AI can help with specific tasks inside a business process, such as reading, drafting, classifying, or summarizing information. It cannot decide what the process should achieve, clarify who has authority, repair unreliable information, or make someone accountable. Start by diagnosing the workflow and defining a measurable outcome; then decide whether AI belongs in the redesigned process.
Start with the outcome, not the AI tool
Choose one process and state the business or customer outcome it is meant to produce. Identify where the process begins and ends, who owns it, and how it performs now. Without a baseline, a team cannot tell whether a change improved the work or merely moved effort somewhere else.
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Make success specific to the problem: for example, fewer handoff delays, less rework, more consistent decisions, or better handling of requests. Set the measures before changing the workflow. The appropriate measures depend on the process; there is no universal productivity or financial return to assume.
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Document the current workflow from trigger to outcome, not just the steps in a policy document. Validate the map with the people who perform the work and those responsible for it. Include:
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- Tasks, handoffs, decision points, rules, and approval rights.
- Information and knowledge workers need, where it comes from, and whether it is reliable.
- Exceptions, rework, delays, controls, and escalation paths.
- Process owners and the measures used to assess performance.
This map helps separate a process problem from an information bottleneck or a repetitive task that may be suitable for automation. If decision rights are unclear, data is unreliable, or exceptions have no owner, introducing AI does not resolve those underlying issues. APQC’s guidance covers governing work performed by AI agents and using AI in process workshops and future-state mapping.
Redesign roles and rules before choosing AI tasks
Fix unclear ownership, unnecessary handoffs, contradictory rules, and missing exception paths in the future-state workflow. Then identify specific steps where AI could help. Language and information work—such as summarizing material, drafting text, or classifying incoming requests—may be candidates. Ambiguous, exceptional, or knowledge-intensive work may still require human judgment. An open textbook chapter on AI-enabled workflow automation discusses these capabilities and limitations, but it is educational guidance, not a measured guarantee of business results.
Compare the options by what they do in the process, rather than by labels alone:
| Consideration | Manual workflow | Traditional automation | AI-supported workflow |
|---|---|---|---|
| Step addressed | People perform the work directly. | Rules automate defined, repeatable steps. | AI supports selected information or language tasks within the workflow. |
| Variation and exceptions | People can apply judgment, but handling may vary. | Works best when rules and inputs are defined; exceptions need a route. | May assist with variable information, but ambiguity and reliability still require controls. |
| Data and knowledge | Workers rely on available information and experience. | Inputs and rules must be sufficiently structured. | Relevant information and knowledge must be available and reliable enough for the task. |
| Decision authority | Assigned workers make decisions within their remit. | Execution follows configured rules and permissions. | Define whether AI may recommend, generate, decide, or execute; retain human authority where required. |
| Failure and controls | Errors need detection, correction, and accountability. | Controls should address incorrect rules, inputs, and execution. | Controls should address unreliable output, bias, ambiguity, review, and escalation. |
| Business outcomes | Assess against the process baseline. | Assess against the process baseline. | Assess against the same agreed process measures, including quality and exceptions. |
This is a decision framework, not a performance benchmark: the right option depends on the task, consequences of failure, available information, and required controls. The open textbook chapter also emphasizes human control over approvals, decisions, and accountability.
Set authority, review, and accountability
Before deployment, write down what the AI component is permitted to do and where a person must review or intervene. OECD guidance recommends embedding responsible-AI due diligence in enterprise systems, documenting responsibilities and risks, and incorporating cross-functional feedback.
- Specify whether the system can recommend, draft, classify, decide, or execute.
- Identify decisions or actions that require human approval.
- Define how uncertain cases, exceptions, and failures are escalated.
- Name the accountable process owner and the people responsible for oversight and intervention.
- Document relevant risks, controls, and training needs, and involve affected functions.
ISO/IEC DIS 42105 is a draft guidance document in the cited material, not a finalized standard. It discusses human monitoring, intervention, governance, and training; do not treat its draft status as evidence of a certification requirement.
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Pilot inside the real workflow and measure results
Test the redesigned process with the people, information, handoffs, and controls it will actually use. Agree on target outcomes and acceptable reliability before the pilot. Observe not only the AI output but also the surrounding work: adoption, review burden, exceptions, rework, cycle time, quality, and compliance where relevant.
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- Set a baseline and define success measures for the selected process.
- Run the AI-supported step within the redesigned workflow, with review and escalation in place.
- Record outcomes, errors, exceptions, overrides, delays, and effects on adjacent steps.
- Adjust the workflow, permissions, knowledge inputs, or controls when results show a gap.
- Scale only when results meet agreed targets and owners can sustain oversight.
APQC’s guidance on when an AI agent is ready to scale frames readiness as a process and governance question, not just a tool decision. Keep the process map and governance documentation current as the workflow changes.
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What a successful fix looks like
A sound deployment has a defined outcome, an understood workflow, clear decision rights, reliable information for the task, and named people accountable for exceptions and oversight. AI may then improve a particular step. If the process still has unclear ownership, broken handoffs, or no way to measure results, adding AI is likely to reproduce those problems in a new form.
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