Enterprise Search: A Story That Repeats Again and Again
July 2, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
I spent some time looking to see if I have hard copies of the books I have written in my long, non-illustrious career. I have copies on the Enterprise Search Report, published more than a decade ago by an outfit of which I have lost track. The ESR was a best-seller. (That means I actually made a profit over the three editions of the 300 page document. Amazing!)
I included some discussion of Elastic and its predecessor Compass. Shay Banon was a person with a vision: Open source search and retrieval was the way to meet organization’s need for finding information it already possessed. The Elastic effort did reasonably well and spawned revenue via professional services, a pivot to cyber security via log file analysis, and a good marketing approach relying on training sessions in places like the Big Apple.
Does the coach’s advice make sense? Probably not. But coaches have to come up with something that does not create more problems for himself, his organization, and his gifted professionals. MidJourney, good enough.
Then two things happened, and I am not sure too many people noticed. I did, but after three editions of the ESR, I was eager to do work in other sectors. I kept my eye on a couple of outfits, and Elastic was one of these.
So what happened?
An outfit called Lucid Imagination later renamed to Lucid Works grabbed Lucene and Solr. The idea was that Lucid Words was a better commercial search and retrieval system. It’s still around, but the key event is not immediately clear (I wanted to write “lucid” but I didn’t.) Amazon hired a couple of go-getters from Lucid Works. That cross pollination of information was the seed that germinated into what I think of as the Amazon bulldozer driving slowly over Elastic. The result is that Amazon now offers search in flavors and one of those, in my opinion, is sure similar to Elastic’s approach.
The second event was the mostly accidental and quite messy spilling of AI technology into organizational information retrieval. Elastic jumped into that game, but it appears that people like using hallucinating artificial intelligence more than key word queries. I think that Elastic like Apple and Telegram are now suffering from the firm’s AI strategy or imprecision of its AI tactics.
If I were to write another Enterprise Search Report which I am not going to do, I would not include companies like Elastic or some of the firms that Gartner Group is presenting as enterprise search leaders. It’s time to put down those stone axes with AI trimmings. Just a thought from a dinobaby, of course.
Why did I take this quick trip down memory lane? I read “CEO Ash Kulkarni’s Organizational Announcement to Elastic Employees.” The write up said on June 24, 2026:
The industry is changing. Advances in AI, automation, and technology are reshaping how work gets done, and we’re changing with them. Customer expectations are increasing and evolving faster than ever before, and this requires us to move faster and operate leaner than we have before. We provide the data store and context engine for the technology that is changing the world, and we help observe and protect the infrastructure that runs it. We’re in a great position to lead here. To do it, we’re shifting our pace of innovation, simplifying how we operate, and investing in new skills. That’s what this reorganization is for: a simpler structure, with fewer layers, less complexity, and less friction.
My translation: “We’re need to cut costs. Hire more sales people who can close deals. Regroup to come up with a winning AI strategy. You are the first batch to be able to run a taco truck. More of you will have that opportunity in the next few months.”
I don’t think AI is particularly reliable unless the smart part is trained on quite specific content and then continuously updated to avoid the wonderful Bayesian drift like that suffered in the early Autonomy neural linguistic programming methods from the late 1990s. Drift is part of the game, and that’s why expensive training and retraining are part of the “fixing hallucination” function of the Googley transformer method.
Hybrid AI and traditional search models are unlikely to reduce the “cost” of finding information in an organization. Why? Organizations are silos of data. The legal department, particularly when engaged in litigation, is a little silo and sharing some information can have outsized downsides. The pharma and semiconductor R&D outfits use silos too. Powerful people in organizations expect silos. Index their mobile text messages and join the Elastic folks running a taco truck. Some government contracts require silos. Screw up government processes and you too can experience what I refer to as the “Anthropic wake up call.” Plus, organizations — contrary to popular belief — are busy outputting data and information, usually in a quite uncontrolled way.
The information at your fingertips pitch for enterprise search usually crashes into unseen shoals or get blown into rocks by business conditions.
Several observations:
- Enterprise search is in 2026 a difficult service to deliver, keep users happy, and costs low.
- Enterprise search without AI is expensive to pull off. That’s why most of the outfits I profiled in the three editions of the ESR are gone, morphed into some else, or embedded in some other enterprise service like document conversion.
- Enterprise search with AI is super difficult and potentially even more costly. US companies may not be comfortable loading their data into Qwen and hoping for the best in order to reduce token costs.
Net net: Elastic has had a good run. The big question is, “Can it handle the next leg of the enterprise software race ?”
Stephen E Arnold, July 2, 2026
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