Board announces the launch of the Contextual Decision Layer, a new layer designed to connect enterprise data, artificial intelligence and planning processes. The objective: to help organizations transform their data into financial and operational decisions more effectively.
A new approach to business decision-making
Companies today have access to numerous data platforms, analytical solutions and artificial intelligence technologies. Yet these investments do not always effectively address planning and decision-making needs.
According to Board, the challenges stem in particular from data fragmentation, different definitions across systems and the lack of business context in certain AI applications. Information may therefore be available without being directly usable for building a budget, adjusting a forecast or evaluating a scenario.
It is in this context that Board introduces its Contextual Decision Layer for Continuous Planning, designed to provide a layer of business context and governance between data, AI and decisions.
Adding context to enterprise data
The Contextual Decision Layer does not seek to replace existing data platforms. Rather, Board aims to complement them by adding the elements required for planning and decision-making.
The solution can notably integrate common definitions of metrics, hierarchies, plans, forecasts, scenarios, assumptions and calculation rules. It also takes into account operational constraints, workflows, approval processes, access rights and traceability.
Board specifically cites data environments such as Databricks, Snowflake and Microsoft Fabric, which can serve as foundations for data, analytics and artificial intelligence.
The objective is to create continuity between the data available within the enterprise, planning models, the decisions made and their implementation in operational processes.
Putting guardrails around artificial intelligence
One of the main issues raised by Board also concerns the use of AI in planning processes.
Generative artificial intelligence can analyze data or produce recommendations, but it does not necessarily know an organization’s specific rules, calculation methods or validation processes.
The Contextual Decision Layer therefore aims to provide AI with a more precise business framework. Models can rely on governed data, common definitions, deterministic calculations, business rules and controlled workflows.
This approach should in particular make AI-assisted decisions more consistent, explainable and traceable. Human validation also remains integrated into the process.
Toward continuous planning
This new layer directly supports the continuous planning strategy advocated by Board.
The principle is to evolve plans and forecasts as the economic and operational environment changes, rather than limiting planning to a few annual cycles.
Companies can thus reassess their assumptions, compare different scenarios and measure the financial and operational consequences of their decisions.
For finance departments, this approach can notably facilitate forecast updates and trade-off analysis. For operational teams, it aims to improve coordination between different functions, particularly finance, supply chain, merchandising and operations.
Governance extending through to decisions
Board also emphasizes governance. As AI takes on a greater role in business processes, the issue is no longer limited to the quality of the data used, but also concerns how decisions are constructed and validated.
The platform therefore incorporates various control mechanisms: access management, data-level security, version management, approval workflows, human review, traceability and audit.
This approach aims to meet the expectations of IT leaders and enterprise architects who wish to integrate AI into sensitive processes without multiplying isolated systems.
Transforming data into decisions
With the Contextual Decision Layer, Board ultimately seeks to bring together three dimensions that often remain separate within companies: data, artificial intelligence and planning.
The promise is therefore not simply to provide users with more information, but to enable them to place this information in a business context, test different assumptions and transform the results into decisions that can be validated and then executed.
This development also illustrates a broader trend in the EPM market: artificial intelligence is no longer limited to analysis or content generation. It is gradually becoming integrated into planning, forecasting and decision-making processes.
With this announcement, Board thus intends to strengthen its position around continuous planning, offering an approach that seeks to connect investments made in data and AI platforms with the concrete needs of finance and operational departments.
source: Board
