Ever since the first search engines entered the online world in the early ’90s, the progression of search-based tools has been lackluster at best. Don’t get me wrong — it’s not all bad. After all, we got a refined product along the way. However, the actual innovation was always shoved aside for the sake of a bit faster and slightly more accurate algorithm with every update.

The thing is, we need that innovation, and the sooner the better. The emergence of big data resulted in too much information, an amount that cannot be handled easily nor understood effectively on a daily basis. In order to extrapolate valuable insights both structured and unstructured data can provide, it’s vital to use machine learning and other forms of artificial intelligence.

The same can be said for workplace search tools: This is an emerging area where not much has been created to date. However, rapid changes in workplaces and market trends are pressuring organizations to stay on top of developments and manage their workforces efficiently. These changes are generating new ideas and expectations at a much faster rate than companies can keep up.

The modern workplace is all about collaboration: sharing knowledge and experiences with the generous help of technology. And just like the latent economic shifts, technology is on the fast track to change, forcing organizations to get down with the program (pun intended) and stay up to speed with the latest developments in workforce management. Specifically, when there’s a need to thoroughly search the workforce — from inductions to incidents and everything in between.

To date, the vast majority of search tools are designed to retrieve information. They typically interact with a specific program such as a web crawler that functions on the “find and discover” principle. It interacts with a database, be it a set of them or the entire internet, and retrieves results based on a simple keyword.

The on-demand basis of search tools does little to solve unpredictable and ambiguous variations of searches that are to be expected. That’s where machine learning comes in. It takes the whole query business a step further and adds a much-needed layer of understanding of the search query in question. Because the algorithms are programmed to absorb, learn and improve without human input and/or reprogramming, they essentially learn from their experience.

A fine search-related example is Google’s RankBrain algorithm, put into motion in 2015 and gradually updated. It allows the search engine company to analyze the context and gain a deeper understanding behind the query, thus providing more relevant and accurate search results.

Granted, there are far fewer idiosyncrasies and less ambiguity in workplace searches, and the subsequent element of popularity is virtually nonexistent. In that regard, machine learning adds something new to an already familiar setting — the element of prediction.

Because machine learning is constantly evolving, often it’s hard to gauge exactly what the benefits are without understanding the underlying processes. The ability to derive performance insights is used for something so simple and commonplace these days, like searching for information online.

Adding machine learning elements into the mix, the final result goes beyond a standard query result, whether it’s a query classification or common spelling suggestion or correction. Suddenly, there’s an analytical approach to it that offers a more intuitive experience. Searching one thing can highlight potential concerns that otherwise might have gone unnoticed. If you are a manager responsible for the performance of a select group of people, you just might notice there’s an ongoing drop in reaching monthly goals when setting the desired and required outcomes to meet the goals. What machine learning is really doing is leveraging algorithms to compute different characteristics of specific things based on previously available data.

There are many examples that articulate how machine learning will be able to instill predictive and proactive workplace management. One easily applicable example is through human resources. By searching for the top performers and collating their performance data, HR managers will be able to formulate a profile/template of an ideal worker — one they can use to match potential candidates and make more precise hiring decisions. Alternatively, the profile can be used in an opposite direction — to identify potential mismatches and avoid workplace disruption.

Such smart data analysis that learns and grows simultaneously with your organization warrants a more conscious approach to artificial intelligence. I’ve talked about this before. This facet of AI is only an extension of technology that many professionals already use in their daily routines. Despite unjustified and sometimes irrational fears, artificial intelligence — and machine learning, in particular — doesn’t have the power to replace us humans as the primary source of organizational growth. It can only help make the effort more coherent and efficient.

From a technical point of view, one of the most important challenges in the workplace is the lack of proper connectivity between systems. Because these systems operate as silos, companies cannot obtain a clear picture of their performance, nor pinpoint ways to improve. It’s really a subject of its own, but the same thing is happening with the business side of the equation. The way businesses operate is anything but simple, and a lot of it stems from how they use data. Machine learning is a small but significant step forward toward the ability to detect changes and be at the forefront.

Understanding the algorithmic factor is undeniably important, but it’s only one layer of the cake. The system in which algorithms are leveraged in is equally, if not more, important than the process itself. The sooner organizations can equip themselves with relevant technology to dissect the huge quantities of readily available information, the sooner they’ll be able to uncover apparent patterns and begin to make sense of them.

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