Traditional (rules-based) automation
Triggers, conditions, actions: routing, sync, schedules, approvals, notifications. Predictable because encoded. Changes come from rule/integration updates—not the model “noticing” something.
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Buyer guide
Rules and integrations when paths are known. AI when language, variation, or judgment blocks scale. Strong programs use both—sequencing beats picking a winner.
When deterministic steps suffice, when AI earns complexity, when hybrid is honest—approaches, not a vendor bake-off.
Workflow behavior, not marketing.
Triggers, conditions, actions: routing, sync, schedules, approvals, notifications. Predictable because encoded. Changes come from rule/integration updates—not the model “noticing” something.
Unstructured inputs; classify, draft, retrieve from a bounded corpus; assist inside policy. Probabilistic outputs need guardrails, review, citations—not a substitute for process ownership.
Input stability and error cost drive the choice.
Naming downsides keeps projects honest.
Predictable and testable. Brittle when reality drifts—each exception is a project. Maintenance: rules and integrations.
Handles variation at scale. Needs governance: data scope, eval, review for high stakes. Unbounded AI → shadow processes harder to audit than a sheet.
Often best ROI: AI for classify/extract/draft; rules once data is structured. Avoid “AI everywhere” when rules would be cheaper.
Rules need owners when policy changes; AI needs eval when data shifts. Pick the maintenance model your org can sustain and budget for it from day one.
Which column do your answers cluster in—and does order matter?
Highly stable, structured inputs favor automation. High language or format variance favors AI—or human triage until patterns emerge.
Low stakes may tolerate suggestions and review. High stakes demand deterministic gates, logging, and clear accountability—often automation with AI only upstream.
If the drag is integration and handoffs, fix plumbing first. If the drag is reading, classifying, or searching, AI may shorten the path to structured work.
Rules need owners when policies change. AI needs evaluation when data or behavior shifts. Pick the maintenance model your org can actually run.
Start narrow: automate what is explicit; add AI where variation blocks scale; measure before widening.
Undefined workflow → clarity first. Clear + repetitive → rules often default. Experts drowning in variation inside policy → bounded AI pilot with agreed eval.
Deeper: insight on AI vs traditional workflow automation (below) and use cases for combinations in practice.
Related reads on the same themes.
What you run today—where rules end, where AI might help, what we would skip for now.