A common mistake is treating AI as the automation and everything before it as manual work.<\/p>\n
ERPs have automated things for decades. Reordering rules. Approval routing. Recurring invoices. Scheduled reports. Follow-up sequences.<\/p>\n
That is rule-based automation<\/strong>, and for a large share of what businesses need, it is still the better answer.<\/p>\n The question is not which is more advanced. It is which fits this particular task<\/strong>.<\/p>\n Rule-based automation does what you told it.<\/strong><\/p>\n AI-based automation does what usually works.<\/strong><\/p>\n Everything else follows from that.<\/p>\n A rule is written by a person. It is explicit, predictable and auditable. It handles exactly the cases it was written for and fails on everything else.<\/p>\n A model learns from examples. It handles cases nobody anticipated, and it is right most of the time rather than always.<\/p>\n FIGURE 1: THE CORE DIFFERENCE<\/p>\n Rules<\/p>\n Patterns<\/p>\n Five situations. Notice how many ordinary business tasks fall here.<\/p>\n When the logic is definite.<\/strong> “Orders over 50,000 need director approval.” There is no judgement. A rule is correct every time and a model would be worse.<\/p>\n When you must explain it.<\/strong> Anything an auditor, regulator or customer may question. “Because the rule says so” is an answer. “The model determined it” is not.<\/p>\n When errors are expensive.<\/strong> Payment thresholds, credit limits, tax treatment. You want predictable, not usually-right.<\/p>\n When you have little data.<\/strong> A model needs examples. A rule needs one person who knows the policy.<\/p>\n When the rule changes by decision.<\/strong> Your approval limit changes because management decided. A model learning from history would still be applying the old one.<\/p>\n Four situations.<\/p>\n When the variation is unbounded.<\/strong> Supplier invoices come in thousands of layouts. Writing rules for each is impossible; recognising an invoice total is exactly what a model does well.<\/p>\n When the pattern is real but nobody can articulate it.<\/strong> Which leads convert. Which invoices get paid late. There is a pattern; nobody can write it as a rule.<\/p>\n When it should adapt.<\/strong> Demand shifts. A fixed reorder minimum does not. A forecast does.<\/p>\n When the input is unstructured.<\/strong> Documents, free text, images. Rules need structure.<\/p>\n FIGURE 2: WHICH TOOL FOR WHICH JOB<\/p>\n Use rules when<\/p>\n Use AI when<\/p>\n The practical answer is rarely one or the other. It is usually a chain.<\/p>\n A worked example \u2014 processing a supplier bill:<\/strong><\/p>\n AI<\/strong> reads the scanned document and extracts vendor, dates, amounts and lines. Rules could not handle the variety of layouts.<\/p>\n Rules<\/strong> check it. Does the total match the purchase order within tolerance? Is the vendor approved? Is the amount within the approval limit? These are policies and they must be exact.<\/p>\n AI<\/strong> flags it if the amount is unusual for this vendor. No rule captures “unusual” well.<\/p>\n Rules<\/strong> route it. Over the threshold, it goes to a director. That is policy.<\/p>\n A person<\/strong> reviews and posts.<\/p>\n Each part uses the right tool.<\/strong> Extraction where variety is unbounded, rules where the policy is definite, a person where money moves.<\/p>\n FIGURE 3: THE TWO WORKING TOGETHER<\/p>\n AI reads<\/p>\n Rules check<\/p>\n AI flags<\/p>\n A person posts<\/p>\n Using AI where a rule would do.<\/strong> More expensive, less predictable, harder to explain. If your approval limit is 50,000, write a rule. Do not train a model to guess where the limit is.<\/p>\n Using rules where the variety defeats them.<\/strong> The classic version is invoice processing built as a template per supplier. It works, then a supplier changes their layout, and you are writing rules forever.<\/p>\n A quick test:<\/strong> if you can write the logic down in a sentence or two, use a rule. If you would need pages of exceptions and still miss cases, use AI.<\/p>\n Two things.<\/p>\n Rules also rot.<\/strong> A reorder minimum set three years ago is still running with last year’s demand. A rule is predictable, not correct \u2014 it keeps doing what it was told even when what it was told is outdated.<\/p>\n Review your rules yearly.<\/strong> The same discipline you would apply to anything else.<\/p>\n Rules are easier to audit and easier to forget.<\/strong> Both are true. Nobody questions a rule that has been running for years, which is exactly why it should be checked.<\/p>\n It needs data.<\/strong> Not just any data \u2014 enough of it, clean, with outcomes recorded. Most disappointments trace back to this rather than to the model.<\/p>\n It cannot explain itself in detail.<\/strong> For an operational nudge, fine. For anything you must defend, use a rule.<\/p>\n It changes.<\/strong> Retrained on new data, it may behave differently. A rule does not drift.<\/p>\n Five questions.<\/p>\n Can I write the logic in a sentence?<\/strong> Yes \u2014 rule.<\/p>\n Must I explain the decision?<\/strong> Yes \u2014 rule.<\/p>\n Is the input structured or unstructured?<\/strong> Unstructured \u2014 AI.<\/p>\n Do I have hundreds of examples with recorded outcomes?<\/strong> No \u2014 rule.<\/p>\n Does an error cost real money?<\/strong> Yes \u2014 whichever you use, keep a person on the commit.<\/p>\n Rules and AI are both automation, and they suit different problems.<\/p>\n Rules for definite policy, explainability and expensive errors. AI for unbounded variation, patterns nobody can write down, and unstructured input.<\/strong><\/p>\n Most good systems use both \u2014 AI to read and flag, rules to check and route, a person to commit.<\/p>\n And review your rules yearly. Predictable is not the same as correct.<\/p>\n Not sure whether your automation problem needs a rule or a model?<\/p>\n Get in touch. We will look at the specific task \u2014 and quite often the answer is a rule you can write this afternoon.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":" Both are automation A common mistake is treating AI as the automation and everything before it as manual work. ERPs have automated things for decades. Reordering rules. Approval routing. Recurring invoices. Scheduled reports. Follow-up sequences. That is rule-based automation, and for a large share of what businesses need, it is still the better answer. The […]<\/p>\n","protected":false},"author":3,"featured_media":9220,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-9219","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"\nThe difference in one line<\/h2>\n
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Where rules win<\/h2>\n
Where AI wins<\/h2>\n
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Most systems need both<\/h2>\n
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The mistake in each direction<\/h2>\n
What people forget about rules<\/h2>\n
What people forget about AI<\/h2>\n
Deciding for a given task<\/h2>\n
The short version<\/h2>\n