Data First, AI Second

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I watched a company burn $1.3+ million on AI while their data lived in 12 different systems that didn't talk to each other.

The leadership of this manufacturing firm chased the prestige of an "AI transformation" without addressing the broken foundation beneath it: fragmented, inaccessible, and low-quality data.

What they needed first:

• Clean, accessible data - boring but critical

• Cross-functional data governance - even more boring, yet even more critical

• Then maybe something shiny with AI

Why the Unsexy Stuff Matters

AI is everywhere today, but the most significant barrier to success isn't the lack of AI tools…it's the lack of AI-ready data. According to Gartner, 39% of organizations cite "lack of data" as a top obstacle to AI implementation, but they mean poor data quality and readiness.

Manufacturing is a prime example. AI projects often fail because they rely on limited or idealized data that doesn't reflect the messy realities of factory floors…missing events, sensor errors, or inconsistent labeling. Integration becomes a nightmare when data is siloed across multiple incompatible systems, leading to obsolete insights and costly delays.

The Real Foundation is Data Quality and Governance

AI can help with tedious tasks and scale with business growth, but only if the data is accessible and well-managed. Good data governance isn't just about compliance; it's about creating a unified, trustworthy data environment. This means:

• Establishing clear policies and accountability for data accuracy, provenance, and ethical use

• Creating cross-functional governance frameworks that unify fragmented data sources

• Implementing continuous data quality controls to avoid "garbage in, garbage out" scenarios

Without these fundamentals, AI investments risk becoming expensive experiments with little return.

Lessons from Industry Leaders

McKinsey highlights that poor data quality is a consistent roadblock for AI in manufacturing. Legacy systems, weak governance, and decentralized data teams often leave companies stuck with ungovernable data silos. Fixing these issues requires targeted, iterative efforts focused on the highest-impact problems, not waiting years for perfect systems to emerge.

Harvard Business Review research further confirms this pattern across industries. Their study of AI implementations found that organizations with strong data foundations were 3x more likely to report significant value from their AI investments than those rushing to implement without proper data infrastructure.

What Are You Neglecting?

Before chasing the next big AI breakthrough, ask yourself:

• Is your data clean, accessible, and integrated?

• Do you have a governance framework that balances security with usability?

• Are your teams empowered to maintain data quality continuously?

The unglamorous work of data management always pays the most dividends. AI success starts with a solid foundation, not flashy tools on a shaky base.

If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.

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