Research Note
Workplace Workflow Mapping Record
An AI product is evaluated against a work system. Without a current-state map, a team can automate the visible drafting step while leaving intake, evidence gathering, exc
Workplace Workflow Mapping Record
Why the map precedes the product
An AI product is evaluated against a work system. Without a current-state map, a team can automate the visible drafting step while leaving intake, evidence gathering, exception handling, review, filing, correction, and accountability untouched.
NIST's AI RMF Core asks organizations to define the specific task, deployment context, expected benefits and costs, human oversight, third-party components, and affected people. Those questions begin with the work.
Observation boundary
The official procedure is not always the actual process. The map should follow a representative case from trigger to accepted record and include the people who perform and receive the work. It should distinguish observed practice, required policy, workaround, exception, and proposed future state.
Worker review is evidence, not ceremony. A manager's diagram can omit judgment, informal coordination, accessibility needs, exception work, and correction steps that make the process function.
Minimum map
The map should identify the trigger, inputs, data owners, source authority, decisions, transformations, tools, handoffs, queues, exceptions, controls, output, acceptance standard, system of record, rework, incident path, and accountable owner.
For each step, record elapsed time and active effort separately. Waiting for approval is different from work time. AI may shorten generation while leaving queue time unchanged.
Intervention decision
The map should expose the narrow constraint. The problem may be missing source authority, duplicate records, unclear ownership, bad permissions, scarce reviewer capacity, inconsistent intake, or a policy conflict. AI is not the default treatment.
The final record should state whether the constraint is suitable for AI assistance, ordinary automation, workflow repair, training, staffing, a policy decision, or no change.
Sources
Follow the evidence.
- NIST AI RMF Measure guidanceairc.nist.gov
- ftc.gov: ai companies uphold your privacy confidentiality commitmentsftc.gov
- youtu.be: 0cC1Ez33ryIyoutu.be
- daltonanderson.ghost.io: ai in the workplace a practical guide to get starteddaltonanderson.ghost.io
- NIST AI Risk Management Frameworknist.gov
- NIST AI Resource Centerairc.nist.gov
- eeoc.gov: prohibited employment policiespracticeseeoc.gov
- eeoc.gov: us eeoc and us department justice warn against disability discriminationeeoc.gov
- nber.org: w31161nber.org
- open.spotify.com: 7LIXDoSM2gG97vFGftskQsopen.spotify.com
- NIST Privacy Frameworknist.gov
- nber.org: w33795nber.org
- eeoc.gov: strategic enforcement plan fiscal years 2024 2028eeoc.gov
- NIST Generative AI Profilenvlpubs.nist.gov
- ftc.gov: start security guide businessftc.gov
- dol.gov: ten 07 25dol.gov
- hbs.edu: dell acqua et al 2026 navigating the jagged technological frontier 5c589c8c fbb5 458f b285 c944746cd717hbs.edu
- cisa.gov: cisa and uk ncsc unveil joint guidelines secure ai system developmentcisa.gov