Functional practice leaders and AI-enabled knowledge work

Define the decision portfolio before choosing tools

Begin with decisions the practice repeatedly makes: approving a client recommendation, releasing a forecast, accepting a control exception, signing off a research synthesis, or escalating an unresolved discrepancy. Record the audience, consequence of error, source authority, review deadline, and the person with release authority. Classify the practice leader as owner, influencer, reviewer, or collaborator for each row.

This boundary matters because a practice leader may set methods and quality expectations while information security controls platform access, legal counsel interprets obligations, and individual engagement owners approve deliverables. A single label such as “AI owner” erases these distinctions and makes accountability harder to execute.

The NIST AI Risk Management Framework Core separates governance, context mapping, measurement, and risk treatment. It also calls for clear roles and responsibilities and for risk work to continue as systems and contexts change. The framework is voluntary and non-sector-specific; it provides a useful organizing model, not a practice-specific certification.

Sources: [1]

Build a practice assurance map

Create one row for every decision-bearing deliverable. Include the task, authoritative inputs, expected output, AI action, human judgment point, verification method, evidence retained, release authority, and escalation condition. Describe the AI action precisely: retrieval, extraction, classification, calculation support, drafting, critique, or transformation. Different actions require different checks.

For retrieval, test whether the selected source is current and authoritative. For extraction, compare sampled fields to the original. For calculation support, reperform material calculations independently. For drafting, trace material claims and recommendations back to evidence. For critique, make sure the reviewer—not the model—decides whether an issue is real and what response is proportionate.

The GAO AI Accountability Framework was developed for federal agencies and other entities around governance, data, performance, and monitoring. Its emphasis on documentation and accountability supports keeping evidence close to the decision. It does not establish a universal professional-practice rule, so the practice must still apply its own standards and obligations.

Sources: [2]

Worked artifact: a synthetic proposal-review practice

Consider a synthetic consulting practice that uses AI to help review proposal drafts. The initial workflow asks a model to identify unsupported claims, inconsistent scope statements, and missing evidence. The practice assurance map makes clear that the model produces review leads, not final findings. The engagement lead owns the response, a subject-matter reviewer confirms technical claims, and commercial leadership approves any change to price or scope.

In one sample proposal, the model flags a sentence claiming that a migration approach will reduce processing time by 40 percent. The reviewer traces the claim to an internal pilot summary and discovers that the result covered one workload under controlled conditions. The claim is narrowed to describe the pilot and its boundary. A second flag alleges a missing security requirement, but the reviewer finds the requirement in an incorporated appendix and closes the flag as false.

The retained artifact contains the original claim, source reference, reviewer disposition, revised wording, unresolved questions, and release approval. The example is synthetic. It is not customer proof, a Two Prune benchmark, or evidence that AI review improves proposal outcomes.

Calibrate review effort by consequence

Do not apply the same review ritual to every task. Define tiers using consequence, reversibility, evidence complexity, and external reliance. A low-consequence internal brainstorm may need attribution and confidentiality checks. A decision sent to a client, regulator, executive committee, or financial process may require source tracing, independent calculation, specialist review, and explicit release authority.

The NIST Core describes measurement as using quantitative, qualitative, or mixed methods and documenting what cannot be measured. Apply that principle to practice oversight: track material defects found before release, unsupported claims, unresolved exceptions, review latency, and rework causes, while recording gaps in observation. Tool activity and prompt volume may describe usage but do not demonstrate that judgment or verification was sound.

Verify that the operating map is real

Sample completed work and attempt to reconstruct the decision. Confirm that the named source existed in the cited version, the verification step occurred, the reviewer had the stated authority, and exceptions reached the appropriate owner. Compare the written map with actual workflow permissions and approval records. Revisit it after a model, source system, policy, client requirement, or delivery method changes.

This method can make professional standards and review ownership more explicit. It does not certify service quality, prove business outcomes, or transfer accountability to an AI system. Two Prune can create task-bounded evidence of how a participant handles a realistic simulation. It does not operate the practice, approve client work, or make employment decisions.