An eight-page guide to the decisions that turn a recurring business problem into an implementation worth pursuing: the workflow, the business case, the systems, the people, and the evidence.
Start with a decision your business needs to make
AI transformation begins with how work gets done: the decisions people make, the information they need, the systems they use, and the handoffs that slow them down. A useful first question is specific: which recurring process is preventing the team from delivering the service, capacity, or control the business needs?
This guide is an original Clairvance planning method for that question. It is designed for an operations leader, finance sponsor, or technology team preparing a change together. It does not require a new AI platform, promise a return, or rank your company against a supposed industry benchmark. The output is a decision brief that your team can challenge and improve.
Choose a workflow with a visible beginning and end
Name the trigger, the completed outcome, and the person accountable for the result. For example, 'process customer orders' is broad. 'Turn an emailed purchase order into a reviewed order ready for ERP entry' identifies a boundary that a team can observe and test. Include the people who handle exceptions; a process map drawn only from its ideal path will miss important work.
Compare a few candidates using separate questions about value, feasibility, and readiness. Is the volume meaningful? Can the team measure handling time or rework? Are representative records available? Can someone authorize the required system access? Record unknowns as evidence to collect. A high-value idea with no process owner is not automatically ready to build.
Check the simpler options first
Consider removing an unnecessary step, changing a handoff, configuring a feature you already license, or connecting systems through supported interfaces. Rules and structured imports are often worth evaluating when inputs and decisions are consistent. An AI component may be useful when the work involves varied documents, language, or judgment that can still be checked against explicit acceptance criteria.
Compare options using the same sample cases. Ask what each approach does with a missing field, a conflicting record, an unfamiliar format, or an unavailable downstream system. A persuasive demo is useful evidence of one path; it is not evidence that the entire workflow is ready. Include ongoing ownership, access, vendor dependencies, and failure recovery in the comparison.
Build a business case your finance team can examine
Measure current volume, handling effort, waiting time, and the share of work that needs correction. Then estimate the proposed process, including review, exception handling, maintenance, and adoption. Keep assumptions visible and use a range when evidence is weak. A proposal should state which observations would make the team change its mind.
Time released is potential capacity. It becomes a cash saving only when a specific cost changes, such as overtime or outside processing expense. Additional capacity can still be valuable, but its benefit depends on whether the business can use it. Avoid counting the same improvement as both labor savings and additional output. Include recurring software and support costs in the decision.
Make control and ownership part of the design
Document which information the workflow can read, what it may change, and which actions require approval. Decide who handles unresolved items, what evidence the reviewer sees, and how work continues if an integration fails. These choices are part of a usable operating process, not paperwork to add after a demonstration.
NIST's AI Risk Management Framework is voluntary guidance organized around governing, mapping, measuring, and managing AI risks. The guidance is a useful reference; this guide is not a NIST assessment or certification. Read NIST AI RMF 1.0. NIST lists a revision in progress, so verify the current version when setting your organization's requirements. Check the current framework status.
Test the workflow and the way people will use it
Agree acceptance criteria before choosing the most impressive output. Prepare representative ordinary cases and deliberately difficult ones. Keep development examples separate from final evaluation examples where practical. Check output quality, review effort, wrong actions, access boundaries, duplicate handling, and the ability to recover when a system is unavailable.
Involve the people who will use and support the workflow. Define what changes in their day, provide practice with exceptions, and make it clear where to get help. A sponsor resolves priorities and removes obstacles; a process owner decides whether the work is improving. A technical owner maintains the integration. These responsibilities may sit with different people.
Use evidence to decide what happens next
A controlled pilot should finish with a decision: expand, adjust, keep observing, or stop. Review results against the original baseline and record the population and time period measured. A small successful sample may justify a wider test; it does not establish reliability across every location, supplier, or document format.
The downloadable guide includes an illustrative distributor-order example, an option comparison, a baseline measurement table, a responsibilities table, test scenarios, and a first-implementation decision memo. The example is constructed for planning and is not a Clairvance client result. Bring the completed brief to a discussion about adapting the workflow to your business.
Sources & further reading
- Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology, 2023-01-26. Accessed 2026-09-09.
- AI Risk Management Framework: current status and resources. National Institute of Standards and Technology, Current resource page; accessed September 2026. Accessed 2026-09-09.