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Map how work actually gets done, find where it waits or loops back, and remove unnecessary steps before choosing technology. Enterprise AI is most useful when it supports a clear, measurable process; putting it into an unclear workflow can encode or amplify the existing problems.
Start by defining the workflow and its outcome
Choose one consequential process—or a well-bounded slice of one—rather than trying to map an entire department at once. Specify the event that starts the work, the event that finishes it, who receives the output, who owns the process, and what a successful outcome means.
Include both the people who perform the work and those who depend on its output. Their perspectives help reveal whether the process is meeting its intended purpose and where a local improvement might create problems elsewhere. Microsoft’s business process management guidance recommends setting objectives and involving stakeholders in assessment and process design.
Map what people really do
Document the current workflow as it operates, not just the official procedure. Include meaningful activities, decisions, roles, systems, information inputs and outputs, and handoffs. NIH’s process mapping guidance describes maps as a way to show inputs, activities, handoffs, decisions, and outputs. Microsoft likewise advises mapping current reality rather than what is merely documented or intended in its agentic AI maturity guidance.
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Ask the people doing the work where items wait, where reminders are needed, what causes a return or correction, where information is copied between systems, and which exceptions require escalation. Informal workarounds and manual coordination belong on the map too: omitting them can make a process appear simpler than it is.
Use interviews or a facilitated workshop to surface tacit work and decisions. If systems capture suitable event data, process mining can add evidence about route variants and observed timing. Treat that evidence as a complement to staff knowledge: event records need interpretation, and they may not represent manual work or explain why a path occurred. Verify current tool prerequisites and licensing before adopting a process-mining product.
Spot and validate likely bottlenecks
Look for evidence that work is constrained, rather than assuming the most visibly slow task is the cause. A bottleneck matters when it materially limits the end-to-end outcome.
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- Work accumulates before a particular role, approval, or system.
- Items wait for repeated reminders or move through several handoffs.
- Queues or completion times vary substantially across routes or teams.
- Work is rejected, corrected, re-entered, or escalated repeatedly.
- People duplicate data entry, reports, or coordination activities.
- Exceptions, quality failures, or incomplete transactions are common.
Use process records and discussion with process owners to test each hypothesis. A long task may not be the limiting constraint if it does not hold up the rest of the workflow; conversely, a short approval step can create a substantial queue. There is no universal numeric cutoff for declaring a bottleneck. Establish a baseline and a target appropriate to the process instead.
Choose a discovery method that fits the work
Facilitated mapping and process mining answer different questions. Mapping helps make decisions, handoffs, exceptions, and undocumented workarounds visible. Process mining can help examine routes, variants, and timing when systems capture usable event data. Neither method guarantees a complete picture on its own.
| Approach | Useful when | Check before relying on it |
|---|---|---|
| Facilitated process mapping | You need to understand human decisions, manual steps, handoffs, exceptions, or informal workarounds. | Include the staff who perform the work and validate the map against actual cases. |
| Process mining | Relevant systems capture event data and you need to inspect observed routes, variants, or timing. | Check event-data coverage and quality, manual-work gaps, access and privacy constraints, required skills, and tool prerequisites. |
Choose based on the question you need to answer, available data, coverage of manual work, and ability to validate findings with staff. Not every organization needs process mining or a particular software product.
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Eliminate waste before automating
For each step, ask what value it provides, who needs it, and what would happen if it stopped. Check whether law, policy, contractual obligations, security, quality, or delegated authority requires it before removing a control.
The U.S. General Services Administration’s three pillars of EOA guidance recommends critically examining activities that are no longer necessary, add little value, or are redundant. Apply that principle in order:
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- Optimize: Simplify necessary steps, clarify ownership, standardize where useful, and improve communication between roles.
- Automate: Consider technology for repetitive manual work after the process has been examined and the required controls are clear.
Confirm proposed control changes with the appropriate legal, policy, security, quality, or process owner. A step that feels bureaucratic may still protect a legitimate decision right or obligation.
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Prioritize redesign and define success measures
Rank friction by its effect on speed, cost, quality, or experience; how often it occurs and how much effort it consumes; and its strategic importance. Do not optimize one team’s local step if doing so makes the complete workflow worse.
Before introducing technology, select a small set of meaningful measures and record the baseline. Depending on the process, useful measures include:
- End-to-end cycle time or wait time at a particular handoff.
- Cost per transaction and process completion rate.
- First-pass quality, exception rate, or escalation volume.
- User or customer experience, collected consistently.
Microsoft’s business strategy guidance recommends defining the problem and outcome, choosing value signals, establishing a baseline, and tracking change. Compare like with like after an intervention—for example, the same type of transaction and comparable operating conditions. Do not treat a measure as proof of improvement if the underlying workload or case mix has changed.
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Decide whether enterprise AI fits the improved process
Consider AI orchestration only when the process is clear enough to describe, valuable enough to justify its complexity, and measurable enough to evaluate. Confirm that ownership and governance are established and review the process’s data and system access, identity and permissions, integrations, accountability, audit requirements, privacy, security, approvals, and escalation paths.
Microsoft cautions that orchestration can amplify dysfunction when a process is unclear, disputed, or inconsistent across teams in its guidance on enterprise AI orchestration. Resolve that ambiguity before encoding the workflow into an agent. AI is one possible option in the improvement sequence, not the automatic destination.
Define the agent’s boundaries before launch:
- What may it retrieve, reason about, initiate, or change?
- Which decisions require human approval, and who provides it?
- How are exceptions routed, and what triggers escalation?
- How can a person review, correct, or override an outcome?
- Which data and systems are necessary, and what access is appropriate?
Match autonomy to the process’s maturity and risk. More consequential actions require clear accountability and appropriate human oversight.
Pilot, compare, and iterate
Test the redesigned process with a bounded proof of concept or pilot before expanding it. Involve the people doing the work, collect their feedback, and compare results with the baseline using the measures selected in advance. Microsoft’s BPM guidance describes modeling and testing workflows and starting implementation with a small group; its AI maturity guidance recommends using evidence to decide whether to scale, improve, or retire an agent.
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