TRENDING
Lori MacVittie, F5 Distinguished Engineer
Early adopters impacted by data immaturity
Every survey on Generative AI, including our own, points to one inescapable conclusion: data immaturity is going to get in the way of fully realising the potential of Generative AI.
When we asked about challenges to AI adoption in our 2024 State of Application Strategy report, the top response with 56 % of respondents was data immaturity. A quick look around the industry validates that data immaturity is a serious obstacle on the AI adoption path.
A 2023 study by MIT Sloan Management Review highlights that organisations 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.
Data immaturity prevents organisations from harnessing the full potential of AI because high-quality, well-managed, and accessible data is foundational for developing reliable and effective AI systems.
Organisations 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.
Data immaturity is a drag on AI adoption. Adoption is already slowing because organisations have 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. Organisations that fail to prioritise 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.
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• Poor data quality: Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes.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, in the context of AI, refers to an organisation’ s underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively.
• Consider employee experience across the hybrid work environments and collaborative smart spaces.
• Determine whether the infrastructure will scale and adapt as technology needs change, particularly when AI workloads become part of the equation.
Align stakeholders
Engage all key stakeholders across the organisation early – from IT teams to business-unit leaders. This will be beneficial to understand their needs, concerns, and address these in the networkmodernisation strategy.
Not in one go
Implement a phased approach to network modernisation by prioritising critical systems and areas where the improvement will deliver the most immediate value. Assess how the organisation is evolving and plan new features and functionalities required to support these changes. p
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