A practical comparison

Does this workflow actually need AI?

Use explicit rules when the inputs and decisions are predictable. Consider AI for interpreting varied language or documents, with a person reviewing uncertain or consequential results.

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Gouache illustration of two practical approaches to organizing the same task
At a glance
What to considerAI-assisted workflowRules-based automation
InputsVaried emails, documents or natural languageConsistent fields and known formats
DecisionsProbabilistic suggestions that need evaluationExplicit conditions that can be traced
ReviewPeople check ambiguity and high-impact outputsPeople handle exceptions and rule changes
Operating costsModel usage, evaluation and monitoringHosting, integrations and rule maintenance
Good fitDrafting or extracting information for reviewRouting, calculations and repeatable checks
Document extractionProposed values from changing layouts or varied wording, with source evidenceKnown fields, stable templates and explicit matching patterns
ValidationSuggestions still need format, total, duplicate and source checksRepeatable checks can reject invalid data; exceptions still need an owner

A general comparison of approaches. Actual costs and capabilities depend on the tools and scope you choose.

AI vs. rules-based document extraction

For a consistent form with readable text or structured fields, explicit rules can read known field names, match a supplier ID and reject a missing total. OCR converts a scanned page into text; it does not decide which contract amendment controls a renewal date. Consider AI when you need proposed fields from changing layouts or varied language, such as finding a notice clause across differently written agreements. Keep the source text and page with every proposed value so a reviewer can check it.

A mixed workflow can use OCR for scans, rules for predictable checks and AI for proposed values that need interpretation. Rules can test whether a date has a valid format or an invoice total adds up, but those checks cannot establish that the extractor selected the right clause or amount. Hold conflicting and unsupported values for review before writing a business record.

Compare the cost per verified record

Give both approaches the same representative documents and compare their results against records your team has already checked. Include clear files, poor scans, changed layouts and missing fields. Track accepted records, missed or incorrect fields, exception handling and the time someone spends reviewing each result. Choose based on the work your team can safely finish with less total handling.

A rules-based extractor may need template maintenance when suppliers change their forms. An AI-assisted extractor adds model usage and evaluation, and may send more uncertain records for review. Include setup, processing, corrections, review and upkeep in the comparison. Reducing typing can improve margins when it reduces the total cost of completing the work; faster extraction alone does not show that improvement.

Talk through the tradeoffs.

Bring the tools, constraints and recurring task you want to improve.

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A few things people ask.

Can you help us implement AI?

Yes. We start with the work you want to improve, assess your data and tools, and scope an implementation with review, testing and a clear owner. Sometimes a simpler automation is the better fit.

Do you work with Toledo and Detroit Metro businesses?

Yes. We work with businesses in both metro areas. Use the contact form to tell us where you are and what you need. We will confirm call and meeting arrangements directly.

What should I bring to a first conversation?

Bring one example of the work, the tools involved and what is getting in the way. Please leave passwords, customer records and other sensitive information out of the contact form.

Keep the conversation going.

Tell us what you need to understand before your next step.

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