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AI and automation: where each fits in a real business process

Automation follows defined rules and routes work between systems. AI can help with a bounded information task inside that route. Both need a named owner, source record and exception path.

Use the practical checklist Discuss an AI-assisted workflow

Tell the difference in plain English

The terms overlap in conversation, but they do different jobs inside an operating process. This is Durward's practical distinction, not a claim that five categories solve every situation.

  1. 01

    Workflow automation follows a defined route

    It can move a known record, create a task, send an approved update or change a status when the trigger and rule are clear.

  2. 02

    Business process automation connects the whole route

    It considers people, source records, hand-offs, controls, exceptions and the result the owner must be able to inspect, not merely an app-to-app connection.

  3. 03

    RPA works with a user interface

    It can repeat a structured, authorised screen-based task when there is no suitable integration route. It needs especially careful maintenance when the interface changes.

  4. 04

    AI assistance prepares information that needs interpretation

    It can help extract fields, classify material, organise research or draft a first response when a person can inspect and correct the output.

  5. 05

    Agentic workflows need the narrowest safe boundary

    A more autonomous workflow may choose between bounded tools or routes. It is not a person-like worker and should not be given open-ended authority over consequential decisions.

Choose the right job for AI

These are illustrative process patterns. AI earns its place when a rule alone cannot do the information work and the business can still define what acceptable output looks like.

  1. 01

    Extract from a known source

    Use it to prepare structured fields from approved documents or messages, then compare the output against the source before it affects a workflow.

  2. 02

    Classify or prioritise a queue

    Use it to prepare a category, summary or suggested next route for a person to review, not to make an unattended consequential decision.

  3. 03

    Draft material for review

    Use it to produce a starting draft based on approved inputs, then give the owner a clear review and edit point before anything is sent or applied.

Design the controls around the task

The useful work is in the surrounding route: inputs, checks, ownership and what happens when a case does not fit.

  1. 01

    Set the authorised input boundary

    Identify the source material, field set and access needed for the defined test. Do not bring in more business data simply because it might be useful later.

  2. 02

    Define a review standard

    Agree what the reviewer will check, what evidence they need to see and when an output should be treated as unclear rather than acted on.

  3. 03

    Build a recovery route

    Make it easy for a person to correct, pause or route an exception. The workflow should remain understandable when AI output is not usable.

Test the workflow, not just the prompt

An AI task is useful only if it improves the wider business route without removing the visibility or control the team needs.

  1. 01

    Test real but bounded cases

    Use a small authorised set of inputs and inspect the source, output, review step and exception route together.

  2. 02

    Measure practical usefulness

    Review whether the prepared output is usable, whether the route reaches the right person and where the team still has to rework the result.

  3. 03

    Decide whether to extend it

    Keep, adapt or stop the route based on the evidence from the test. The responsible owner makes that decision, not the AI component.

A quick AI and automation checklist

Use this to decide whether you have a bounded information task or a broader process problem to solve first.

  1. 1.Can a defined rule or existing platform feature complete the task without AI?
  2. 2.If not, what exact information task would AI prepare?
  3. 3.What approved source material and fields would the task use?
  4. 4.Who reviews the output and what makes it acceptable?
  5. 5.What happens when the output is incomplete, ambiguous or wrong?
  6. 6.What operational outcome would prove the test worth continuing?

Take the next step from the process

The right route depends on whether you need a whole workflow, a decision framework or an AI task assessment.

Bring one information task to the conversation

Share the source material, expected output, review point and process outcome. We can assess the non-AI route as well as the AI-assisted one.

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