A first AI initiative should be chosen like an operating change: around a defined task, accountable owner, trusted inputs, and a result that can be measured.
Why readiness is an operating question
Most stalled AI programs begin with a technology decision and discover the operating problem later. A more useful starting point is the work itself: a recurring task that has a known trigger, a visible output, and someone who can explain the exception path. That does not make the work simple. It makes it testable.
For example, an agent may assemble a customer-support brief from approved records, classify incoming requests, prepare a change-impact summary, or draft a first response. In each case, the agent supports a decision process; it does not become the owner of the decision. The business owner remains responsible for what happens when the inputs are incomplete, the output conflicts with policy, or a user challenges the result.
The readiness scorecard
| Readiness dimension | Evidence to collect | What a weak answer means |
|---|---|---|
| Business value | Baseline cycle time, backlog, rework, or service measure | The pilot cannot demonstrate value. |
| Ownership | Named accountable owner and technical lead | Exceptions will have nowhere to go. |
| Knowledge | Approved sources, freshness owner, access rules | Retrieval will be inconsistent or unsafe. |
| Evaluation | Representative tasks and acceptable-result criteria | A polished demo may hide failure modes. |
| Risk boundary | Actions the system may recommend, never execute, and must escalate | The workflow can overreach its authority. |
Use a one-page operating brief
Before selecting a model or platform, document the trigger, users, inputs, systems of record, required output, prohibited actions, approvals, escalation path, and baseline measure. This brief becomes the common reference for a business sponsor, architect, security reviewer, and delivery team. It also avoids the familiar failure mode in which every stakeholder imagines a different “agent.”
Decision tool: candidate-workflow test
- Can a reviewer tell whether the output is useful without reading the model’s reasoning?
- Can the task be safely limited to preparation, retrieval, classification, or recommendation in the first release?
- Can the team assemble examples of normal, difficult, and unacceptable cases?
- Is there a named owner for source data and exception handling?
If any answer is no, resolve it before advancing to implementation.
A measured pilot, not a role-replacement promise
A pilot earns expansion by comparing the supported workflow with the baseline: time to prepare a packet, percentage of complete classifications, reviewer correction rate, source-citation coverage, or escalation rate. The number that matters is specific to the workflow. “The agent is impressive” is not an operating metric.
SoloSoft’s established delivery material reinforces the same discipline: requirements, architecture, quality planning, and acceptance criteria come before release. AI changes the interface and risk profile; it does not remove the need for a defined delivery process.
Sources and further reading
- NIST AI Risk Management Framework — a practical risk-management vocabulary for AI systems.
- OWASP Top 10 for LLM Applications — implementation risks to test and control.
- NIST AI RMF Playbook — actions for governing, mapping, measuring, and managing AI risk.
Turn the framework into a working plan
SoloSoft can help define the workflow, system boundaries, success measures, and delivery sequence before implementation begins.