Solving the Data Readiness Conundrum: Best Practices for Excelling with AI and Advanced Analytics

Everyone wants to move faster with AI, but few mid-market companies have the data foundation to do it effectively. Our new AI Data Readiness Research Report exposes just how wide that readiness gap is and what separates organizations realizing real value from those still struggling to get their data under control. In this blog, we share key findings and takeaways from our research report, including the biggest challenges mid-market companies face and the practical steps leading organizations are taking to prepare their data for AI and advanced analytics.

What separates AI leaders from the rest?

In today’s “everything AI” environment, organizations are racing to adopt AI, but few are adequately investing in the foundation that makes AI work: their data. Clean, consistent, and accessible data isn’t just an operational nicety; it’s a strategic imperative. Without it, even the most sophisticated AI struggles to deliver meaningful or reliable results.

Many companies attempt to leverage AI for automation and as a productivity multiplier, but forward-thinking organizations go further by embracing AI to enhance decision-making across functional departments. The latter use case relies on companies’ proprietary data, with the goal of turning that data into a competitive advantage and enabling faster, smarter decisions across the enterprise.

However, achieving that vision can be challenging. Gartner estimates that 63% of organizations do not have or are unsure if they have the right data management practices for AI. And as a result, Gartner believes that 60% of AI projects will be abandoned by the end of next year due to a lack of AI-ready data.

The data management challenge extends beyond maintaining structured data in relational databases. To truly unlock AI’s potential, organizations must also manage and govern vast amounts of unstructured data – emails, documents, transcripts, logs, images, audio, etc. – often siloed in file-sharing systems like Microsoft SharePoint. Chief Data and Information Officers have long governed how humans access and use unstructured data, but they are now scrambling to adapt these strategies to support AI-driven consumption.

Despite the ominous warnings and early failures, mid-market companies continue to invest in AI to unlock growth, enhance operational efficiency, reduce costs, improve product and service offerings, and stoke innovation. In fact, a fall 2024 study commissioned by the National Center for the Middle Market revealed that faster-growing companies are more advanced in their use of AI, a finding corroborated by our study.

Mid-market companies, however, have unique challenges readying their data for AI and BI (business intelligence). Situated between behemoth enterprises with large IT budgets and smaller digital native organizations whose operations run on modern SaaS platforms, mid-size firms often lack the talent, tools, and financial wherewithal to collect and manage the hordes of source data in inconsistent formats across incompatible legacy systems.

Our AI readiness survey and what it revealed

Get the full AI Data Readiness Research Report

To better understand what undermines the mid-market’s effort to prep data for AI and BI, we conducted an online survey in September 2025 of business and technology leaders at 102 North American companies across the financial services, insurance, health sciences and consumer products goods sectors. Based on their responses, we categorized these companies into three cohorts:

• Leaders, whose revenue increased 15%+ since 2020.
• Followers, whose revenue increased from 1% to less than 15% over the previous four years.
• Laggards, whose revenue declined since the beginning of the decade.

Our findings revealed six primary categories of challenges related to data foundations, data transformation, and analytics tools that prevent AI and BI deployments from achieving business objectives. Although our analysis is focused on the mid-market, our observations and recommendations also apply to larger firms that are struggling to get their data ready for both AI and BI.

Our top-line findings reveal:

• Data readiness remains more aspirational than real. A mere 14% of mid-market organizations polled said they have achieved full data readiness. Even more alarming, 15% of companies report that 10% or less of their data is prepared for AI. Leaders are acting differently. 87% of respondents from these high-performing companies said that at least 75% of their data is ready for AI; only 11% of Followers said the same. Sadly, none of the Laggards said at least 75% of their data was prepped for AI.
• Companies struggle to effectively manage both structured and unstructured data. 57% of respondents rate their firms as “effective” or “extremely effective” at managing structured data.  Only 41% claim that their organizations have achieved the same level of proficiency with unstructured data, which we believe is hypercritical for concurrently enabling data readiness for both AI and BI.
• Data strategy remains relatively underfunded. Only 14% of spending on IT-related AI and analytics projects goes toward data strategy. This lack of investment in essential strategic planning has an impact on whether platforms, tools, and overarching AI initiatives deliver results.
• Data ingestion and analytic tools are falling short. 40% of respondents acknowledge these tools are “ineffective.” Perhaps most concerning is that nearly one in five companies (19%) report their tools are “extremely ineffective.”
• Inconsistent data architecture, poor data hygiene, and siloed systems were cited as the biggest impediments. Collectively, 85% of companies cite these issues as the top challenges. Moreover, if not addressed, they can add to technical debt, creating an environment where data remains trapped, dirty, and unusable for AI applications. Almost certainly related, 73% of surveyed organizations identify talent shortages as a primary data-readiness obstacle- likely a reflection of the limited availability of skilled professionals capable of building and maintaining the modern data infrastructure required to support AI.
• Technological shortcomings and organizational challenges inhibit AI applications from reaching production. 17% of survey respondents cited a variety of technological barriers. Organizational barriers follow closely, including budget misalignment (15%) and limited executive sponsorship (10%).

Download the full research report and get guidance on how to accelerate progress on the journey toward AI and advanced analytics. The report outlines best practices for achieving data readiness and includes insights for leaders who want to see how their organizations compare and where to focus next

Key Takeaways

• AI success starts with data readiness. Clean, consistent, and governed data is the foundation for reliable analytics and scalable AI outcomes.
• Only 14% of mid-market organizations report full data readiness—highlighting a significant execution gap between aspiration and capability.
• High-performing companies invest early in data strategy, governance, and centralized platforms to ensure accuracy, speed, and trust in insights.
• Structured data management is improving, but unstructured data remains a critical weakness that limits AI’s reach.
• Underinvestment in data strategy—just 14% of AI and analytics budgets—continues to stall progress and reduce ROI.
• Inconsistent architecture, poor data hygiene, and siloed systems create technical debt that traps valuable information.
• Talent shortages are now one of the most cited barriers to building and maintaining modern data infrastructure.
• Organizations that address these gaps are moving faster from experimentation to production, achieving measurable business outcomes with AI and analytics.

This article was written by Patrick Vinton and originated on www.analytics8.com.