan, enterprise software providers such as Microsoft, Oracle, SAP and Workday, and data-platform companies including Snowflake and Databricks. Their approaches differ, but the competitive direction is similar: connect financial and operational data, automate repetitive analysis and make forecasts more responsive.
AI is accelerating that transition.
For CFO organizations, however, adoption will depend on more than model performance. Data governance, integration, security, explainability and human oversight are critical when AI begins influencing financial forecasts and business decisions.
The Jedox-Polestar relationship reflects that reality by combining planning software with data engineering and implementation expertise.
What it means for enterprise teams
For finance leaders evaluating AI planning platforms, several questions should come before deployment:
- Can the platform connect to the organization’s existing ERP, CRM and data warehouse environment?
- Can finance teams audit the assumptions behind AI-generated forecasts?
- How much implementation work is required before the system produces useful results?
- Can planning extend into supply chain, sales and workforce functions?
- What controls exist for autonomous or agentic AI workflows?
- Can internal teams maintain and adapt the platform after implementation?
Those questions will increasingly separate genuine enterprise transformation from AI feature adoption.
Top Insights
- Jedox and Polestar Analytics are combining AI-powered planning with enterprise data engineering, targeting CFO teams that need faster forecasts and more connected decision-making.
- The partnership expands planning beyond finance, connecting financial forecasts with supply chain, sales and workforce planning across complex enterprise environments.
- Polestar’s experience across Anaplan, Microsoft, AWS, Snowflake and Databricks gives the partnership a multi-platform perspective on enterprise data integration.
- Agentic AI could automate parts of financial planning, but governance, auditability and human oversight remain essential as AI influences consequential business decisions.
- Competition includes Anaplan, Microsoft, Oracle, SAP and Workday, making implementation speed, data integration and measurable business outcomes critical differentiators.
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