AI, machine learning, and automation are often discussed as interchangeable technologies. A better decision starts with the work to be improved, not the label attached to the solution.
Rules-based automation follows known steps
Conventional automation is a strong fit when inputs, decisions, and actions can be described reliably. Examples include:
- synchronizing approved records between applications;
- routing a request by customer or product;
- generating a report from structured data;
- notifying someone when a threshold is crossed; and
- copying validated order data into another system.
When clear rules solve the problem, they are usually easier to test and operate than a probabilistic model. Our workflow automation approach considers that option first.
Machine learning estimates from examples
Machine learning uses data to learn patterns for classification, prediction, ranking, or anomaly detection. It may be useful for forecasting demand, prioritizing leads, or identifying unusual activity.
The business still needs representative data, a measurable target, and a plan for monitoring performance. A model trained on poor or unrepresentative data can produce poor results at scale.
Generative AI works with less-structured information
Generative AI can summarize documents, extract details from notes, draft responses, and help people search approved internal knowledge. Its answers are probabilistic rather than guaranteed.
The appropriate control depends on the consequence of an error. A low-risk draft may need ordinary review. A decision involving money, access, health, employment, or customer commitments may require citations, validation, restricted actions, and explicit human approval.
The NIST AI Risk Management Framework is a useful authoritative reference for organizations defining how AI risks will be governed, measured, and managed.
Useful workflows often combine methods
Consider an incoming customer email. A dependable workflow might:
- use AI to classify the request and extract key details;
- use ordinary rules to validate the customer and route the work;
- retrieve approved account data through an integration;
- draft a response;
- require approval for higher-risk cases; and
- record the final action in the existing system.
AI is only one component. Permissions, integrations, business rules, logging, and exception handling are what make the workflow usable every day.
Five questions to ask before choosing
- What task are we improving? Name the workflow and the person responsible for it.
- How predictable are the inputs and decisions? Consistent work may need rules, not AI.
- What happens when the system is wrong? Consequence determines the control.
- What evidence is available? Machine learning needs appropriate examples; generative AI needs approved context.
- How will value be measured? Use a business result such as time saved, errors reduced, or faster response—not adoption of a technology.
The simplest dependable solution is usually the strongest one. Schedule a workflow fit call if you want help identifying the smallest useful starting point.