Is Your Organization AI Ready?
By Cheryl Ahrens Young, IGP, CSM, CSPO, CTT+, ERMm, ECMp
[email protected]
The more things change, the more they remain the same. The graph below is from 2008, when learning servers were gaining hold in streamlining workflow processes, like accounts payable processing of invoices.
At INFOCON this year, this point was driven home, again, by the keynote speakers. An AI is only as good as the information in its LLM. In every project I worked on in the following seventeen years, not one database was 100% accurate. Errors ranged from using acronyms and abbreviations to fields missing entirely. For one project, I was given an organization’s glossary of acronyms, the meanings of which were dependent on the division that entered the data.
Why did this happen? Managing the database software was under the purview of IT and they left it up to the business units to ensure data accuracy. How accurate are you when you are on deadline or it’s Friday afternoon before a 3-day weekend? Most non-manufacturing organizations do not follow either 6-Sigma or Lean guidelines to reduce defects and there are not any data checks in place beyond a 10% random sampling after a migration.
So, the first set of tasks in one of my process improvement projects, which AI can definitely be, was to standardize and correct the data.
Agentic AI assumes that the large language models built from existing databases and systems of record metadata are accurate, factual, reliable and have data integrity. For those of us in the field of records and information management, information governance and project management, we know that an assumption leads to “ass: u & me”. This is before you add in the comprehension of intent in unstructured documents and notes which have also been used to “teach” AIs, like ChatGPT and Grok.
For this reason, you will often see a disclaimer in content created by AI that the information provided may not be accurate. There was preliminary study that it is taking, on average, two hours for a person to correct a document that AI has created because they have to verify the facts, like citations, before it can be released.
What can we do? Use Microsoft’s own recommendations for a file plan and retention labels to get a seat at the table of your AI team! (Troubleshoot Copilot Tuning document generation | Microsoft Learn)
If you didn’t attend InfoCon this year, the keynote speakers presentations can be found here: Troubleshoot Copilot Tuning document generation | Microsoft Learn
And keep checking the OCARMA website for information on the upcoming 4th Annual California Chapters Summer conference!
