The 2018 Index of Predictive Tools in HRTech: The Emergence of Intelligent Software is the product of great conversations around the world and in various forms of social media. (In particular, my facebook feed has been the scene of a lot of thoughtful commentary on AI in HRTech. Please friend me to join that conversation).

The goal of this post is to provide a nexus for reviews of, commentaries on, and debates about the report. It would be beyond surprising if everyone who reads the report agrees with it. Transparency is an essential element of the next generation of software. This is meant to be an experiment in the public dialog about an analyst’s report.

We are in the earliest of stages in the evolution of intelligent software. While there is little evidence of actual Artificial Intelligence, there are a good number of really interesting experiments that apply Machine Learning, Natural Language Processing, Neural Nets, and Big Data Techniques to Human Resources questions and data. The report explores 30 of those projects and draws top-level conclusions.
Imagine that these are the early days of electrification in the United States. There are electrical wires strung everywhere. Houses are beginning to have light bulbs. The power flickers and surges. The now ubiquitous transformer (which takes variable input and produces stable output) isn’t yet a part of the system. 

We are at the pre-transformer stage of Intelligent Systems. In non-HR settings, predictive tools and techniques are applied to systems that have predefined rules, outcomes, and end-states. Systems that learn have definite conclusions.

When you try to apply those techniques to dynamic human systems, you run into significant challenges. All organizations change their decision-making processes based on a stream of relatively unpredictable events. Each time the organization encounters changes such as capital availability, leadership structure, mergers/acquisitions, policy updates, competitive environment shifts, disruption, reorganizations, layoffs, product redesign, new products, economic environments, stock price volatility, and so on, the learning system has to recalibrate. These things are the essence of organizational life.

It’s clear that the quality of recommendations varies in response to these changes. It’s not clear how we know if or when it’s happening. That’s where the metaphorical need for a ‘transformer’ arises.

The report is designed to illuminate the risks and opportunities of a technology at this stage of development. 

I am anxious to discover what you thought of it.

Back to the report page.

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feature image HRExaminer Weekly Edition v8.41 October 20, 2017
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Maren Hogan returns from a spate of HR and Recruiting conferences with her predictions on the latest trends. From sourcing...