Overall, the report underscores that though enterprises are desirous to harness the potential of generative AI, important infrastructure and knowledge administration groundwork is required to understand its advantages and guarantee sustainable, long-term success.
A CIO’s to-do listing from hell
Most enterprises knew that they had knowledge points lengthy earlier than AI began to affect the market in important methods. Indeed, most have averted AI and enterprise intelligence investments attributable to their insecurity of their knowledge. Nobody within the firm fully understands the place the info is and what it means. Silo leaders personal and handle the info, so there isn’t a single supply of reality for issues so simple as what a buyer is and the place buyer knowledge ought to come from. Redundancy is widespread in gross sales, manufacturing monitoring, and different areas the place the info is mismanaged.
How did issues get this unhealthy? Most enterprises spent years centered on new, shiny objects reminiscent of ERP and CRM programs, which comprise essential knowledge, nevertheless it’s locked up in proprietary knowledge shops. After ERP and CRM got here knowledge warehousing, distributed programs, knowledge integration, and now cloud. Through all of it, knowledge has gotten extra complicated, distributed, and heterogeneous, with a scarcity of centralized management. Too many firms don’t perceive the metadata and might’t hint knowledge correctly by way of the enterprise processes. Also, acquisitions have pushed some knowledge redundancy; many enterprises nonetheless function the older programs that got here with the companies they acquired. Now, we’re dealing with AI, the place the which means, construction, and truthfulness of information usually are not elective.
