Data Quality and Accessibility: The Real Bottlenecks in AI
For many enterprises, the hardest part of AI isn’t building algorithms, it’s getting data ready. According to a finding by 66 Degrees, only 18% of organizations feel they have the foundational data readiness for AI integration . The rest are held back by siloed systems, poor quality, and inconsistent governance that stall initiatives before they scale. As Gianthomas Volpe of Alation observes, “the critical oversight in many AI initiatives isn’t the sophistication of the algorithms, but the quality and governance of the data feeding those algorithms” .
When data is messy or inaccessible, even the best AI models will stumble. Over four out of five companies still struggle with data quality in their AI projects . Drew Clarke of Qlik warns, “As companies rush to implement AI, they risk building on flawed data, leading to biased models, unreliable insights, and poor ROI”. In other words, poor data is the silent killer of AI agility.
Laying a Strong Data Foundation Accelerates AI
Improving data readiness can dramatically speed AI delivery. Clean, well-governed, and accessible data is the hidden accelerator for fast pilots and trusted outcomes. As per a USDSI finding, one global bank saved ≈$50 million in one year by deploying self-service analytics to 5,000 employees, eliminating bottlenecks and accelerating insight delivery .
According to EY, 83% of executives say poor data infrastructure is a major AI adoption barrier. Investing in stronger data foundations is what turns AI strategy into AI execution. With trusted, accessible data, pilots move from concept to production in weeks rather than months, and results are credible enough to earn enterprise-wide buy-in.
Practical Steps to Prepare Data for AI Pilots
1. Audit the Data Landscape
Start with a candid review of your data sources. Identify silos, quality issues, and bottlenecks that slow teams down. This highlights the gaps blocking agility.
2. Improve Quality and Definitions
Dedicate effort to cleaning and standardizing data before deploying AI. Define common terms, implement automated checks, and enforce version control so everyone works from “one truth” .
3. Establish Governance with Access
Balance security and accessibility. Modern governance frameworks allow safe self-service data access using role-based controls and catalogs. This ensures compliance without slowing down teams.
4. Modernize Platforms and Pipelines
Legacy silos delay AI. Invest in cloud-ready data platforms or data mesh architectures that integrate sources and automate pipelines. Fresh, real-time data pipelines are the AI-ready supply chain.
5. Launch Focused Pilots
With quality data in place, select a narrow use case with clear business impact. Deliver it in weeks, measure results, and use the win to build momentum.
6. Build Data Literacy and Culture
Upskill teams across levels so they can use and trust data. Encourage collaboration between business and technical staff to align AI projects with real needs. A data-driven culture ensures readiness isn’t a one-off effort but a lasting advantage.
A Consultant’s Perspective
For Chief Data Officers, the message is clear: AI readiness starts with data readiness. Boards want proof of value, not experiments that drag on. Without trusted data, pilots stall. With it, AI can launch quickly, deliver measurable ROI, and scale sustainably.
It may not be glamorous work, but strengthening your data foundations pays off many times over. With accessible, reliable data, AI teams move faster and business leaders gain confidence in the results. Data readiness is the accelerator hiding in plain sight. If you are looking to fuel it now, reach out to Mesh for a consultation on not just how to start your AI journey faster, but how to scale it smarter as well.