Start with a Decision About the Work
Choose a task with a clear beginning, a useful output and someone responsible for accepting that output. For example, an internal service assistant could retrieve an approved procedure and draft a response for a colleague to review.
Before selecting a model, record how the task works today: who performs it, where delays occur, what information is required and how mistakes are corrected. Agree on the acceptable result. A fluent answer is insufficient if the employee still has to repeat the entire task.
Assign Authority and Accountability
Name the business owner, the technical operator and the person authorized to approve changes. Specify whether the system may suggest, draft or execute each action. A recommendation that a person checks carries different responsibilities from a system that changes a customer record.
The NIST AI Risk Management Framework provides voluntary guidance for incorporating trustworthiness into AI design, development, use and evaluation. Use it to structure the discussion, then translate the relevant risks into named responsibilities and release decisions for your workflow.
Test the Task Under Realistic Conditions
Build an evaluation set from approved examples that represent normal work, incomplete requests, conflicting information and situations requiring escalation. Include expected answers or review criteria. Test the connected workflow as well as the model, including retrieval, permissions and downstream actions.
NIST's Generative AI Profile cautions against generalizing capability from narrow, anecdotal assessments and recommends verifying generated sources and citations. Review failures with people who understand the task. Define the conditions that prevent release, rather than relying on an attractive demonstration or one overall score.
Control Access and Consequential Actions
Give the application only the information and permissions needed for its assigned task. Enforce access in the surrounding systems. For consequential actions, design an approval step that shows the proposed change and relevant evidence to an authorized reviewer.
External documents and emails can contain instructions designed to redirect an AI system. Microsoft's Prompt Shields documentation describes this risk and notes that detection can miss attacks or flag legitimate input. Treat filters as one control among several. Test unauthorized requests and confirm that restricted actions remain blocked.
Plan for Operation and Change
Agree on monitoring, incident ownership and a usable fallback before expanding access. Microsoft's AI observability guidance separates evaluation, production monitoring and tracing. Together, these help teams examine answer quality, operational performance and the steps behind a result.
Set review triggers for model updates, new data sources and changes to business rules. Keep enough evidence to investigate failures while applying appropriate access and retention controls to logs. Make it clear who can pause the workflow and how employees continue their work during an interruption.
Make Expansion an Evidence-Based Decision
Compare the pilot with the original process using consistent measures: accepted task completion, review effort, corrections, elapsed time and total operating cost. Include integration, evaluation and support work in the economics. Results from a limited pilot do not guarantee returns at a larger scale.
Nyne's recommended starting point is one defined workstream, an agreed acceptance standard and a named operating owner. Use the evidence to decide whether to expand, refine the scope or keep the existing process.
- Define the business task and acceptance criteria before selecting the model.
- Evaluate realistic work and failure conditions, including system permissions.
- Assign ownership for approvals, monitoring, fallback and future changes.
Sources and Further Reading
- NIST AI Risk Management Framework
- NIST Generative Artificial Intelligence Profile, AI 600-1
- Microsoft: Prompt Shields
- Microsoft: Observability in generative AI