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AI-Driven Search: Bringing AI into Enterprise Search

A McKinsey report has found that employees spend on average 1.8 hours every single day searching for and gathering information. This is too long to simply be trying to locate information and is potentially a major blockage in the knowledge economy.

None of this is a huge surprise, given that enterprise search is based on technology from decades ago, and is unsuitable for the needs and requirements of the modern enterprise. Why has enterprise search grown so unfit for purpose, and can cognitive insight bring the desired speed and accuracy to search?

Different content, changing search habits

Only a decade ago or so, most enterprise information and content came in just a few simple formats, easy to file, manage and access. In 2017, there are more file types and formats than ever before, and much of this is ‘unstructured data’, meaning it is not easily recognized and filed by CRM platform and other enterprise systems. Knowledge workers could sometimes be hunting for information that their enterprise does not know it has.

Furthermore, the way in which we search for information is changing rapidly. Instead of actively searching for something, we’re starting to let the computer, website or AI personality anticipate what we want and give it to us proactively.

As is often the way, consumer services are the first to dip their toes in new ways of getting things done, but this experimentation will eventually impact the enterprise after validation with consumers. Given the shift in search behavior, companies need to prepare for the impending demise of enterprise search.

A new approach to enterprise search

That’s why some of the biggest firms in the world have turned their attention to enterprise search in 2017. Bing for business is a special version of the Bing search engine, currently in private preview, while Google Cloud Search was introduced in February 2017, and is essentially a watered-down version of Google Search.

But search is slow to change

The principal elements of search were actually mostly developed in the 1980s, but took a long time to come to market. Searching through customer data, industry news, and analytics reports just isn’t an efficient use of company time. Intelligent recommendations are the new search results.

Just consider a recommendation example that most of us are familiar with: Amazon. You show Amazon what you’re interested in through your browsing and purchase history and then relevant recommendations are provided. A majority of the time these recommendations are a good fit, based on your previous behaviors and likely intent.

Intelligent insights

While every enterprise is not Amazon, every enterprise is drowning in data that can be used to provide intelligent insights and recommendations. Search provides isolated pieces of data, but it won’t provide the AI engine that delivers automatic context, insights, and next steps. While basic search points you to data at that point in time, you really need something that takes it to the next level by analyzing activity and habits over a period of time.

No matter what your business scenario is, you shouldn’t have to search for information. Instead, you should connect relevant data sources, and just like Amazon does, you can provide some indication of what you’re interested in and let it come to you. Smart companies shouldn’t search for information to make decisions on: they should just make decisions based on data that they already have in front of them at all times.

Context is king

The future of search is linked directly to the emergence of cognitive computing, which will provide the framework for a new era of cognitive search. This recognizes intent and interest and provides structure to the content, capturing more accurately what is contained within the text.

The four key elements of context detection are as follows:

Who – which user is looking for information? What have they looked for previously and what are they likely to be interested in finding in future? Who the individual is key as to what results are delivered to them.

What – the nature of the information is also highly important. Search has moved on from structured or even unstructured text within documents and web pages. Users may be looking for information in any number of different forms, from data within databases and in formats ranging from video and audio, to images and data collected from the internet-of-things (IOT).

When – the timing of the search itself, or the date/time that the information was created will both influence the relevancy and accuracy of results.

Where – the location of the user and also of the information – on-premise, in the cloud, within a database, contained in social media – make up the fourth element of the context that is such an integral part of cognitive search.

Many people still think of search as putting words in a box, but this is hugely limiting. In the future, it’s going to seem crazy to us that there was a time when we had to tell computers what we wanted them to do for each and every task. Safe to say, no one will be looking for enterprise search solutions once the productivity benefits of intelligent recommendations are experienced.

Source: AI-Driven Search: Bringing AI into Enterprise Search

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