This is the second installment in Netrio’s Executive AI Tip Series for leaders embarking on practical, scalable AI strategies and implementation plans. Make sure to go back and check out Create Clarity Before Change: The AI Governance Tip Many Miss before moving on to this blog. 

Governance is the guardrail to keep you on track. Data is the fuel to propel you forward. Together, they determine how far and how safely AI can take your organization.

AI Is Only the Tip of the Iceberg

Every executive is feeling the gravitational pull of AI. Boardrooms want results. Teams want automation. Customers want faster, more personalized experiences. And the technology itself seems limitless, until you try to use it at scale.

The truth is that AI is the visible part of the transformation. Data is the massive, invisible structure beneath it.

AI will only ever be as good as the data that holds it up.

Sources estimate 80% of AI projects fail. Many of those failures are rooted in data quality and data governance issues, not model performance. That statistic alone should stop every executive in their tracks. The most sophisticated AI models in the world cannot compensate for duplicate, dirty, incomplete, outdated, or siloed data.

If your data is a mess, AI will magnify it.

Before AI can deliver accuracy, insight, automation, or ROI, mid-market leaders must fix the data foundation. It is not glamorous work, but it is the work that makes everything else possible.

This is the tip many executives miss, not because they’re unaware of their data issues, but because the urgency of AI makes them feel like they can’t afford to slow down. In reality, you can’t afford not to.

Why Data Quality Determines AI Success

AI models rely entirely on the information you feed them. For predictive functions, automation, decision support, or even enhanced search, the math is simple:

Good data → good AI.
Bad data → bad AI.

AI maturity depends on data maturity. Figuring out where your organization is on the maturity curve is crucial and something we detail extensively in our ebook From Buzzword to Blueprint: AI Success for Mid-Market Organizations.

Here’s what every business leader must understand:

• AI models require structured, consistent, accurate data to perform reliably.
• Dirty or duplicated data sabotages predictive accuracy, causing everything from hallucinations to faulty reporting.
• Siloed data produces siloed answers, undermining enterprise-wide decision-making.
• Lack of metadata or lineage creates opacity, making it impossible to track how an AI came to its conclusion.

Many organizations enthusiastically pursue AI investments, yet the data foundation needed to support those investments lags behind. Gartner predicts that over 60% of organizations will abandon AI projects due to a majority (63%) not having—or are unsure if they have—the necessary high-quality data to pursue those initiatives.

The mid-market feels this most acutely. Years of decentralized systems, rapid SaaS adoption, inherited technical debt, and lean teams make “clean data” feel like a luxury. But with AI, that luxury becomes a requirement.

The Hidden Costs of Poor Data, or The Problems Leaders Don’t See Yet

Bad data has always come with costs. But with AI, it can be catastrophic. Here’s what happens when AI is built on shaky data foundations:

• Inaccurate insights → flawed strategic and operational decisions
• Hallucinated outputs → higher error rates and downstream rework
• Siloed data → inconsistent customer, employee, and partner experiences
• Compliance gaps → regulatory and audit exposure
• Operational noise increases → teams must check, validate, and correct AI outputs
• Diminished ROI → AI tools underperform or fail to gain adoption

Executives often underestimate these risks because they’re not immediately visible. But they show up eventually, whether in customer churn, compliance findings, IT backlog, or diminishing confidence in AI programs.

What We Mean by “Fixing Your Data First”

Data readiness isn’t about perfection. It’s about stability, integrity, and clarity. For mid-market organizations, data readiness boils down to three executive-friendly pillars:

1. Data Integrity

This includes:

• Accuracy
• Standardization
• Complete metadata
• Validation rules
• Deduplication

Without integrity, AI tools can’t distinguish signal from noise.

2. Data Accessibility

AI thrives on connected ecosystems. You need:

• Broken-down silos
• Integrated data sources
• Real-time or near-real-time pipeline availability
• Clear APIs and interoperability

Disconnected data leads to disconnected intelligence.

3. Data Governance

You need control around:

• Ownership and stewardship
• Quality standards
• Privacy boundaries
• Data lineage
• Retention and access policies

This tightens the relationship between your first AI tip—governance—and this one. Governance sets the rules; high-quality data makes the rules meaningful.

How a Strong Data Foundation Improves AI Outcomes

Fixing data isn’t maintenance work. It is strategic work that directly improves every AI initiative you pursue.

Executives will see:

• More accurate outputs, leaving fewer errors and far less rework
• Better forecasts and predictions, leading to stronger decision-making
• Streamlined workflows, bringing higher team productivity
• Greater trust in AI, causing faster adoption across the enterprise
• Stronger compliance posture, creating reduced audit and regulatory risk

When the data becomes trustworthy, AI stops feeling experimental and starts to become operational. Fix the foundation, and AI stops feeling mysterious. It starts to become measurable. This is the practical shift executives must make if they want AI to deliver real enterprise value.

Fixing Your Data Before Deploying AI

Here is a practical, actionable list for leaders who want to prepare their organization for AI at scale:

1. Audit your current data health: Identify accuracy gaps, completeness issues, and fragmentation across systems.

2. Identify high-value datasets tied to priority AI use cases: Don’t fix everything, fix the most important items, and focus on what moves the needle.

3. Consolidate sources where possible; integrate or pipeline where not: Removing silos is one of the fastest ways to increase AI value.

4. Implement data governance policies: Clarify who owns which data, who maintains quality, and how it flows.

5. Invest in tools/platforms that improve real-time visibility: NetrioNow dashboards, risk registers, and reporting tools create transparency across environments.

6. Start with one domain: Prove success, then build internal trust, then scale horizontally.

Data improvement doesn’t need to be slow or overwhelming. It just needs to be intentional.

Your AI Can Only Be as Smart as Your Data

Executives are right to feel urgency around AI adoption. But the organizations getting it right—the ones seeing meaningful efficiency gains, risk reduction, and competitive advantage—aren’t the ones moving fastest. They’re the ones building the strongest foundation.

And that foundation is data.

Fix the data, and your AI will deliver better results, clearer insights, and sustainable ROI. Skip this step, and AI will become unpredictable at best. Dangerous at worst.

Learn how data readiness fits into your broader AI strategy. Download our ebook, From Buzzword to Blueprint: AI Success for Mid-Market Organizations for a step-by-step roadmap built for the mid-market.

Ready to strengthen your journey with AI? At Netrio, We’ve Got This. Contact us for an AI plan that fits your organization.