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AI can help with tasks such as reading, drafting, classifying, summarizing, and interpreting information. It cannot, by itself, clarify what a business process is meant to achieve, fix confusing handoffs, make unreliable information dependable, or decide who is accountable. Before adding AI, identify the process problem, redesign the workflow where necessary, and then test whether AI can improve a specific step.
Start with the outcome, not the AI tool
Name the customer or business outcome the process should produce, identify its owner, and record how the process performs now. A goal such as “use AI to speed up approvals” is a technology direction, not a definition of success. Specify the result that matters—such as fewer errors, more predictable handling, or shorter cycle time—and determine how you will measure it in the current workflow.
Set a clear boundary around the process: what triggers it, where it ends, and which people or teams own the work. Without that boundary, it is easy to optimize one task while shifting delays or risk to another part of the business.
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Map how the work actually happens
Document the current workflow with the people who perform and oversee it. A useful map shows more than the official procedure: it captures the decisions, handoffs, information, exceptions, and controls that shape real work. APQC’s process management guidance and process mapping guidance offer practical ways to approach that work.
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- Trigger and endpoint: What starts the process, and what counts as completion?
- Roles and handoffs: Who does each step, who receives the work next, and who owns the result?
- Decisions and rules: What information guides a decision, and which rules or approvals apply?
- Information and knowledge: What records, systems, instructions, or expertise do workers need?
- Exceptions and rework: Where does work stall, return for correction, or require judgment outside the standard path?
- Controls and measures: What checks manage risk, and how is performance assessed?
Compare the map with actual cases. A documented workflow that omits informal workarounds or recurring exceptions is not a reliable basis for automation.
Find the cause before choosing a solution
Once the workflow is visible, distinguish among three different problems: a process-design failure, an information bottleneck, or a repetitive task that may be suitable for automation. They can coexist, but they call for different responses.
- Process-design failure: Unclear decision rights, unnecessary approvals, conflicting rules, or poorly assigned handoffs require redesign. AI may reproduce the confusion faster rather than remove it.
- Information bottleneck: Workers may spend time finding, reading, or consolidating information. AI could assist with some of those tasks, but the information still needs to be relevant, reliable, and governed.
- Repetitive task: A stable, well-defined step may suit traditional automation or AI support. Consider how much variation it has and what happens when an input falls outside the expected pattern.
AI is most plausibly a contributor to particular information or language tasks, not a replacement for process design. The educational chapter Business Applications of Artificial Intelligence and Machine Learning discusses AI in workflow contexts alongside human control, reliability, bias, and ambiguity. It is explanatory material, not evidence that a particular deployment will improve business results.
Compare the workflow options by their risks and needs
There is no universal winner between manual work, traditional automation, and AI support. Compare them for the specific step and its consequences, not by whether a tool is labeled “AI.” The following is a decision framework, not a benchmark or a claim that any option delivers a particular result.
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| Question | Manual workflow | Traditional automation | AI-supported workflow |
|---|---|---|---|
| What is addressed? | A person performs the task. | A defined task or rule is executed automatically. | AI supports a selected task, such as interpreting or generating information. |
| How is variation handled? | A worker can use judgment, subject to the process’s rules and authority. | Variation outside programmed rules may require a separate path. | AI may assist with variable information, but ambiguity and exceptions still need an explicit handling path. |
| What information is needed? | Workers need access to relevant records, instructions, and knowledge. | Rules and inputs must be defined for the automated step. | Relevant information and knowledge must be available, and output reliability must be evaluated. |
| Who has decision authority? | People act within assigned decision rights. | Automation executes specified rules; process owners define the authority and controls. | Define separately what AI can recommend, generate, decide, or execute, and where a person must review. |
| What happens on failure? | Workers need a way to identify errors and escalate unusual cases. | Exceptions and failed rules need a recovery or escalation path. | Set review, escalation, and intervention procedures for unreliable or out-of-scope outputs. |
| How is value assessed? | Measure the process against its baseline and intended outcome. | Measure the same outcome, including errors and exceptions. | Measure the same outcome alongside reliability, adoption, exceptions, rework, and relevant risks. |
Redesign the workflow and define AI’s authority
Fix unnecessary steps, clarify who owns decisions, and improve handoffs before deciding where AI belongs. Then specify its role in the future workflow. For every AI-supported step, decide whether the system may offer a recommendation, draft content, make a decision, or execute an action. These are different levels of authority and should not be left implicit.
- Identify which outputs require human review or approval.
- Define how uncertain, incomplete, or out-of-scope cases are recognized and escalated.
- Set out who can intervene, pause the workflow, or correct an output.
- Assign a person or role accountable for the process and its outcomes.
- Document the relevant risks, responsibilities, and controls, and involve the functions affected by them.
The OECD Due Diligence Guidance for Responsible AI recommends embedding responsible-AI due diligence in enterprise systems, documenting responsibilities and risks, and incorporating cross-functional feedback. ISO/IEC DIS 42105 is a draft guidance document, not a finalized standard; its surfaced guidance discusses human monitoring, intervention, governance, and training. Do not treat a draft as a settled compliance requirement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pilot in the real workflow before scaling
Test the redesigned process with real users and the operating conditions it will face. A demonstration on ideal examples cannot show whether the workflow handles ordinary variation, exceptions, or the consequences of a bad output.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Agree on success measures: Compare results with the baseline and targets tied to the business outcome. Choose measures relevant to the process, such as quality, cycle time, errors, rework, or compliance.
- Observe the whole workflow: Track what happens before and after the AI-supported step, including handoffs and cases sent for human review.
- Check reliability and exceptions: Record incorrect, incomplete, or uncertain outputs and whether the escalation path works.
- Assess adoption and risk: Find out whether people can use the process as designed and whether controls are operating in practice.
- Revise and retest: Update the workflow, instructions, permissions, and training when observed behavior exposes a gap.
APQC’s guidance on governing work performed by AI agents and readiness to scale AI agents addresses governance and scaling questions. Treat these as professional guidance rather than proof of outcomes for a particular organization. Scale only when the process is stable enough for the intended use, owners and controls are in place, and results meet the targets set for the pilot. Keep process maps and governance documentation current as the work changes.
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What success looks like
A sound AI-enabled process has a defined outcome, a workflow people recognize as accurate, clear decision rights, reliable information for the task, and a working path for review and exceptions. AI may help with a bounded step; the organization remains responsible for the process, its controls, and its results. No universal financial return or productivity improvement follows from adopting AI or redesigning a workflow: judge each effort against its own baseline and measured outcomes.
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