A real concern Lincoln leaders raise
Our data isn't clean enough for AI
Often true. Sometimes a reason to fix the data first. More often, an excuse to defer the work. Here's how to tell which one your team is actually saying.
Text Rosey · Schedule a call →Common questions from Lincoln 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 fragmented or full of duplicates.
- When is it a deferral?
- When the use case is unstructured drafting (correspondence, summaries, training content). Documents don't need clean data.
- How do we tell which is happening?
- Ask the question at the workflow level, not the company level. 'Is THIS workflow's data ready?'
- What if the data is genuinely a problem?
- Pick a different first wedge. Ship one in 90 days while data work happens in parallel.
- Does AI help clean the data?
- Sometimes — for unstructured-to-structured extraction. For deduplication and schema reconciliation, traditional data tooling fits better.
Sources
- AI high performers are nearly 3x as likely as others to say their organizations have fundamentally redesigned individual workflows — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
- Roughly two-thirds of organizations have not yet begun scaling AI across the enterprise — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
- 69% of respondents expect implementing a governance strategy will take more than one year — Now decides next: Generating a new future — State of Generative AI in the Enterprise Quarter four, Deloitte AI Institute, 2025
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