Writing
Enterprise AI, workflow design and field-tested systems thinking — 225 pieces so far
The OWASP LLM Top 10 (2026), Read From a Finance Seat
All ten 2026 entries with what changed from 2025 — and which of them a finance or treasury deployment actually meets first.
NIST AI RMF: The Functions and Categories, Explained
All four functions and 19 categories of the NIST AI Risk Management Framework, with subcategory counts — and which finance controls already cover each one.
Four-Eyes and Segregation of Duties for AI Agents
A human clicking approve on an agent's proposal is not a second pair of eyes. What independence requires when one of the two parties is a model.
Should You Connect an AI Agent Directly to Your ERP?
Practitioners say don't, vendors say it's supported. They answer different questions. Three things a direct connection costs — one of them commercial.
AI Pilot Exit Criteria: What a Pilot Must Prove
Not an accuracy percentage. The five gates a pilot has to pass before production — borrowed from cutover, where the questions are older and harder.
How to Judge an Enterprise AI Vendor's Claims
An accuracy number without a denominator is not a claim. The questions that separate a product from a demo, from someone who runs vendor selections.
Where to Start With Enterprise AI
Not with a pilot. A sequence borrowed from enterprise systems delivery — and the three steps in it that change when the component is probabilistic.
AI Agents in SAP: What Actually Lands in the System
Role-aware is not segregation-safe. What an agent posting in S/4HANA leaves on the document, in the release path, and in front of the auditor at period end.
Can an AI Agent Approve Its Own Payment?
It is not a new question. It is a release strategy — and the per-transaction limit everyone reaches for is the one an agent defeats first.
Security Boundaries for Enterprise AI Agents
An agent that reads untrusted content can be instructed by it. What the boundaries are, which of them hold under pressure, and which are theatre.
The EU AI Act's High-Risk Rules Are a Control Environment
The high-risk deadline moved to December 2027. What the rules ask for did not move — and a finance function already runs most of it under other names.
AI Workflow Teardown: Bank Reconciliation
Bank reconciliation taken apart: why the matching engine was never the bottleneck, what AI does with the residue, and which breaks need a named human.
Agent Identity: Who Is the AI Acting As?
Delegated, shared, agent-specific or temporary — the four identities an AI agent can act under, and what each one costs you in attribution and blast radius.
The Enterprise AI Control Layer
You cannot make an AI system safe by improving the model. Safety lives in a control layer between your systems of record and the agents acting on them.
How to Measure ROI From an AI Workflow
Licences times adoption times a guess measures deployment, not outcome. The ten metrics that move when work is redesigned, and how to baseline them credibly.
Designing the Exception Path
Demos are built on the clean case; enterprise work lives in the other one. Detection, queue, escalation, resolution and the write-back almost everyone omits.
Human Decision Rights in AI-Native Workflows
The useful question is not what AI can automate. It is who is allowed to decide what — a rights matrix over every action a workflow contains.
How to Find Workflows Worth Redesigning With AI
Eight dimensions you can observe rather than debate — frequency, effort, input, judgement, exceptions, error cost, state, control — and the verdict they give.
AI Workflow Teardown: Cash Forecasting
A corporate cash forecast, taken apart: where the elapsed time really sits, what stays deterministic, what AI genuinely does, and what a named human decides.
How AI Agents Fit Into Existing Enterprise Systems
The four placements an AI agent can take beside your existing systems — read-only, propose-and-queue, scoped write, orchestrator — and what each one demands.
Why Enterprise AI Is an Architecture Problem
Enterprise AI stalls on architecture, not model quality: who owns the record, what the AI may write, under whose identity, and how it is logged and rolled back.
AI Automation vs Augmentation vs Autonomy
Three genuinely different designs, not three rungs of a ladder. The criteria that decide which one a piece of enterprise work actually deserves.
The Anatomy of an AI-Native Enterprise Workflow
Take a complex process apart: what must stay deterministic, what AI genuinely does well, what a named human decides, and the state the work has to hold.
Why Most AI Pilots Never Become Operating Systems
A pilot proves capability. Running the work needs state, an exception path, decision rights and an owner — which is why good demos never become the system.