Search to Decision: A Journey Likely to End in a One Star Hotel
July 10, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
I read a pretty wild and wooly essay intended for top dogs in organizations. My concern is that some of these deciders will fall for the razzle dazzle and end up in a bit of a swamp. The essay is “AI Knowledge Management Moves from Search Tool to Enterprise Decision Layer.” Yeah, okay. Enterprise search is not exactly a smooth running Toyota RAV in most organizations. Some information is not findable. Usually there are good reasons for the voids. (Drop into a pharma company and see if you can find info about a current clinical trial.)
Okay, Midjourney. Sort of disappointing.
The write up begins with a “typical” and I assume compelling example of a real life situation in the ideal corporate entity in the United States. Here’s the use case:
This demand is especially strong in organizations where information is spread across multiple systems. A salesperson may need product guidance from a knowledge base, a support agent may need context from past tickets and a product manager may be looking for insights from customer conversations. When that information lives in separate places, employees often spend time searching for answers or end up making decisions with only part of the picture. AI-powered platforms aim to reduce that friction. They can summarize long records, suggest relevant content and answer questions based on approved sources. The strongest systems also show where the answer came from, which helps users judge whether the information is current and reliable.
On the surface, the straw man seems reasonable. Let’s consider it from three angles.
First, the information required does not appear to be related to a law suit or the shroud of legal discovery. The information is not part of a government project operating under rules for classified information. The information does not seem to be that which is in emails, chats, or files on a computing device of an employee working remotely or on a device used by a contractor from a third party performing work for the organization. I am not sure if the information needed to answer certain questions is likely to be a system given indiscriminate access to the content in an organization.
Second, the old IDC chestnut that employees spend lots of time searching for information. Okay, but based on research in which I was involved at a blue chip consulting firm, employees find information this way: [a] A quick Google search, [b] Ask someone, [c] flip through local information on a laptop, a pile of folders on a credenza, etc. The searching angle does not hold up when employee work practices are observed, documented, and analyzed. A bonus insight: The closer one is to the top of the management hierarchy, asking and making a judgment call appear as a favorite method among a majority of senior managers.
Third, AI systems can output answers and suggestions based on approved sources. Okay? What is the time and cost to approve sources? How does an organization bumbling from one opportunity or crisis approve new sources, get them into the training set, and benefit from the flow of “new” information? The answer is, “Most AI systems can but don’t?” Why? How about cost and human fiddling around time? Based on the research I have done into information retrieval over the years, talk about fresh data available in real time is baloney. When an employee cannot locate the PowerPoint the sales person cooked up seal a deal confirmed in an email sent via Yahoo, that employee tries to “get in touch.” Yeah, good luck with that in today’s work environment.
Fourth, the user — that is, the employee who is fully informed, intelligent, and attentive k— will judge whether the information is current and reliable. What craziness is this? No employee knows if the information output is current, complete, and accurate. The painful truth is that people perceive the computer as being correct. This means that employees just use what’s output.
As you can tell, this write up is a marketing confection.
Here’s the conclusion to the write up:
AI-powered knowledge management is becoming more than enterprise search with a new interface. It is becoming a decision layer that connects people to usable institutional knowledge. The companies that succeed will be those that combine AI capability with governance discipline and practical workplace integration.
This passage contains a small nugget or uranium ore; to wit, “AI powered knowledge management is becoming more than enterprise search with a new interface.” Yes, I agree. It is going to become the glittering chaff of marketing pitches in the balance of 2026 and into 2027. Finding information is hard. Traditional enterprise search said, “No, it’s not.” Well, after the implosion of enterprise search vendors, licensees learned that search vendors were blowing smoke. Now the cycle is going to repeat. AI is a finding utility. Enterprise search is hard. AI is unlikely to make search better, faster, or cheaper, but it will definitely hallucinate and lead to some interesting decisions. Maybe companies should stick with paper, folders, and filing cabinets in separate organizational units. That one, one might know whom to ask for an answer. You may not get it, but at least you were close to a source.
Stephen E Arnold, July 10, 2026
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