Why 500 agent skills are less useful than five measured outcomes
Reddit surfaces large skill collections while buyers still face integration and channel-fit friction..
The useful question for buyers is whether an agent workflow can keep operating when the work becomes real: production teams need scoped tools, evidence logs, rollback, and reusable workflows.
My take: Publish task-level acceptance evidence and integration fit, not catalogue size.. That means teams should judge agent platforms by the boring proof that makes autonomy usable: scoped tools, fixed helper commands, evidence logs, validation before publishing, and recovery paths that preserve the work already done.
The market will keep rewarding polished demos in the short term. But durable value will come from systems that can show their sources, explain their actions, recover from failed runs, and leave clean handoffs for the next operator.
That is the difference between theatre and dependable business infrastructure.
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Cash forecasting is often described as a spreadsheet problem. In an ERP-enabled finance team, it is usually a data ownership problem.
The opening cash balance may be sound. The uncertainty sits in the timing: when customers will actually pay, when supplier invoices will be approved, which payroll and tax payments are fixed, and which commitments have not yet reached the ledger.
AI can help treasury teams spot patterns in payment behaviour and flag forecast exceptions. But a model cannot repair unclear source definitions or make an unapproved purchase order a reliable cash commitment. Before adding AI, finance leaders need to know exactly which Business Central, SAP or other ERP events feed the forecast, who owns each assumption, and how exceptions are resolved.
A practical starting point is a rolling 13-week forecast with three layers:
• Reconciled bank and ledger opening positions.
• Contractual inflows and outflows tied to source records and due dates.
• Behavioural adjustments, visibly separated from booked facts.
Then measure forecast error by category and horizon. If receipts are consistently late, investigate customer terms, collections and dispute status. If payments are early, examine approval workflows and payment-run rules. These findings improve both the process and any future AI model.
The useful question is not “Can AI predict cash?” It is “Can finance explain why the forecast changed, trace it to evidence, and act early enough to matter?”
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