Pre-Flight Checklist: "Slop" Happens (Post #4 of 20)
Do you have a data quality crisis?
Have you created a distorted reality built on messy code or ambiguous data?
That question defines the single biggest quality threat to your AI adoption. When executives see AI failing, they often blame the model or the tool. But the truth is simpler and harder to fix.
The AI industry gave us the soft, technical term “hallucination” to describe a mistake, or incorrect output from a question (prompt). But let’s be honest about the data quality problem. “Slop” is the most accurate term available. It perfectly describes the messy, disorganized, often-corrupted junk your AI bots and agents are forced to wade through in your organization’s files.
How does slop happen?
AI Slop eats ambiguity for breakfast.
It happens when your system encounters multiple, conflicting versions of the truth. Think about the complexity. How many different versions do you have of your TPS report in the data repository that you just asked a bot to summarize? We’ve all seen the results clogging up our feeds with the generic, bland text, and hallucinations (mistakes) generated from data that just smells of AI.
Slop is the result of a systemic governance failure; trusting the output of systems built on flawed, ambiguous data while skipping the Pre-Flight Checklist. This undifferentiated content is now saturating professional channels, visible in generic newsletters and repetitive posts on platforms like LinkedIn. When generated without personal expertise or critical review, the result is an amplified echo chamber of mediocrity.
AI cannot fix a chaotic system, it only amplifies it.
Isolated success can’t scale. Build quality control of data into your foundational framework.
3 Steps to Eliminate Slop
Slop is a symptom of poor data integrity and a missing process.* This is a simple approach for minimum review required.
Assign a Human Data Steward. Conduct a data integrity review before you share a document, whether internally or externally.
Examine Source Data. For data to be considered clean for your desired output (e.g. having an Agent create a Monthly Status Report), do you have multiple versions that it’s summarizing, or Single Source of Truth Data.
Validate Document Integrity. Assess the overall output (e.g. Monthly Status Report) and formally acknowledge Slop risk. (e.g. High Confidence: Data source is clean and consistent. Low confidence: Data is ambiguous and may lead to Slop.) If you have low confidence, consider reviewing the source data you are using.
Test Flight: The Data Integrity Exercise
This exercise demonstrates the systemic data risk of Slop.
Observation: Identify a complex document or dataset in your organization that is known to contain ambiguous or contradictory data (e.g. sales figures from two different, non-reconciled systems).
Initial Output Rating: Ask your preferred AI tool to summarize or reconcile this dataset. Review the output. Rate the confidence of the AI’s final verdict on a scale of 1 to 10 (10 being ready to publish). The score will be low due to the ambiguous input.
Integrity Check: Use just one verified document and re-run the summary. Rate the second document. Note the difference.
Mission Debrief
How did it go? Did applying a data integrity review process produce a higher-quality, more trustworthy output?
The only way to eliminate systemic Slop is to have simple review criteria and final output reviewed by a human, and a threshold for what you’re willing to release. Slop is a sign that your Systems didn’t define the criteria or review the output. By having high level human integrity review, you create a more reliable outcome.
*In the next post, we will tackle non-negotiable conditions required to achieve clean, scalable data.