The practical answer is to redesign the work before choosing the AI. Start with a business or customer result, map the complete path from request to outcome, and then decide which tasks should be automated, which should be performed by people using AI, and which require people alone. Remove unnecessary handoffs, connect AI output directly to the next useful step, assign a human owner, and measure the whole human-and-AI workflow against a baseline.
This avoids a common failure: AI makes one task faster while the queue, approval bottleneck, duplicate data entry, or unclear accountability remains untouched. The method below uses an illustrative customer-support process, but the same logic applies to procurement, product delivery, finance operations, HR services, and internal IT.
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Why adding an AI step often changes little
A team can deploy a chatbot, summarizer, classifier, or agent and still deliver the same result at the same speed. If a support specialist must copy a customer message into a separate tool, paste the answer into a ticket, wait for an approval, and then update three systems, one faster drafting step does not redesign the workflow. It may simply move effort to another person or create a new review queue.
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Tool-first projects also tend to preserve every historical handoff. A process that accumulated checks for an older risk profile keeps those checks even when the work, data, or accountability has changed. Employees then have to learn an additional interface without gaining decision rights, better information, or relief from the underlying bottleneck.
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The better question is not “Where can we add AI?” It is “What outcome is this workflow supposed to deliver, and how should work move to deliver it?” McKinsey describes the operating-model implication this way: “There is no single winning operating model structure; rather, the common thread is organizing work end to end, reducing unnecessary handoffs, and moving decisions and resources faster when priorities change.” McKinsey, 2026.
Start with the outcome and a real workflow
Choose an end-to-end result
Select a result that matters to the organization or its customers, such as resolving a service request accurately, paying a supplier on time, or moving an approved product idea into delivery. Define what “better” means before discussing models or licenses. A support team might choose faster resolution without an increase in reopened cases; a procurement team might choose shorter purchase-to-payment time while preserving policy compliance.
Keep the scope broad enough to include the work that determines the result. “Draft a reply” is a task. “Customer request received through verified resolution” is a workflow. The latter reveals queues, missing information, approvals, and exception handling that a task-level experiment would miss.
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Establish the baseline
Record current performance for the selected workflow. Depending on the outcome, useful baseline measures include median and 90th-percentile cycle time, first-pass accuracy, rework, number of handoffs, exception frequency, backlog age, service quality, employee workload, and total operating cost. Note the period, population, and exclusions so a later comparison is fair.
Also document the constraints: regulated data, contractual response times, systems that cannot be changed, approval requirements, and the kinds of errors that have financial, safety, legal, or reputational consequences. These constraints determine where autonomy is acceptable.
Illustrative workflow: customer request to resolution
Throughout this guide, imagine a service team handling warranty requests. The desired result is a correct resolution within two business days, with complete records and no unauthorized commitments. The current path is intake, identity verification, issue classification, policy lookup, draft response, specialist approval, customer reply, case update, and quality review.
The point of the example is not that every service team should automate warranty work. It shows the level of detail needed to redesign a process rather than attach an AI feature to one step.
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Capture the actual process, not only the official procedure
Observe several real cases from start to finish. For every task, record who acts, what information they use, which system contains it, what decision is made, what output is produced, and where the work waits. Include unofficial spreadsheets, copied notes, private messages, manual searches, and workarounds. Those hidden activities often explain why a formal process map looks efficient while employees experience constant friction.
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In the warranty example, the map may show that agents retype order numbers because the intake form does not pass them to the case system; policy specialists maintain a separate document because the approved policy database is difficult to search; and supervisors review every response even when only a small subset contains financial or legal risk.
Mark handoffs, decisions, and information gaps
Use a swim-lane or equivalent map with lanes for customers, frontline staff, specialists, systems, and approvers. Mark:
- Every handoff and the information transferred.
- Decisions and the rules or judgment behind them.
- Inputs that are missing, duplicated, stale, or inaccessible.
- Exceptions and the person who currently resolves them.
- Outputs that are copied into another system or used by a later team.
- Points where an error can create a material consequence.
Do not assume that an AI system can compensate for unavailable or contradictory data. If agents cannot reliably retrieve the order record or current policy, improving text generation will not produce a dependable resolution.
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After mapping, classify each task as AI alone under defined controls, people working with AI, or people alone. This is a design decision, not a permanent label. Reassess it when data access, model capability, error rates, workflow scope, or the level of autonomy changes.
| Work arrangement | Good candidates | Conditions and controls |
|---|---|---|
| AI alone | High-volume, repeatable actions with clear rules, limited consequences, and an easy way to contain or reverse errors. Examples include extracting fields from a standard form, routing a request, or checking that required fields are present. | Use approved data and permissions, confidence or validation checks, logging, rate limits, and a human escalation path. The output must not silently trigger an irreversible consequential decision. |
| People using AI | Work where AI can search, summarize, draft, compare, or suggest while a trained employee supplies context and makes or approves the decision. | Show source context, preserve the employee’s decision rights, make review effort explicit, and capture corrections so recurring failure modes can be fixed. |
| People alone | Ambiguous exceptions, sensitive conversations, novel situations, high-impact decisions, and judgments requiring accountability, empathy, or context not available to the system. | Give the person the information and authority needed to act. Do not force an AI step into the path when it adds delay or creates a misleading appearance of objectivity. |
Evaluate the task, not the marketing category
For each task, ask whether the rules are clear, the inputs are accessible and reliable, the work is repeatable, the context is stable, the consequences of an error are containable, and the review cost is lower than doing the task directly. Ask who is accountable if the output is wrong and whether that person can inspect the evidence used.
In the warranty example, AI might extract the order number, summarize the customer’s description, retrieve the relevant policy passages, and draft a response. A trained agent should verify identity, resolve contradictory records, decide whether an exception applies, and approve any refund or replacement outside standard limits.
Make the exception path visible
Write an explicit branch for cases that do not fit the normal rules. For example:
- If identity or order data is incomplete, pause the automated path and request the missing information.
- If policy sources disagree, route the case to a policy specialist and preserve both sources in the record.
- If the requested remedy exceeds the standard limit, require an authorized human approval.
- If the model cannot cite supporting policy text or confidence checks fail, prevent an outbound response and send the case to a trained agent.
- If a customer disputes the decision, route to the defined escalation team rather than restarting the same automated loop.
An exception route is part of the workflow, not a failure outside its design. Measure how often it occurs, how long it takes, and whether repeated exceptions indicate a missing rule, poor data, or an unsuitable allocation.
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Redesign the flow and handoffs
Remove work that no longer serves the outcome
Challenge every transfer, duplicate entry, and approval. If two teams review the same low-risk item, consider a risk-based check instead of a universal one. If a specialist is only locating a policy paragraph, provide that context to the frontline employee while keeping specialist authority for ambiguous cases. If a status update is copied manually, make the system of record update part of the same transaction.
Put AI output into the next useful step
An AI summary should arrive with the case, source passages, extracted fields, and recommended next action—not in a disconnected interface that requires copy-and-paste. The next worker should be able to verify what the system used, correct it, and continue the process without rebuilding context.
Business and technology staff should jointly test whether integrations preserve data lineage, permissions, latency, and error handling. A technically impressive prototype is not a redesigned workflow if employees still maintain parallel records or if the output cannot be audited.
Design a fallback
Specify what happens when the model, integration, data source, or identity service is unavailable. The fallback may be a manual queue, a simpler rules-based route, or a temporary pause with a service-level notification. Define who activates it, how queued work is recovered, and how customers or other teams are informed. Reliability includes graceful degradation, not only normal-path performance.
Assign ownership and safeguards
Name one accountable workflow owner
A human owner should be accountable for the outcome and for how AI operates within the workflow. This is not necessarily the person who manages the AI system. The owner must have authority to change steps, approve controls, stop the automation, and resolve conflicts between speed, quality, risk, and cost.
Define the control set
Document the following before expanding the pilot:
- Approved tools, models, data sources, and prohibited data.
- Role-based permissions for reading, writing, approving, and changing instructions.
- Validation rules, confidence thresholds, sampling, and mandatory human approvals.
- Escalation contacts and response times for exceptions, incidents, and customer complaints.
- Logs that retain inputs, outputs, source context, decisions, corrections, and version changes as appropriate.
- Monitoring for drift, unusual error patterns, bias, latency, cost, and unauthorized access.
- The person or committee allowed to change the workflow, and the review required for a material change.
Controls should evolve with scope. An assistant that drafts internal notes requires a different control profile from an agent that sends messages, changes records, approves spending, or acts across systems.
Separate recommendation from authorization
Where consequences are significant, let AI gather evidence or recommend an action while an authorized person decides. The approval screen should show the relevant source material, policy version, proposed change, and any uncertainty—not just a green checkmark. The approver remains accountable; clicking through an opaque recommendation is not meaningful oversight.
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Use the people who perform the process as designers
Frontline employees know where records are incomplete, which exceptions recur, and which “temporary” workarounds have become permanent. Include representatives in process mapping, task allocation, prototype evaluation, and incident review. Ask them to identify ways the redesigned workflow could shift work to another team, increase emotional labor, or remove the context needed for good judgment.
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Involvement is also a safety mechanism. Employees should have a clear channel to report an incorrect output, unsafe recommendation, privacy concern, or workload transfer without being penalized for stopping the automation.
Train by role and scenario
Training should cover the new process, not merely prompt-writing. A frontline worker may need to verify citations, correct extracted data, and escalate an exception. A supervisor may need to interpret quality dashboards and approve workflow changes. A system administrator may need to manage permissions, logs, and fallback operation. Practice normal cases, ambiguous cases, outages, and deliberate bad inputs.
McKinsey’s change-management article reports that 48 percent of US employees said they would use generative-AI tools more often with formal training, while 45 percent said they would use them more frequently if the tools were integrated into daily workflows. These are employee survey responses from its January 2025 Superagency in the workplace research, not proof that training or integration alone produces business results. Read the source and its stated context.
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Employees need a credible answer to what happens if the workflow requires fewer minutes per case. Possible choices include handling more demand, improving service, reducing backlog, lowering workload, developing new capabilities, funding growth, or reducing expense. Decide the intended use before the pilot and revisit it when gains are verified. A promise of efficiency without a capacity plan can encourage hidden work, rushed reviews, or resistance.
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Choose a bounded pilot
Select a workflow with a clear outcome, credible data access, manageable investment, and a reversible scope. Define inclusion and exclusion criteria, the accountable owner, the baseline period, the control group or comparison method where practical, the pilot duration, and the conditions that pause the test.
Run the redesigned path on real cases rather than relying only on synthetic demonstrations. Sample outputs for quality, observe where employees intervene, and record every exception and workaround. Compare the workload created by review, correction, and escalation with the time saved by automation.
Use outcome measures, not activity counts
Prompt volume, logins, model calls, or the percentage of employees who opened a tool show adoption, not value. Select measures that match the chosen outcome:
- Speed: median and tail cycle time, queue age, and time waiting at each handoff.
- Quality: accuracy, first-pass resolution, rework, reopened cases, policy violations, and customer outcomes.
- Flow: throughput, exception frequency, escalation rate, and number of manual transfers.
- People: review time, workload distribution, training completion, confidence to stop unsafe automation, and skill development.
- Economics: total cost of people and AI together, including implementation, integration, maintenance, monitoring, review, and incident handling.
Compare these measures with the baseline under equivalent demand and note changes in case mix. A faster average with more severe errors or a lower frontline workload accompanied by a new specialist backlog is not an unqualified improvement.
Interpret external figures carefully
McKinsey’s 2026 article reports that 80 percent of respondents said AI improved their individual productivity, 37 percent reported positive EBIT impact, 6 percent qualified as AI high performers, and 11 percent of organizations reached its “reinvention” horizon. These are survey figures and categories from that publication, not a causal estimate of what redesign will deliver for a particular team. Individual time savings become organizational value only when the operating model changes how work, decisions, and capacity move. See McKinsey’s definitions and methodology.
The Conference Board’s 2026 framework is based on 16 primary research sessions held from February through July 2026 with more than 75 senior HR, talent, and AI leaders, plus council sessions, a member workshop, and a governed workflow simulation platform used by more than 100 members. Its five stages are to start with desired outcomes, redesign and allocate tasks before deployment, build infrastructure and governance, measure combined human-agent work, and decide where verified gains go. It is a practitioner framework based on qualitative and member research, not a controlled productivity trial. Read the framework.
A McKinsey 2026 product-development survey summary identifies workflow or process redesign among the top two enablers reported by teams experiencing twofold productivity gains. That is an association reported by surveyed teams, not evidence that redesign alone caused the gains; the cited survey was conducted in May 2026. View the source summary.
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Another McKinsey 2026 report summary cautions that agents should not sit at the center of every workflow because they still lack human capabilities and require oversight. The summary is a limited supporting source, so use it as a caution rather than a quantified benchmark. Read the available report link.
Compare redesign options with one consistent baseline
Teams often face several plausible designs: automate intake and routing, give agents an evidence panel and drafting assistant, or redesign the approval policy and exception route first. Compare them using the same outcome definition and baseline rather than choosing the option with the most visible AI.
| Decision axis | Questions to answer |
|---|---|
| Outcome impact | Which design is most likely to improve the selected customer or business result, and how will that improvement be observed? |
| Feasibility | Are the required data, permissions, integrations, skills, and change capacity available now? |
| Risk and reversibility | What happens if the output is wrong, and can the change be paused or reversed without losing records? |
| Human review | Where is review mandatory, how long does it take, and who handles exceptions? |
| Flow and quality | Does the design reduce waiting and rework while preserving accuracy and service quality? |
| Maintenance | Who updates data connections, rules, prompts, policies, evaluations, and monitoring? |
| Employee effects | Does work become more understandable and manageable, or does it shift to another team or create surveillance pressure? |
| Accountability | Is one person clearly responsible for the result, controls, and escalation decisions? |
| Full cost | What is the combined cost of AI, integration, human review, training, support, incidents, and change management? |
Common failure modes and corrections
Failure: optimizing a local task
Symptom: drafting is faster, but resolution time is unchanged because approvals and missing data still dominate. Correction: measure the end-to-end outcome and redesign the queue, handoffs, and decision rights around it.
Failure: putting an agent at the center by default
Symptom: employees must route every case through an agent even when a direct human or rules-based path is clearer. Correction: allocate only suitable tasks to AI and preserve human paths for ambiguity, high consequence, and sensitive interactions.
Failure: treating adoption as value
Symptom: dashboards celebrate usage while rework, customer complaints, or total cost rise. Correction: track the selected outcome, quality, workload, exceptions, and combined human-agent cost against the baseline.
Failure: hiding the exception path
Symptom: unusual cases circulate through informal messages or sit in a queue with no owner. Correction: define triggers, escalation authority, service targets, evidence retention, and recovery steps before launch.
Failure: training only on the interface
Symptom: employees can operate the tool but do not know when to challenge an answer, verify sources, or stop automation. Correction: train by role using normal, ambiguous, adversarial, and outage scenarios, with manager support after launch.
A small-pilot checklist
- State one measurable business or customer outcome.
- Map the real end-to-end workflow, including unofficial workarounds.
- Record a baseline for speed, quality, workload, exceptions, and cost.
- List tasks, decisions, inputs, systems, handoffs, and consequential errors.
- Assign each task to AI alone, people using AI, or people alone, with reasons.
- Design the normal path, exception path, fallback, and recovery process.
- Name the accountable human owner and define decision rights.
- Set permissions, approved data, validation, review, escalation, logging, and monitoring controls.
- Involve employees who perform the work and provide role-based training.
- Choose how verified capacity gains will be used.
- Run on real work, compare with the baseline, and include the full cost of human-and-AI work.
- Pause, revise, or expand only when the outcome, quality, risk, and employee effects support that decision.
The central discipline is simple: redesign the movement of work first, then use AI where it improves that movement. As The Conference Board puts it, “The technology cannot decide that allocation; leaders must.” The Conference Board, 2026.
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