Why do LLMs struggle with enterprise planning?

Large language models like ChatGPT and Claude are incredibly powerful, but

enterprise planning requires more than intelligence.

It requires trusted business context.

The challenge of using generic AI directly on financial operational data is that

enterprises quickly run into problems around semantics, governance, scale, and

cost. For example, finance teams rely on very specific business logic.

Which version of the plan? Which hierarchy? Which allocation rule? Which scenario?

Which assumptions? Without that context, AI can generate inconsistent or misleading

outputs. And then there's scale. Enterprise planning involves massive data sets,

complex calculations, write-back approvals, workflows, and auditability.

That's not what general-purpose LLMs were designed to manage.

At Board, we believe AI becomes enterprise-ready only when it's grounded in

governed planning semantics and deterministic business logic.

That's why Board is the trusted planning foundation for AI.

Your AI investments become dramatically more valuable when they understand the

business context behind the numbers.