The Tool Desk
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What makes a business process a good AI candidate?
A process is a credible AI candidate when five conditions line up:
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- A named owner: Someone is accountable for the workflow, its service levels, and the result of changing it.
- A defined outcome: You can state what should improve, such as cycle time, first-contact resolution, rework, forecast accuracy, or cost per case.
- A understood workflow: The actual sequence of tasks, handoffs, exceptions, and approvals is known rather than inferred from a procedure manual.
- Appropriate data: The inputs exist, can be accessed lawfully, are sufficiently accurate and representative, and can be monitored.
- A controllable production path: Users can review outputs, exceptions have an escalation route, and harmful actions can be stopped or undone.
IBM’s AI Center of Excellence guidance uses similar dimensions: owner, workflow, data, measurable goals, and a realistic path to production. Treat them as entry conditions, not as a guarantee of savings. A process that lacks an owner or baseline should be clarified before it is automated.
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Start with a process inventory, not an AI feature
“Use AI in operations” is an ambition, not a testable use case. Build an inventory of concrete workflows and the steps inside them. Include work performed by employees, contractors, customers, and systems; a process can cross several departments.
#1 Best Overall
Record the process facts
- Process name and accountable owner
- Customers, employees, or other people affected
- Participants, roles, approvals, and escalation points
- Source systems, documents, messages, forms, and other inputs
- Outputs, downstream systems, and records that must be retained
- Transaction volume, seasonality, average handling time, and backlog
- Common exceptions, rework loops, handoffs, and manual reconciliations
- Current pain points and the consequence of delay or error
Keep the unit of analysis small enough to measure. “Accounts payable” is a department; “extract invoice fields, match them to a purchase order, and route exceptions for approval” is a process segment that can be evaluated.
Use evidence from the work itself
Process documentation often describes an ideal route. Process mining uses event logs and other digital footprints to show the routes people actually take, including variants, bottlenecks, repeated work, and steps that bypass the intended sequence. IBM describes process mining as a way to identify automation opportunities and compare recorded paths with expected ones. Check whether your systems capture reliable timestamps and case identifiers before relying on a map; missing events can make a clean-looking diagram misleading.
Interview the people who perform and supervise the work as well as the system owner. Ask where information arrives incomplete, which cases require judgment, and what they do when a system recommendation is wrong. Those answers often identify a narrow, lower-risk AI opportunity inside a larger process.
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Write an opportunity statement in this form:
For [process or user], improve [measured result] while maintaining [quality, service, legal, or control requirement].
For example, a support team might seek to reduce request-resolution time while maintaining answer accuracy; a procurement group might shorten review time while preserving required approvals. These are outcome patterns, not promised benefit levels.
Choose measures that expose both benefit and harm
| Dimension | Possible baseline measure | Why it matters |
|---|---|---|
| Speed | Cycle time, queue time, first-response time | Shows whether work moves faster, not merely whether a model responds quickly. |
| Workload | Cases per employee, handling minutes, backlog, touches per case | Reveals whether effort was removed or simply shifted to review and correction. |
| Quality | Error rate, rework rate, completeness, audit findings | Prevents a faster process from being counted as successful when defects rise. |
| Service | Service-level attainment, abandonment, resolution rate, satisfaction | Connects process change to customer or internal-user outcomes. |
| Financial | Cost per case, avoided loss, working-capital days | Provides an organization-specific basis for a business case instead of a generic ROI claim. |
| Control and safety | Approval compliance, escalation rate, harmful or irreversible errors | Shows whether safeguards continue to work under real conditions. |
Measure the current process for a representative period and document how each metric is calculated. Separate one-time implementation costs, recurring operating costs, and any displaced work. Do not assume that reducing handling time equals cash savings unless staffing, capacity, or revenue effects are actually established.
Rank #2
Break the workflow into task types
AI is usually applied to a task, not to an entire department. Label every step so you can choose the simplest intervention that can meet the outcome.
Rules-based work
Stable logic with exact thresholds, validations, or routing is often better handled by deterministic rules or a conventional workflow engine. Rules are easier to test and explain when the inputs and decisions are predictable. Do not add a model merely because the process is repetitive.
Retrieval and synthesis
Searching policies, contracts, case histories, or technical records and producing a draft summary can suit AI when the source material is accessible and citations or links can be retained. Define what sources are authoritative, how stale information is detected, and how a reviewer checks the result.
Classification and prediction
Routing requests, detecting anomalies, estimating demand, or prioritizing cases can be useful when historical labels or outcomes are available. Test performance across relevant segments and define what happens when confidence is low or the case falls outside the training data.
Generation
Drafting a response, report, code change, or form can save time when a person remains responsible for approval. Specify permitted data, prohibited content, required fields, tone or format, and the evidence a reviewer must see before sending or publishing.
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Human judgment and consequential action
Decisions involving eligibility, employment, credit, safety, legal duties, or access to essential services require stronger controls. AI may assist with information gathering or flagging, but a trained person may need authority to decide, explain the result, and correct the record. Keep the system advisory unless the organization has established an appropriate legal, fairness, transparency, and oversight framework.
Rank #3
- Used Book in Good Condition
For each step, mark whether the AI output is advisory or can trigger a system action. The closer a system is to changing money, rights, access, safety, or a permanent record, the more demanding the review, traceability, and rollback design should be.
Check data and technical readiness before scoring value
A promising workflow can fail because its data is inaccessible, unsuitable, or impossible to monitor. Evaluate readiness explicitly.
Data questions
- Do the required inputs exist for the cases you intend to handle?
- Are records complete, timely, consistently formatted, and linked to the correct case?
- Do examples represent seasons, regions, languages, customer groups, and exception types?
- Is the proposed use permitted by contracts, consent, confidentiality obligations, policy, and applicable law?
- Can sensitive fields be minimized, protected, retained, and deleted appropriately?
- Can you retain the input, output, model or prompt version, and reviewer decision needed for an audit?
System and operating questions
- Can the AI connect to the systems where work begins and ends without unsafe copying or uncontrolled exports?
- Are permissions, identity, secrets, and environment boundaries defined?
- Can you monitor input drift, output quality, latency, cost, incidents, and override rates?
- Who owns prompt or model changes, vendor dependencies, maintenance, user training, and incident response?
- What is the fallback when a service is unavailable, data is missing, or an action fails halfway through?
NIST procurement guidance highlights consent and data use, confidentiality and integrity, accuracy criteria, bias testing, user training, transparency, and maintenance. Its procurement materials are guidance rather than binding law for every organization, and one cited document is older; apply the questions to your own jurisdiction and sector requirements.
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Use the same dimensions for every candidate, but choose weights locally. A universal formula would hide differences between a low-impact internal workflow and a decision affecting people.
| Dimension | What to examine | Evidence to collect |
|---|---|---|
| Expected value | Likely change in time, cost, quality, service, experience, or decision support | Baseline, target, affected volume, and assumptions |
| Opportunity scale | Frequency and amount of work consumed by the candidate steps | Case counts, handling minutes, seasonality, and backlog |
| Process stability | Standardization, documented variation, exception frequency, and clarity of ownership | Process map, event-log variants, interviews, and rework data |
| Data readiness | Access, quality, representativeness, permitted use, and evidence retention | Data inventory, sample review, permissions, and privacy assessment |
| Implementation effort | Integration, security, testing, training, operating ownership, and support | Architecture estimate, controls checklist, and staffing plan |
| Impact and reversibility | Consequences for people, finances, service, legal duties, and ability to undo errors | Impact assessment, rollback design, and incident scenarios |
| Oversight fit | Whether a qualified person has enough information, time, authority, and an escalation route | Review procedure, queue capacity, audit trail, and escalation owner |
Score with evidence and record uncertainty rather than awarding optimistic points. A high-volume task with poor data may rank below a smaller task with reliable records and a clear control path. NIST notes data quality, effects on core and business functions, job impact, and the level of human checks as factors that should influence scrutiny and safeguards. IBM’s risk-focused guidance similarly argues that consequential or hard-to-reverse decisions require stronger explainability and control.
Choose the intervention, not just the model
After scoring, select the least complex intervention that can achieve the stated outcome:
Rank #4
- Process redesign: Remove an unnecessary approval, duplicate entry, or handoff.
- Deterministic automation: Apply fixed rules where logic is stable and exact.
- Predictive or classification AI: Prioritize, route, forecast, or flag cases when labeled outcomes support testing.
- Generative AI: Retrieve and synthesize information or draft content with a defined review step.
- Human-only handling: Keep the current process when data, controls, or consequences make automation unjustified.
AI is not automatically necessary. NIST procurement questions explicitly ask whether AI is needed for the outcome and whether the proposed data use is permitted. A simpler change can be cheaper, more reliable, and easier to govern.
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Design a pilot that can fail safely
A pilot is a learning and validation stage, not proof that the idea works. Define its boundary before building it.
- Name the process slice and users. Specify the case types included and excluded, the owner, and the systems in scope.
- Define allowed inputs and actions. List permitted data sources, prohibited fields, output formats, and whether the system can only draft, recommend, or execute.
- Set human checkpoints. Identify who reviews, what evidence they see, how much time they have, and when escalation is mandatory.
- Write acceptance thresholds. Include speed or cost targets plus accuracy, completeness, fairness, service, exception, override, and control measures.
- Specify failure handling. Provide a manual fallback for missing data, low confidence, unavailable services, unsafe content, and partial system actions.
- Define stop and rollback conditions. Stop if harmful errors, unacceptable bias, security incidents, control failures, or sustained quality degradation exceed agreed limits. State who can halt the pilot.
- Instrument the workflow. Log inputs and outputs as permitted, model or prompt versions, reviewer decisions, overrides, latency, cost, and incidents.
- Train participants. Explain what the system can and cannot do, how to challenge an output, how to protect data, and how to report an incident.
Use a representative evaluation set as well as live monitoring. Include difficult and unusual cases, not only the easiest examples. Compare results with the established baseline and record the additional work created by review, correction, and exception handling.
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At the end of the pilot, the process owner should be able to answer:
- Did the target outcome improve against the baseline, under the defined conditions?
- Did quality, fairness, safety, service, or approval compliance deteriorate for any important group or case type?
- How often did people override, correct, or bypass the output?
- Were exceptions handled within the promised service level?
- Can the organization operate, secure, monitor, retrain, and support the system at the expected volume?
- Are the costs and risks acceptable compared with a rule, redesign, or no-change option?
Scale only when the improvement is demonstrated and the controls work in the operating environment. Narrow the scope or redesign the workflow when benefits appear only for easy cases. Stop when acceptance or safety conditions are not met; a well-documented stop prevents a weak experiment from becoming an uncontrolled production dependency.
Where candidate patterns commonly appear—and where caution is essential
Information-heavy service work
Customer service, internal help desks, and operations teams may have repeated requests, large knowledge bases, and measurable resolution times. Suitable tasks can include retrieval, summarization, drafting, and routing, provided authoritative sources and human escalation are clear.
Best Value
Document and transaction handling
Invoices, forms, claims, and case files can contain repetitive extraction, classification, and matching steps. Validate field-level accuracy, document variation, unreadable scans, duplicate records, and the cost of human correction before claiming an end-to-end benefit.
Forecasting and inspection
Demand forecasting, maintenance signals, and manufacturing inspection can be candidates when historical measurements and outcomes are available. Establish how false positives and false negatives affect inventory, downtime, safety, or customer commitments.
High-impact decisions
Hiring, credit, eligibility, and similar decisions affecting people demand a separate impact and legal analysis. The existence of an example use case does not endorse automated decision-making. Identify affected people, applicable rules, notice and appeal requirements, fairness tests, human authority, and a way to correct records before proceeding.
A concise readiness checklist
Move a candidate to pilot only if the owner can answer “yes” or document a credible plan for each item:
- Is one person accountable for the process and outcome?
- Is the candidate a defined step or workflow rather than a department-wide aspiration?
- Do you have a current baseline and a target that includes quality or control?
- Have you mapped actual paths, handoffs, exceptions, and rework?
- Is AI more suitable than a rule, redesign, or conventional automation?
- Are data access, permitted use, security, representativeness, and retention understood?
- Can the system be integrated, monitored, supported, and audited?
- Is a qualified human review point available where judgment or consequences require it?
- Are acceptance thresholds, failure handling, stop criteria, and rollback written down?
- Is there a plan to reassess when data, models, tools, access, or business purpose changes?
The strongest candidates are not necessarily the most technically impressive. They are the workflows where a specific problem is measurable, the data and process are ready enough to test, and the organization can control what happens when the system is wrong.
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