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The best business processes for AI are not the most fashionable or technically impressive. They are owned workflows with a measurable outcome, usable data, enough repetition to justify the effort, and controls that keep errors from causing unacceptable harm. Start by mapping the work as it is actually performed, establish a baseline, and compare value, readiness, implementation effort, and risk. Pilot the smallest useful process slice with explicit acceptance and stop criteria before expanding.
What makes a process a good AI candidate?
A candidate is a specific workflow or process step, not a department-wide ambition such as “use AI in operations.” It should have a person accountable for the result, a clearly described current state, and an outcome that can be measured against today’s performance. IBM’s AI Center of Excellence guidance identifies a clear owner, defined workflow, sufficient data, measurable goals, and a realistic production path as characteristics of a high-value use case.
Good candidates often combine four conditions:
- Visible friction: employees spend substantial time searching, classifying, summarizing, checking, or moving information; cases wait in queues; or rework and handoffs are common.
- A measurable improvement: the organization can track cycle time, backlog, error rate, cost per case, service-level performance, or another outcome that matters.
- Accessible, appropriate data: the inputs exist, can be accessed by the system, are sufficiently accurate and representative, and may properly be used for this purpose.
- A safe operating model: a trained person can review or override outputs where needed, and mistakes can be detected, corrected, or rolled back.
AI is only one possible intervention. A fixed rule, better form, redesigned approval path, conventional automation, or removal of an unnecessary step may solve the problem more reliably and cheaply. The selection question is therefore not “Where can we put a model?” but “Which intervention can improve this owned process while preserving its quality, legal, security, and service requirements?”
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Start with a process inventory, not a technology demo
Create an inventory of concrete workflows and their steps. Broad labels such as “customer service” hide dozens of different activities with different data, risks, and owners. Record enough detail to compare candidates consistently.
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| Inventory item | What to record | Why it matters |
|---|---|---|
| Process and step | The precise start and end points, including the step you might change | Defines the pilot boundary and prevents an untestable “AI transformation” project |
| Owner | The person accountable for performance, controls, and operational decisions | Provides authority to set targets, resolve exceptions, and accept or reject the result |
| Participants | Teams, roles, customers, suppliers, and approvers involved | Shows where training, permissions, and handoff changes will be required |
| Systems | Applications, queues, databases, files, APIs, and manual tools used | Reveals integration effort and where evidence of an action will be stored |
| Inputs and outputs | Documents, messages, records, decisions, and downstream actions | Identifies data formats, unstructured information, and possible automation points |
| Volume and timing | Cases per day or month, seasonality, peak loads, and service deadlines | Indicates opportunity scale and whether performance is stable enough to evaluate |
| Exceptions | Common deviations, missing information, escalations, and failure paths | Prevents a design that works only on the happy path |
| Pain points | Delays, rework, errors, duplicated entry, search time, and employee or customer complaints | Connects an intervention to a real business outcome |
Interview the people who perform and supervise the work. The documented procedure is often different from the actual path. Process mining can use system logs and other digital footprints to show the routes cases really take, where they wait, how often they loop back, and which variants consume the most time. This is useful when handoffs or rework are suspected but not visible in policy documents. Treat the resulting map as evidence to validate with process owners, not as a complete description of work that happens outside recorded systems.
Define the outcome before selecting an AI technique
Write a one-sentence opportunity statement using this pattern:
For [process or user], improve [measured result] while maintaining [quality, service, safety, or control requirement].
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Choose a small set of measures that describe both benefit and protection against unintended degradation.
| Measure type | Examples | Question to answer |
|---|---|---|
| Speed and capacity | Cycle time, queue age, throughput, first-response time | Does work move faster or allow the team to handle more demand? |
| Quality | Error rate, rework, completeness, accuracy against a reviewed sample | Is the result correct and usable, not merely quick? |
| Cost and effort | Cost per case, employee minutes, overtime, manual touches | Is effort reduced after including review and exception handling? |
| Service and experience | Service-level attainment, abandonment, customer or employee effort | Does the change improve the experience that the process exists to provide? |
| Risk and control | Unauthorized actions, escalation misses, harmful outputs, audit completeness | Are control requirements still met under normal and unusual conditions? |
Measure the current process before estimating benefits. Do not insert a generic “AI saves 30 percent” assumption: the available evidence does not establish a universal savings rate. Build any forecast from your own volume, time, error, and cost data, and label assumptions separately from observed results.
Rank #2
Break the workflow into task types
AI may be suitable for one step while being a poor fit for the next. Decompose the process and label each step by the kind of work it performs.
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Rules-based work
If the logic is stable, explicit, and exact—such as checking a field against a fixed threshold or routing by a known code—a conventional rule or workflow may be preferable. It is easier to test, explain, and maintain. Introducing a probabilistic model where a deterministic rule already works can add failure modes without adding value.
Information retrieval and synthesis
Searching many documents, extracting relevant passages, or preparing a draft summary can be a plausible AI-assisted step when the source material is accessible and current. Define which sources are permitted, how citations or evidence are retained, and what a reviewer must verify. A fluent summary is not proof that the underlying information was retrieved correctly.
Prediction and classification
Forecasting demand, prioritizing a queue, or classifying incoming cases may benefit from patterns in historical data. Establish the target label, the time period represented, the cost of false positives and false negatives, and whether outcomes are measured consistently. Test performance across relevant segments rather than relying on one aggregate accuracy number.
Generation
Drafting a response, report, code fragment, or internal explanation can reduce preparation time when a person remains responsible for checking facts, tone, permissions, and policy compliance. Limit what the system may send or change automatically, especially when an incorrect statement is difficult to retract.
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Keep trained human involvement where the step requires contextual judgment, weighs competing interests, affects a person’s rights or livelihood, or triggers an irreversible action. Specify what information the reviewer sees, how much time is available, what authority they have to override the output, and where uncertain cases are escalated.
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For every AI-assisted step, mark whether the output is advisory or can cause a system action. The latter needs stronger permissions, logging, testing, and rollback than a draft that a person may freely edit.
Check data and technical readiness
A promising process can fail because the necessary data is missing, inaccessible, unreliable, or inappropriate to use. Perform this check before choosing a model or vendor.
- Availability: Are the required records present for the full process period, including exceptions and rejected cases?
- Access: Can the proposed system retrieve the data through approved interfaces and permissions without creating uncontrolled copies?
- Quality: Are fields complete, current, consistently defined, and free of duplicates or systematic errors?
- Representativeness: Do historical examples cover the customers, regions, products, languages, and unusual cases the system will encounter?
- Use authority: Is the organization allowed to use the data for this purpose, and are consent, contractual, confidentiality, retention, and jurisdiction requirements satisfied?
- Security: Can sensitive information be protected in transit, during processing, and in logs? Can access be limited by role?
- Evidence: Can inputs, outputs, model or prompt versions, reviewer actions, and final decisions be retained for investigation and audit?
- Monitoring: Can the organization detect data drift, quality decline, unusual output patterns, and changes in process volume?
NIST procurement guidance highlights consent and data use, confidentiality and integrity, accuracy criteria, bias testing, user training, transparency, and maintenance. Its procurement material is guidance rather than a binding rule for every organization, and one of the cited documents is older; use it as a structured set of questions, then apply the laws, contracts, and policies that govern your particular use.
Compare value, feasibility, and risk in one scorecard
Do not rank candidates on expected savings alone. Use a consistent scorecard with weights chosen by your organization. A high-value process with unusable data or unacceptable consequences should not outrank a slightly smaller opportunity that can be operated safely.
| Dimension | Assessment questions | Evidence to collect |
|---|---|---|
| Expected value | What measurable change is plausible in time, cost, quality, experience, or decision support? | Baseline metrics, pain-point evidence, and a documented benefit hypothesis |
| Opportunity scale | How often does the process run, and how much work do the candidate steps consume? | Actual volumes, seasonality, minutes per case, and queue data |
| Process stability | How standardized is the work? How many variants and exceptions occur? | Process map, logs, interviews, exception counts, and documentation quality |
| Data readiness | Are data access, quality, representativeness, permission, and evidence retention adequate? | Data inventory, sample review, access assessment, and legal or policy review |
| Implementation effort | What integration, security, training, operating ownership, and support are required? | System interfaces, identity model, staffing plan, test plan, and maintenance estimate |
| Impact and reversibility | What happens if the output is wrong or late, and can the result be corrected? | Impact analysis, rollback design, affected-person analysis, and incident scenarios |
| Oversight fit | Can a qualified person review outputs with enough information, time, authority, and escalation support? | Reviewer workload, confidence thresholds, escalation route, and training requirements |
Use a simple local scale—such as low, medium, and high—for each dimension, or assign numerical weights if your governance process requires them. The numbers are a prioritization aid, not an objective truth. Record the evidence and uncertainty behind every rating so that a score cannot hide a weak assumption.
Risk should influence the architecture and controls. IBM’s risk-focused guidance argues that consequential or difficult-to-reverse decisions need stronger explainability, traceability, and control than low-impact assistance. NIST’s procurement considerations likewise point to effects on core and business functions, job impact, data quality, and the level of human checking when deciding the scrutiny and safeguards a system needs.
Rank #4
Recognize promising patterns—and their limits
High-volume document and message handling
Repeated extraction, classification, routing, or drafting can expose a measurable queue and a large amount of unstructured information. Start with a bounded document type and a clear review rule. Test unreadable scans, missing pages, conflicting fields, multilingual content, and attachments rather than evaluating only clean examples.
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Process mining and log analysis can reveal where cases wait, loop between teams, or depart from the expected route. AI might assist with triage or summarization, but the underlying issue may be a missing permission, an unnecessary approval, or an overloaded team. Fixing the process can deliver more value than adding prediction.
Customer and employee assistance
Retrieval and drafting can help users find policy or account information and prepare a response. Define the approved knowledge sources, escalation triggers, privacy boundaries, and whether the system may act or only suggest. Track resolution quality and repeat contacts, not just response speed.
Forecasting and planning
Forecasting can support staffing, inventory, or demand planning when historical data and the decision context are stable enough to evaluate. Identify what happens when the forecast is wrong and retain a human planning step where a change would create shortages, unsafe conditions, or major financial exposure.
Inspection and quality support
Image or sensor-based inspection may be useful where defects can be labeled and a person can verify borderline cases. Establish how missed defects and false alarms are handled, and do not treat a laboratory result as proof of safe performance on every production line.
These patterns are starting points, not endorsements. A process that resembles one of them still requires a local data, impact, and control assessment.
Best Value
Use extra caution for decisions that affect people
Hiring, credit, eligibility, pricing, scheduling, performance management, and other decisions that affect people can carry legal, fairness, transparency, and livelihood consequences. The existence of an example in an AI use-case catalog is not an endorsement of automating that decision.
Before proceeding, identify:
- Who is affected, including people who never interact directly with the system.
- Which laws, regulations, contracts, collective agreements, and internal policies apply.
- What explanation a person can receive and how they can challenge or correct an outcome.
- Which variables may act as proxies for protected or sensitive characteristics.
- How a human reviewer will detect errors, override the result, and document the reason.
- Whether the decision can be reversed before harm occurs and how quickly correction is possible.
If the organization cannot provide meaningful review, appeal, evidence, and correction, the process is not ready for an AI-controlled decision. A lower-risk assistive use—such as organizing information for a qualified decision-maker—may be more appropriate, but it still needs testing and access controls.
Design a pilot that can disprove the idea
A pilot is a learning and validation stage, not proof by itself. Define its boundary and failure handling before users begin.
- State the process boundary. Name the starting event, ending event, included cases, excluded cases, systems, users, and duration.
- Document the baseline. Capture the current measures, sampling method, volume, seasonality, quality level, manual effort, and existing exception rate.
- Specify permitted inputs and actions. List approved data sources, prohibited data, user roles, tools the system may call, and actions it may never take automatically.
- Set human checkpoints. Define which outputs require approval, what evidence the reviewer sees, confidence or uncertainty handling, and the escalation route.
- Write acceptance thresholds. Set target ranges for speed or cost together with minimum quality, safety, fairness, service, and control requirements. Include a sample size and review method appropriate to the process.
- Define failure and rollback. Decide how to stop the system, revert to the prior workflow, notify affected users, preserve records, and investigate an incident.
- Train and instrument. Train users on appropriate reliance, common failure modes, privacy, and escalation. Log inputs, outputs, edits, approvals, overrides, and exceptions in a way that supports monitoring.
- Review results against the baseline. Examine benefit measures together with errors, exceptions, review time, user behavior, and performance differences across relevant cases.
Set a stop condition that is triggered by a control failure even if speed improves. Examples include an unacceptable error rate in a critical field, an inability to reconstruct decisions, unauthorized data exposure, missed escalations, or reviewer workload that makes meaningful checking impossible.
Decide whether to scale, redesign, or stop
After the pilot, the process owner should be able to answer four separate questions:
- Did the outcome improve? Compare the predefined measures with the baseline rather than relying on impressions or a demonstration.
- Did quality and safety hold? Examine sampled errors, harmful outputs, exceptions, escalations, and performance across relevant groups or case types.
- Can people operate it? Confirm that reviewers have sufficient time, authority, training, and a workable escalation path.
- Can the organization maintain it? Confirm ownership for data, integrations, access, monitoring, incident response, updates, and retirement.
Scale only when the improvement is real, controls work under ordinary and unusual conditions, and an accountable owner can maintain the system. Redesign or narrow the scope when the value is plausible but data, integration, or oversight is not ready. Stop when the process cannot meet its control requirements or when a simpler intervention performs the job better.
Revisit the assessment whenever the model, prompts, tools, data, access rights, workflow, users, or business purpose changes. A system that was acceptable for advisory drafting may require a new review if it later receives permission to send messages, update records, or make decisions automatically.
A practical go/no-go checklist
- Is there one named process owner with authority to approve the change?
- Can the team state the process step and desired outcome in measurable terms?
- Has the actual workflow, including variants and exceptions, been mapped?
- Is there a baseline for speed, quality, cost, service, and relevant control measures?
- Is AI necessary, or would a rule, redesign, or conventional automation be better?
- Are the data available, accurate enough, representative, secure, and permitted for this use?
- Can the organization test accuracy, bias or disparate performance where relevant, and failure handling?
- Are system integrations, permissions, logging, retention, and monitoring feasible?
- Are consequential or irreversible steps subject to meaningful human review and escalation?
- Are pilot acceptance thresholds, stop conditions, rollback, and incident responsibilities written down?
- Does the expected value justify implementation and ongoing operating effort?
- Is there a named team that will maintain the process after the pilot?
If several answers are “no,” the candidate is not necessarily discarded; it is a signal to repair the process, data, governance, or measurement first. The strongest AI programs improve the selection conditions before they scale the technology.
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