Articles

Is Your Organization AI Ready? (Part II)

Is Your Organization AI Ready?

By Cheryl Ahrens Young, IGP, CSM, CSPO, CTT+, ERMm, ECMp 
[email protected]

An AI is only as good as the information in its LLM.  

AI developers assume that the large language models built from existing databases and systems’ of record metadata are accurate, factual, reliable and have data integrity.  My experience in business process automation with learning servers has taught me that assumptions lead to “ass: u & me”.   

You will often see a disclaimer in content created by commercial “free” AI that the information provided may not be accurate. 

What is the root cause for the inaccuracies in an AI generated document?  For AI hallucinations? 

ROT – Redundant, Obsolete and Trivial records fed to the LLM.  Added to that, lack of integrity in the information – abbreviations, mis-spellings, homonyms that mean entirely different things (accept vs except), and acronyms that are industry specific (CRM could be Certified Records Manager or Customer Relations Management).

How do you clean up the ROT and improve the integrity of the LLM?

A legally defensible, robust records and information management program which addresses not only retention but integrity, accuracy and accountability.  This all starts with a current records retention schedule that is then applied to the systems of record, shared drives and any other source of information that builds the LLM.  In addition, processes and procedures outline how to name records and enter consistent data into the systems of record.

Who knows about records retention schedules and naming conventions?

Records and Information Managers! 

The question is – how do we get a seat at the AI table?  

Where I’ve had success in getting the technical team to listen is inviting them to a lunch and learn meeting and talking about RIM and knowledge management.  If your company follows Agile or Scrum project methodologies, ask to be part of the team to develop the customer stories.  Your story might revolve around an AI identifying duplicate and near duplicate copies across shared drives and systems of record to reduce the ROT to then provide the best possible information for a iterative process improvement of the information fed to the LLM.