Until CIOs confront issues like siloed, redundant, or untraceable data, generative AI will remain an elusive dream.
AI, particularly generative AI, is a data game. If your data is in shambles, your AI results will be too. AI systems thrive on gigabytes of clean, accurate data to be effective. They detect patterns and provide insights for strategic business decisions. But what happens if your data is dirty or inaccurate? You’ll get faulty AI inferences, or worse, incorrect answers you might not realize are wrong until it’s too late.
And let’s not kid ourselves: AI isn’t cheap to build and run.
Nowhere Near Ready for AI
A recent Enterprise Strategy Group report surveyed 800 IT decision-makers and found that more than 60% of organizations face significant gaps in AI readiness, especially regarding infrastructure and data ecosystems.
Despite the excitement around AI’s potential, many organizations are unprepared for its broad adoption. Over half of AI decision-makers worry their IT teams can’t keep up with the rapid innovations driven by generative AI. Enterprises need to revamp data processes and bolster infrastructure, among many other tasks.
Success metrics for generative AI projects vary widely. About 40% of organizations track progress through qualitative impact analysis, AI response accuracy, or user and process benefits. Roughly 38% prioritize cost savings. As AI initiatives claim a larger chunk of enterprise budgets, the pressure to deliver ROI will only grow.
The report makes one thing clear: while businesses are keen to leverage generative AI, they need substantial infrastructure and data management improvements to realize its benefits and ensure long-term success.
A CIO’s Nightmare To-Do List
Most enterprises have been aware of their data issues long before AI became a hot topic. Many have shied away from AI and business intelligence investments due to a lack of confidence in their data. Often, no one in the company fully understands the data’s location or meaning. Silo leaders control and manage the data, leading to no single source of truth for even basic information, like customer data. Redundancy is rampant in areas like sales and production tracking, where data mismanagement is the norm.
How did things get so bad?
For years, enterprises chased after new shiny objects like ERP and CRM systems, which, while containing valuable data, locked it up in proprietary stores. This was followed by data warehousing, distributed systems, data integration, and now the cloud. Throughout this evolution, data has become more complex, distributed, and heterogeneous, lacking centralized control. Companies often don’t understand their metadata and can’t trace data through business processes. Acquisitions add to the redundancy as older systems from acquired businesses remain operational. Now, with AI, the accuracy, structure, and truthfulness of data are critical.
CIOs need to clean up their data before AI can deliver any value. For many, this may be too costly or risky. The inability to adopt AI due to data issues could spell doom for some businesses, especially as competitors leverage AI as a powerful innovation tool. We’re heading towards a divide of AI haves and have-nots.
Closing the Gaps
Enterprises, take note: if your data isn’t ready, steer clear of AI. Many failed AI projects can be traced back to poor data ecosystems and a reluctance to fix them. Some companies believe AI will fix their data problems, but that’s a myth.
My advice? Improve your data hygiene—it offers more benefits than just AI readiness and is worth the investment.
Often, AI investments are seen as ways to address past mistakes, and many CIOs prefer to leave the problem for their successors. Convincing your leadership and board to invest in fixing years of data neglect without direct, easily traceable business benefits is tough. Most CIOs shy away from this conversation.
However, fixing your data issues is crucial, whether or not you plan to use AI. Let AI be the motivation to get your enterprise’s data in order. It’s a win-win.
