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Across the world, companies invest millions in AI pilots, hire AI leaders, buy tools, and launch innovation labs, yet 70% of these efforts never reach production.
The biggest misconception is that these failures happen because models are complex or the technology isn’t ready.
AI projects fail before they start because the data they rely on is incomplete, inconsistent, inaccessible, or locked inside organizational silos. Most enterprises do not suffer from an AI capability problem; they suffer from a data readiness problem.
Data engineering teams admit they spend up to 80% of project time on:
▪️Cleaning
▪️Reconciling
▪️Validating
▪️Stitching
▪️Mapping
▪️Normalizing
All of this happens before a single model can be tested. AI teams are not building intelligence; they are doing data janitorial work.
And the deeper issue?
Every department works in its own silo, with its own formats, standards, and logic. AI cannot thrive in this fragmentation.
AI is a multiplier. It amplifies what you give it:
▪️Good data → powerful outcomes
▪️Bad data → accelerated failures
If customer data is inconsistent, AI recommends the wrong actions.
If invoice data is incomplete, AI automates the wrong workflows.
If classification is outdated, AI hallucinates patterns that don’t exist.
AI does not fix your data problems; it exposes them.
Successful AI transformations all have one thing in common:
They treat data the same way they treat their core business products.
That means:
▪️Ownership
▪️Standards
▪️SLAs
▪️Observability
▪️Lineage
▪️Quality monitoring
▪️Governance
Most importantly, everything must align with business outcomes, not technical outputs.
AI is ready.
Your data might not be.
Fix the foundation and the entire transformation becomes faster, safer, and dramatically more valuable.