Board Proposes "Decision Layer" for Enterprise AI

Software company Board has highlighted the need for a so-called "decision layer" within enterprise AI architectures. According to the company, current AI solutions, data platforms, and semantic layers are insufficient on their own to ensure trusted and governed AI-driven decision-making.

Board argues that as AI matures and its role in businesses grows, data accessibility alone is no longer enough. AI must also understand the context of the organization's decision-making processes, including assumptions, scenarios, constraints, and approved workflows. These elements have historically been distributed across various applications and human expertise.

The company introduces the "Board Contextual Decision Layer" concept. This is not presented as another application or semantic model, but rather as a business context layer that consolidates planning logic, assumptions, scenarios, constraints, workflows, governance, and accountability in a way that both humans and AI can consistently utilize.

Board suggests that many organizations already possess parts of this capability within their Integrated Business Planning (IBP) and Enterprise Performance Management (EPM) platforms. These tools collect information on business rules, versioning, scenarios, and approval processes. Board's perspective is that planning should no longer be a standalone periodic process but an integral part of a company's AI architecture.