Thinking on decision intelligence.
Practical writing on how organisations make better operational decisions — what works, what does not, and why.
Latest articles.
Agentic ERP ROI: how to build the business case and measure it
Agentic ERP ROI is proven with your own data, not borrowed percentages. How to build the business case with a baseline, decision lead time, recommendation acceptance and avoided cost you can actually trace.
Agentic ERP in retail: stock, replenishment, store transfers and pricing
Agentic ERP in retail means agents watch sales and stock per store and product, then prepare replenishment, store-to-store transfer and pricing recommendations that are written to the ERP after approval.
Agentic ERP for finance and cash management: collections, payments and cash forecasting
Agentic ERP for finance and cash management: agents watch receivables, payables and bank data, then prepare collection priorities, payment plans and cash alerts with reasons, applied only after approval.
Agentic ERP for production planning: capacity, materials and shift decisions
Agentic ERP for production planning lets agents watch work orders, capacity and material status, spot deviations from plan and prepare a reasoned re-plan that is written to the ERP once a planner approves.
Agentic ERP for procurement: from requisition to purchase order with AI agents
Agentic ERP for procurement means AI agents read ERP data, spot a buying need, weigh supplier options and prepare a reasoned purchase order that is written back only after a named buyer approves it.
Agentic ERP governance: approval, roles, audit trail and rollback
Agentic ERP governance is the set of rules defining what AI agents may do, who approves, how every step is recorded and how a wrong transaction is rolled back in the ERP.
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Every pilot is judged on criteria agreed up front.
Agreed before we start
One recurring decision and the measures it will be judged on — for example stock-out store-days, or the share of demand met by transfer instead of a new purchase.
Measured on your data
The measures are tracked from the first day, in your own systems, against the period before the pilot.
Reported, then decided
You receive the measurement report and decide whether to widen the scope. The evidence is yours either way.
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What we read, what we measured, and what did not work. Not product announcements; notes on operational decisions without the marketing layer.
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What your teams gain from Veraius agents.
Budget against reality
Planned and actual spend are compared by cost centre, and variances above your threshold are escalated with their cause.
See the Finance agents SalesKnow which lead is worth the call
Inbound leads are scored hot, warm or cold from their behaviour and fit, and routed to the right owner.
See the Sales agents ManufacturingCapacity against orders
Open orders are compared with available capacity so delivery risk shows before a due date is missed.
See the Manufacturing agentsIn every department
- Agents prepare the work on your own data; your people approve what happens next
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Grounding check: numbers that cannot be traced to a source are not shown
- Explainability, confidence and risk scores with decision rationale
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
- Department-based access policy with personal data masking
Frequently asked questions
Straight answers about integrations, deployment, security and cost — including where the honest answer is "it depends".
Ask your own questionWhat topics does the blog cover?
How operational decisions are actually made and where they fail. Not model architectures — those are well covered elsewhere — but the layer above them: what happens when a forecast is right and the decision is still late, why an audit trail is a product requirement rather than a compliance one, and which decision to automate first.
Who are these articles written for?
The person accountable for the decision, not the person building the model. A supply chain director evaluating whether agentic AI is real, a plant manager who has been shown three dashboards and made no better decisions, an IT lead asked whether this can run on-premise.
Do you publish on a schedule?
When there is something measured to say. A piece that restates a known position adds noise to a category that already has too much of it.