Our data isn't clean enough for AI

Common questions from Omaha leaders

When is 'our data isn't clean' a real blocker?

When the AI use case requires structured analysis across many records (forecasting, pricing, segmentation, ML scoring) and your data is genuinely fragmented across systems or full of duplicates and inconsistent codes. Real blocker: you can't generate the input the workflow needs without weeks of cleanup.

When is it a deferral?

When the AI use case is unstructured drafting (claims correspondence, RFP responses, meeting summaries, training content) where the inputs are already documents people read every day. Documents don't need 'clean data' to feed AI; they need the same readability humans rely on.

How do we tell which one is happening?

Ask the question at the workflow level, not the company level. 'Is THIS workflow's data ready?' If you can describe the workflow and the documents/records it uses, you can usually tell whether it's structured-and-needs-clean-data or unstructured-and-already-feedable.

What if the data is genuinely a problem?

Pick a different first wedge. Use cases that don't require structured-data-cleanup ship in 90 days; use cases that do require 6–12 months of data work first. Better to ship a wedge while the data work happens in parallel than to wait for the full data layer to be clean before starting.

Does AI itself help clean the data?

Sometimes — for unstructured-to-structured extraction (PDFs to CSV, emails to records, free-text to taxonomy). For deduplication, master-data management, and schema reconciliation, the right tools are usually traditional data tooling, not GenAI. Pick the right tool for the work.