Overall, the report underscores that although enterprises are eager to harness the potential of generative AI, significant infrastructure and data management groundwork is required to realize its benefits and ensure sustainable, long-term success.
A CIO’s to-do list from hell
Most enterprises knew they had data issues long before AI started to impact the market in significant ways. Indeed, most have avoided AI and business intelligence investments due to their lack of confidence in their data. Nobody in the company completely understands where the data is and what it means. Silo leaders own and manage the data, so there is no single source of truth for things as simple as what a customer is and where customer data should come from. Redundancy is common in sales, production tracking, and other areas where the data is mismanaged.
How did things get this bad? Most enterprises spent years focused on new, shiny objects such as ERP and CRM systems, which contain important data, but it’s locked up in proprietary data stores. After ERP and CRM came data warehousing, distributed systems, data integration, and now cloud. Through it all, data has gotten more complex, distributed, and heterogeneous, with a lack of centralized control. Too many companies don’t understand the metadata and can’t trace data properly through the business processes. Also, acquisitions have driven some data redundancy; many enterprises still operate the older systems that came with the businesses they acquired. Now, we’re facing AI, where the meaning, structure, and truthfulness of data are not optional.
