My Framework for Delivering AI Automation Projects

What process should an AI automation consultant follow? Clarify the problem and ownership; locate the real workflow and baseline; engineer rules, AI tasks and controls; assemble a narrow connected prototype; review it with task-specific evaluation; launch under limited authority; and refine it using production evidence. Each phase should end with an explicit decision and deliverables the client can inspect.

I use the name CLEAR Path because the method extends my broader CLEAR approach—capture reality, locate friction, engineer the flow, automate wisely, run and refine—into a delivery sequence with concrete gates. The framework is proprietary shorthand, not a certification or claim that projects are risk-free.

The seven-phase CLEAR Path

1. Clarify: decide what success means

We begin with a business decision, not a tool request. What outcome matters? Who experiences the current problem? Who owns policy and acceptance? What is explicitly out of scope? Which actions, data or affected people raise the consequence? A useful brief names the workflow, users, constraints, accountable owner and what evidence would justify continuing.

2. Locate: capture reality and the baseline

I observe actual cases with operators. We map triggers, queues, systems, copied data, informal workarounds, exception categories and approval behavior. We establish volume, cycle time, rework, error severity, service effect and current cost. This prevents the project from automating an imagined SOP.

The AI automation audit provides a broader opportunity backlog; the delivery project selects one bounded path.

3. Engineer: design the operating contract

The target workflow separates deterministic rules from AI interpretation. Every model step has a narrow responsibility, typed output and refusal or escalation state. Tools have least-privilege identities. Consequential actions require meaningful approval. We design evidence, logging, retries, idempotency, manual continuity and decommissioning before the build expands.

4. Assemble: build the smallest connected slice

A prototype should touch the real integration path early enough to expose authentication, data-quality and system constraints. We begin read-only or preparation-first where possible. Configuration, prompts, policies and test cases are versioned. The client can see what the system used, proposed and changed.

5. Review: evaluate the task and the risk

Evaluation covers common cases, edge cases, known failures, adversarial content, permission boundaries and dependency outages. Measures include task success, source support, exception detection, false actions, correction time, user experience, business value and residual risk. NIST's August 2026 TEVV-Athlon draft reinforces a key idea: evaluation objectives and methods should be customized to real organizational outcomes.

Review ends with one recommendation: launch within stated limits, revise and re-test, or stop. A polished demonstration is not an acceptance criterion.

6. Launch: constrain production authority

Initial production has limited users, channels, data, actions and volume. We train operators, document support and incident paths, test rollback and keep a manual fallback. Traces, metrics and logs connect the workflow stages without unnecessarily copying sensitive content. The business owner accepts the residual risk and operating responsibilities.

7. Refine: run the system as an owned capability

After launch, we review exceptions, overrides, drift, latency, vendor changes, cost, access, incidents and business measures. Corrections become evaluation cases. The owner decides whether to expand, change or retire. Continuous improvement is not permanent vendor dependence; documentation, source access, configuration and exit paths should remain understandable to the client.

Client responsibilities

• Provide an accountable owner with authority to make process and risk decisions.
• Give access to representative operators, cases, policies and system owners.
• Confirm rights to use data, content and integrations and obtain needed professional advice.
• Review deliverables and exceptions promptly; do not delegate final accountability to the model.
• Fund post-launch operation, monitoring, incident response and improvement.

Acceptance criteria I expect before expansion

• The workflow's scope, identity, data, tools, approvals and owners are documented.
• Representative and high-severity cases meet agreed task and safety thresholds.
• Unsupported, ambiguous and unavailable-system states stop or escalate correctly.
• External actions are traceable, reconciled and recoverable without duplication.
• Users understand review responsibilities and can report, pause and bypass the workflow.
• Business measures improve enough to justify full operating cost and residual risk.

The ROI guide helps build the economic case, the 30-day pilot plan fits a bounded proof phase, and my workflow-design principles explain the technical and operational controls underneath this method.

Primary sources checked for this guide

Checked 27 August 2026.

• NIST — TEVV-Athlon Framework for Evaluating AI Systems
• NIST AI RMF Core
• OpenTelemetry — traces, metrics and logs
• Google SRE — monitoring, release, incident and reliability practices
• OWASP — Securing Agentic Applications Guide