[{"data":1,"prerenderedAt":49},["ShallowReactive",2],{"topic-ai-workflow-design":3,"topic-faq-ai-workflow-design":20,"topic-nav-counts-ai-workflow-design":43},[4,15],{"path":5,"title":6,"description":7,"type":8,"language":9,"date":10,"order":11,"cluster":12,"minRead":13,"cornerstone":14},"\u002Fblog\u002Fai-automation-vs-augmentation-vs-autonomy","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.","text",null,"2026-08-18",2,"method",8,true,{"path":16,"title":17,"description":18,"type":8,"language":9,"date":10,"order":19,"cluster":12,"minRead":13,"cornerstone":14},"\u002Fblog\u002Fanatomy-of-an-ai-native-enterprise-workflow","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.",1,[21,32],{"path":5,"title":6,"order":11,"faq":22},[23,26,29],{"question":24,"answer":25},"What is the difference between AI augmentation and AI autonomy?","Augmentation means the machine prepares the work and a person decides: it reads the inputs, assembles the evidence, drafts a recommendation, and a named human accepts, changes or rejects it. Autonomy means the machine decides and acts inside a bounded remit — defined limits, defined data, defined actions — and the human moves from deciding each case to setting the boundary and reviewing what came out of it. The distinction is not how capable the model is. It is where the decision sits, and who is answerable for it when the decision turns out to be wrong.",{"question":27,"answer":28},"When does a task justify an autonomous AI agent?","When four things hold at once: the action is reversible or cheaply corrected, the blast radius of a bad run is bounded by real limits rather than good intentions, the exception rate is low and understood from evidence rather than assumed, and someone in the control environment owns the outcome by name. Add a fifth practical test — the remit has to be narrow enough to write down in a sentence. Most enterprise work fails at least one of these, usually the exception rate, and the honest response is to run it as augmentation until the evidence changes.",{"question":30,"answer":31},"Why is augmentation the right default for most enterprise work?","Because most consequential enterprise work is judgement applied to messy inputs, and the expensive part is assembling the evidence, not making the call. Augmentation attacks exactly that: the machine does the reading, gathering and drafting, and the person spends their time on the decision instead of the preparation. It also keeps the control environment intact — the same named approver, the same audit trail — so it can be adopted without renegotiating how the organisation is governed. It is the fastest real gain available and the one that survives contact with an auditor.",{"path":16,"title":17,"order":19,"faq":33},[34,37,40],{"question":35,"answer":36},"What makes a workflow AI-native rather than AI-assisted?","An AI-assisted workflow keeps its original shape and adds a model to one step — the same handoffs, the same approvals, one of them now faster. An AI-native workflow is redesigned around what each part of the work actually is: the calculations and controls stay deterministic code, the reading and drafting and classifying go to a model, the decisions that carry consequence go to a named human, and the whole thing runs inside an application that holds state, versions and an audit trail. The test is simple. If you removed the model, would the process be the old process again? If yes, it was assisted, not redesigned.",{"question":38,"answer":39},"Which parts of an enterprise process should stay deterministic?","Anything whose answer must be identical every time it is asked, and anything a control depends on. That means calculations and totals, limit and threshold checks, entitlement and segregation-of-duties rules, postings to a system of record, and the routing logic that decides who sees what. These are cheap to express as code, easy to test, and easy to audit — and a probabilistic component adds nothing to them except a new failure mode. Keeping them deterministic is also what makes the AI parts safe to use, because the model's output has something firm to be checked against before it moves.",{"question":41,"answer":42},"Why can a chat window not replace an enterprise application?","Because a conversation has no state that survives it. Enterprise work is resumable — someone picks up on Thursday what someone else left on Monday — and that needs things a chat window does not hold: a record with an owner and a status, versions of what changed and why, an audit trail that ties an action to a person and a time, and a queue everyone is looking at rather than a private transcript. The model can live inside the application and do the reading and drafting. What it cannot do is be the place the work lives.",{"enterprise-ai-transformation":11,"enterprise-ai-systems":11,"treasury-management-systems":44,"cash-and-liquidity-management":45,"treasury-risk-management":44,"treasury-systems-architecture":46,"sap-treasury":46,"building-ai-products":47,"finance-systems-delivery":48},21,31,32,36,27,1787086597904]