Data Immaturity: A Roadblock to Advanced AI

Data immaturity, in the context of AI, refers to an organization’s underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure such as:

• Poor data quality: Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes.
• Limited data availability: Data silos across departments hinder access and comprehensive analysis, limiting insights.
• Weak data governance: Lack of policies on data ownership, compliance, and security introduces risks and restricts AI usage.
• Inadequate data infrastructure: Insufficient tools and infrastructure impede data processing and AI model training at scale.
• Unclear data strategy: Lack of a clear strategy results in uncoordinated initiatives and limited focus on valuable data for AI.

Data immaturity prevents organizations from harnessing the full potential of AI because high-quality, well-managed, and accessible data is foundational for developing reliable and effective AI systems. Organizations looking to overcome data immaturity often start by building a data strategy, implementing data governance policies, investing in data infrastructure, and enhancing data literacy across teams.

We went through all that to get to the real point of this post: data immaturity is a drag on AI adoption. Adoption is already slowing because organizations have, for the most part, already picked the low-hanging generative AI fruit (chatbots, assistants, co-pilots) and are running into data immaturity issues as they try to move toward the more valuable use cases such as workflow automation. Organizations that fail to prioritize data maturity will struggle to unlock these more advanced AI capabilities.

Data immaturity leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A 2023 study by MIT Sloan Management Review highlights that organizations with mature data management practices are 60% more likely to succeed in workflow automation than those with immature data practices. Data immaturity limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention.

Organizations must—and that’s the RFC MUST—get their data houses in order before they will be able to truly take advantage of AI’s potential to optimize workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited.