Sequoia Capital recently released a report suggesting we “imagine a world where every profession has its own specialized AI search engine”. This is a fallacy I call ‘one ring to rule them all’, the hope that if we could build the ultimate search & AI product it would automatically ingest any data and solve the enterprise use case for a particular sector. I’m confident in saying this is an impossible dream. So what am I Tolkein about? (sorry, couldn’t resist!)
Why can’t it just…
Anyone working in search will be familiar with the complaint “why can’t it just work like Google”. Users assume that the consumer search engine they’re familiar with (most often, Google web search) is a perfect fit for the enterprise. They’re unhappy with the performance and accuracy of search at work and just want it to be as easy to use and helpful as what they use at home.
Google did of course try to take advantage of this with their Google Search Appliance, a rack-mounted ‘search engine in a box’ with the familiar branding. Not many will remember the GSA now, but for a few years it was a popular solution, likely to receive management approval and budget signoff due to the familiar Google brand. Just point it at your data and off you go! Except, of course, your data wasn’t always that easy to get to, and would often need quite a lot of work to extract, condition and massage before it could be put into the magic box – great news for system integrators, many of whom made a good living from installing and supporting the GSA.
What people often mean however, is “why can’t we just have one easy-to-use search engine at work”. So why can’t this be done, especially with all the power of search & AI we now have at our disposal?
In any business there are multiple use cases, user tasks and information needs. A lawyer, for example, needs to consult past cases for precendents, find all the letters and emails they’ve sent, organising all this by case and matter, plus figure out how to book holiday time, search company records, keep up with legal industry news etc. Some of this information lives within the company intranet, some is in public repositories, some is buried in a WordPerfect document from 1993, a database entry with bad metadata or a dodgily scanned PDF. For each type of data different ranking models may be needed – in some cases recency is most important, in others relevance, or perhaps a blend of the two. The information may need to be presented in different ways, with or without summaries or previews, or the search function itself may be buried inside another application, such a document management system or legal information system.
Enterprise search projects often promise, but never quite manage to reach all these silos. “Search over all your corporate data from one place” is a common theme. However, very simple things can often have the biggest impact on user experience – for example, why even search at all, when a redirect to a single page can give you the answer to “how do I book holiday time”. If your user wants better search in their case management system, fix it there, don’t force them to use another new system. Perhaps we decide that we shouldn’t even allow people to find some of our less reliable data.
So why bother with AI for enterprise search?
I’m certainly not implying that AI techniques won’t (and in many cases already have) revolutionise the world of enterprise search. With the ColPali model (now available in the leading Vespa search platform) we have a way of better searching PDFs (a print format that should never have become a content format in my humble opinion) on a visual basis rather than having to painfully extract text, tables and images. Swirl have cleverly turned the federated search model on its head, relying on the retrieval capabilities of the underlying databases, content management systems etc. but using a LLM to combine and summarise this heterogenous data into a coherent whole. Vector embeddings help boost retrieval by matching meaning rather than just keywords and Retrieval Augmented Generation helps reduce the chance of LLMs hallucinating an answer to your users’ questions.
However none of these techniques completely solve the very real problems of enterprise search – many of which are people problems, not technology problems.
Learning the world
The challenge of building a search system for a particular business sector is that first, you have to learn how people in that sector think – and it’s different for each sector. Building an enterprise search system for lawyers won’t necessarily help you build one for medics, or telecom engineers. You’ll need to employ ex-lawyers to do this effectively; create deep relationships with your legal clients; understand common standards, taxonomies, the history of the profession.
A good strategy for developing an effective ‘Google for my sector’ is to focus on solving actual user problems, not trying to ‘boil the ocean’. Do we really need to search legal cases from 20 years ago, or would it be better to help our new lawyers find an experienced colleague to ask? Sometimes the best strategy is to figure what not to index, rather than throwing every single piece of data into the mix. Data quality is the killer here – an item with bad metadata, tagged in the wrong way, can easily damage search result quality no matter if you are using traditional or AI techniques. Creating effective data curation processes is vital, but seldom a high priority.
Another challenge is that there is a huge amount of inertia and politics within large businesses (e.g. those potentially willing and able to pay for enterprise search projects and with sufficient data volume to make it worth it). Calculating the financial benefit of better search (so you can get sufficient budget for your project) has always been difficult, usually ending up with some vague promise of saving users’ time and thus improving efficiency. Different departments may compete and actively resist the introduction of new features that they perceive may put them at a disadvantage. Some people may not even want to be found by a people search!
Your search team – assuming you have one at all – will probably be under-resourced and reactive, rather than proactive. They may not have time to keep up with the firehose of AI-related news, research and use these new techniques, spending most of their days fixing data problems, responding to user complaints prioritised by how important that user is in the organisation. It’s very hard to say no when a senior manager insists that being able to find that opinion piece they wrote for the company intranet is the single most important search problem to solve today.
Are these people problems insuloble? Of course not, but they require patience, understanding and diplomacy, not just a blanket introduction of new technology.
The giants are coming
As Jo Kristian Bergum wrote recently, the AI giants are now focused on enterprise search, but may be unaware of the “soul-crushing challenges that will drain your team’s energy long before you can sprinkle AI magic on top.” We’re likely to see some grand announcements from OpenAI, Google and others during 2025 (I don’t think we’ll see a new yellow box though). Let’s hope that they will take note of the decades of work that’s already been done to understand enterprise search – a good starting point would be the many books written by Martin White (start with Enterprise Search and A History of Enterprise Search) or my own small contribution with Professor Udo Kruschwitz shown on the right (click for the free PDF).
Enterprise search has always, and will remain a hard problem. We can combine the exciting possibilities of AI with a deep understanding of the people and organisations we’re trying to help, focusing on pragmatic solutions. Let’s begin our quest!
Want to build enterprise search that truly serves your users? Interested in taking advantage of both AI & traditional techniques? Let’s talk.