What Happens When Content Management Morphs into AI? A Jargon Blast
September 16, 2025
Sadly I am a dinobaby and too old and stupid to use smart software to create really wonderful short blog posts.
I did a small project for a killer outfit in Cleveland. The BMW-driving owner of the operation talked about CxO this and CxO that. The jargon meant that “x” was a placeholder for titles like “Chief People Officer” or “Chief Relationship Officer” or some similar GenX concept.
I suppose I have a built in force shield to some business jargon, but I did turn off my blocker to read CxO Today’s marketing article titled helpfully “Gartner: Optimize Enterprise Search to Equip AI Assistants and Agents.” I was puzzled by the advertising essay, but then I realized that almost anything goes in today’s world of sell stuff by using jargon.
The write up is by an “expert” who used to work in the content management field. I must admit that I have zero idea what content management means. Like knowledge management, the blending of an undefined noun with the word “management” creates jargon that mesmerizes certain types of “leadership” or “deciders.”
The article (ad in essay form) is chock full of interesting concepts and words. The intent is to cause a “leadership” or “decider” to “reach out” for the consulting firm Gartner and buy reports or sit-downs with “experts.”
I noticed the term “enterprise search” in the title. What is “enterprise search” other than the foundation for the HP Autonomy dust up and the FAST Search & Transfer legal hassle? Most organizations struggle to find information that someone knows exists within an organization. “Leadership” decrees that “enterprise search” must be upgraded, improved, or installed. Today one can download an open source search system, ring up a cloud service offering remote indexing and search of “content,” or tap one of the super-well-funded newcomers like Glean or other AI-enabled search and retrieval systems.
Here’s what the write up advertorial says:
The advent of semantic search through vectorization and generative AI has revolutionized the way information is retrieved and synthesized. Search is no longer just an experience. It powers the experience by augmenting AI assistants. With RAG-based AI assistants and agents, relevant information fragments can be retrieved and resynthesized into new insights, whether interactively or proactively. However, the synthesis of accurate information depends largely on retrieving relevant data from multiple repositories. These repositories and the data they contain are rarely managed to support retrieval and synthesis beyond their primary application.
My translation of this jargon blast is that content proliferation is taking place and AI may be able to help “leadership” or a regular employee find the information needed to complete work. I mean who doesn’t want “RAG-based AI assistants” when trying to find a purchase order or to check the last quality report about a part that is failing 75 percent of the time for a big customer?
The fix is to embrace “touchpoints.” The write up says:
Multiple touchpoints and therefore multiple search services mean overlap in terms of indexes and usage. This results in unnecessary costs. These costs are both direct, such as licenses, subscriptions, compute and storage, and indirect, such as staff time spent on maintaining search services, incorrect decisions due to inaccurate information, and missed opportunities from lack of information. Additionally, relying on diverse technologies and configurations means that query evaluations vary, requiring different skills and expertise for maintenance and optimization.
To remediate this problem — that is, to deliver a useful enterprise search and retrieval system — the organization needs to:
aim for optimum touchpoints to information provided through maximum applications with minimum services. The ideal scenario is a single underlying service catering to all touchpoints, whether delivered as applications or in applications. However, this is often impractical due to the vast number of applications from numerous vendors… so
hire Gartner to figure out who is responsible for what, reduce the number of search vendors, and cut costs “by rationalizing the underlying search and synthesis services and associated technologies.”
In short, start over with enterprise search.
Several observations:
- Enterprise search is arguably more difficult than some other enterprise information problems. There are very good reasons for this, and they boil down to the nature of what employees need to do a job or complete a task
- AI is not going to solve the problem because these “wrappers” will reflect the problems in the content pools to which the systems have access
- Cost cutting is difficult because those given the job to analyze the “costs” of search discover that certain costs cannot be eliminated; therefore, their attendant licensing and support fees continue to become “pay now” invoices.
What do I make of this advertorial or content marketing item in CxO Today. First, I think calling it “news” is problematic. The write up is a bundle of jargon presented as a sales pitch. Second, the information in the marketing collateral is jargon and provides zero concrete information. And, third, the problem of enterprise search is in most organizational situations is usually a compromise forced on the organization because of work processes, legal snarls, secret government projects, corporate paranoia, and general turf battles inside the outfit itself.
The “fix” is not a study. The “fix” is not a search appliance as Google discovered. The “fix” is not smart software. If you want an answer that won’t work, I can identify whom not to call.
Stephen E Arnold, September 19, 2025
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