For an operations team, useful intelligence has a practical test: does the system help someone complete a defined task with acceptable accuracy, effort and risk? A fluent explanation may help a reviewer, while an incorrect supplier reference can still make the resulting action unusable.
Define the task and its boundaries
Choose an observable outcome, such as categorizing an incoming request, drafting a response or summarizing a project update. Specify the required inputs, permitted information sources and expected output. State when the system should ask for clarification or refer the work to a person.
The NIST AI Risk Management Framework Core emphasizes intended context, defined tasks, knowledge limits and human oversight. Use those questions to describe the proposed workflow before choosing a model or granting access to business records.
Choose the method for each decision
Separate fixed business rules from tasks requiring interpretation. A required approval role or a calculation can have an explicit rule. Interpreting a loosely written service request may call for a different approach. Document how the pieces connect and which component makes the final decision.
For example, an assistant might propose a category and explain the supporting text. An application can then validate the category against allowed values and route the proposal for review. This is a design example; the permissions and review steps must be implemented and tested in the chosen system.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Make evaluation reflect actual work
- Ordinary cases: examples representative of the intended users and process.
- Unclear cases: incomplete requests, conflicting information and outdated documents.
- Boundary cases: records outside the user's company or permitted scope.
- Adversarial cases: supplied text that attempts to redirect the assistant.
Define a correct result and an acceptable escalation for each example. Compare corrected outputs and review time alongside task completion. Keep a versioned evaluation set so changes to prompts, models or retrieval can be assessed consistently.
Expand authority only with evidence
Retrieved material can contain instructions as well as facts. OWASP’s prompt injection guidance explains why retrieval alone does not remove this risk. Enforce access and action controls outside the model, and show reviewers the records and proposed changes needed for an informed decision.
Start with the least consequential useful task. Review exceptions and failure recovery before extending the workflow’s authority. Assign an owner who can pause it when its operating context changes.
The GreatWork.ai module catalogue includes AI Agent and AI Business Advisor. Evaluate any proposed configuration against these task and oversight requirements. The AI business assistant and document workflow overviews provide related planning context.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!