hebel. today · 3 November 202650 / 68
Science · Data Science Applications
Audit AI results with records before scaling
Most organizations struggle to quantify the impact of their AI investments. While 92% of users report that AI meets or exceeds expectations, only 27% can provide a precise number based on actual records. Relying…
From 4 newsletters · 1 min read · one thing to try
Most organizations struggle to quantify the impact of their AI investments. While 92% of users report that AI meets or exceeds expectations, only 27% can provide a precise number based on actual records (Decoding Customer Experience). Relying on anecdotal reports or gut feelings often leads to inaccurate assessments, as engineers frequently overestimate AI-driven productivity gains by nearly 50% (O'Reilly Radar The latest on the trends…). To gain clarity, you must move beyond dashboards and examine the underlying data, such as support tickets or customer interaction logs, to confirm if engagement increases actually drive business value or merely reflect the need for repeated corrections (Top 1% Builder with Ravi).
“Knowing what happened is not the same as knowing what to do.”
Top 1% Builder with Ravi
Effective evaluation requires a clear baseline and a transparent audit trail. When a system proposes a fix, you should review the specific customer contacts that informed the recommendation and verify the change with the relevant policy owner. By logging the specific source files used and flagging missing policy details, you create a system of record that separates machine state from human reporting (Flying Higher AI Making businesses work…). This process allows you to determine whether a result is observed, believed, or tested, ensuring that you only scale solutions that demonstrate a causal link between AI behavior and improved outcomes.
Try today · 30 min
Build a simple audit template for a recurring AI-driven task.
Pick one automated workflow, pull the last three AI outputs, and manually verify them against the source data to identify gaps.
You need
- Three recent AI-generated outputs
- The original source data or policy documents used for those tasks
- A spreadsheet or document for tracking
Steps
- Select one recurring AI task where the output is used for a decision.
- List the specific source data the AI should have used for each output.
- Compare the AI's result against the source to check for factual accuracy.
- Identify any missing policy details or hallucinations.
- Document the 'before' and 'after' state of the workflow if a change is needed.
- Share this audit with the process owner to confirm the findings.