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CIOs: Getting full value from AI solutions is more labor-intensive than anticipated

How to scale into enterprise effectively in 2024?
“If you don’t have your data house in order, AI is going to be less valuable than it would be if it was. You can’t just buy six units of AI and then magically change your business.”

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Despite the promise of AI tools like ChatGPT, Gemini to streamline business processes, many organizations find that implementing them effectively requires significant effort.

“It has been more work than anticipated,” said Sharon Mandell, chief information officer of network tech company Juniper Networks, who is testing tools from several vendors but doesn’t feel ready to put any into production. 

Via WSJ
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Other issues pointed by CIOs include:

  1. Data Quality Issues: Inaccurate or outdated enterprise data hampers AI performance. Instances at companies like Cargill and Eli Lilly show AI tools giving incorrect or outdated answers, reflecting issues with the data they access.
  2. Data Management: Effective use of AI requires clean, accurate, and up-to-date data. Organizations are focusing on data validation, cleaning, and curation to ensure reliability. This includes creating a “golden record” free of inconsistencies.
  3. User Preparedness: Successful AI integration involves not only data management but also educating users on how to interact with the tools. This includes proper prompting and context provision.

“If you don’t have your data house in order, AI is going to be less valuable than it would be if it was. You can’t just buy six units of AI and then magically change your business.” – Google Cloud Chief Evangelist Richard Seroter 

Given the revenue pressure, do you think CIOs will be able to justify investing in data cleanup for a..futuristic AI utopia?

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