How well the workflow, steps, exceptions, and decision rules are defined.
CASE STUDY · AI OPERATIONS · WORKFLOW INTELLIGENCE
AI Workflow Audit
A browser-based assessment system for understanding how work actually happens before deciding what should be automated.
Privacy-first public build: this version runs entirely in the browser, does not require an API key, does not transmit submitted workflow data, and does not persist user inputs.
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THE PROBLEM
Automation can make a broken workflow fail faster.
Organizations often jump from a pain point directly to an automation tool. The Workflow Audit was designed to force a more disciplined sequence: document the current workflow, identify the actual operational constraint, evaluate supporting evidence, decide what should remain human-controlled, and only then determine whether automation is appropriate.
MY ROLE
I designed the assessment model around operational maturity—not technology enthusiasm.
I defined the audit pipeline, maturity dimensions, weighted scoring, evidence-strength separation, bottleneck logic, automation-readiness formula, human-governance boundaries, recommendation logic, and future-state roadmap structure. The design deliberately distinguishes self-reported maturity from evidence-backed findings.
ASSESSMENT MODEL
Eight dimensions create a full operating picture.
Whether accountability and transitions between people or teams are clear.
Whether people have current, usable information at the point of work.
How much work is spread across disconnected systems or duplicate entry.
Where repetitive effort, rework, and avoidable manual steps consume capacity.
Whether timing, ownership, stale work, and follow-up are controlled.
Whether the workflow is stable and bounded enough for automation.
Where discretion, approval, exception handling, and accountability must remain human.
EVIDENCE MODEL
A maturity score without evidence is not treated as verified fact.
Users score explicit maturity questions from 0 to 4 and may separately add observations, metrics, documentation notes, or examples. Evidence strength is classified as Strong, Moderate, Weak, or Insufficient. That distinction prevents a polished-looking score from being presented with more confidence than the input supports.
BOTTLENECK + AUTOMATION LOGIC
The system derives the constraint and readiness from the audit.
The lowest-performing dimension becomes the primary operational constraint. Automation readiness is then calculated from the completed audit using weighted workflow maturity—not displayed as a fixed demo percentage. The recommendation layer distinguishes AI-assisted work, deterministic automation, human-retained authority, and redesign prerequisites.
FUTURE STATE
Repair the foundation before adding intelligence.
The implementation roadmap is generated in three phases: Foundation repairs structural weaknesses, Enablement automates stable bounded work, and Intelligence adds measurement, governance, and continuous improvement. The future-state workflow is derived from the entered process, the lowest-performing dimensions, and explicit governance boundaries.
SECURITY + PRIVACY
The public build intentionally avoids external data processing.
- No API key is embedded.
- No workflow submission is stored by the application.
- No user content is transmitted to an external service.
- User-provided text is handled as browser content rather than model instructions.
- The interface warns users not to enter confidential, regulated, credential, or personally identifiable information.
VALIDATION EVIDENCE
What the live application demonstrates.
The workflow health result comes from the documented weighted assessment framework.
The application distinguishes maturity ratings from the strength of supporting evidence.
Automation readiness is calculated from completed audit dimensions rather than hard-coded.
Exceptions, policy interpretation, sensitive actions, final approval, and automation quality ownership remain human-controlled.
Recommendations and future-state priorities are based on the scored workflow rather than a fixed fictional result.
The portfolio build does not present unmeasured time savings, revenue impact, or operational improvements as real client outcomes.
LIMITATIONS
Assessment is not the same as production evidence.
Results depend on the quality and honesty of the user's maturity ratings and supporting information. Recommendations should be validated against actual process data, policy, system architecture, and stakeholder requirements before implementation. This release is an operational assessment tool, not an autonomous decision-maker.
WHY IT MATTERS
The system demonstrates discernment about where AI belongs.
The project shows the ability to diagnose operations before automating them, quantify workflow maturity without hiding evidence limitations, and preserve human accountability around exceptions and high-impact decisions.