It’s pretty hard to believe how much AI has captured the public’s imagination—especially for such a technical, math-driven field. For many people, AI conjures up images of autonomous robots, fully self-driving cars, and Artificial General Intelligence capable of managing our homes, if not controlling our lives.
However, reality isn’t quite that exciting. While we’ve made great strides in the adoption and creation of AI systems, many believe General AI applications like those mentioned above, are a long off
It might be a relief that sentient robots aren’t here just yet. But the fact is that AI is all around us, it just doesn’t look like Blade Runner or Ex Machina. In our personal lives, we use many forms of AI, from Siri to Google Home or Amazon Alexa. AI is becoming part of our professional lives, too, but it can be harder to see, especially in a people-centric field like HR.
So what is AI?
At its core, AI is the science of training systems to emulate human tasks. An AI system requires three ingredients–a problem, inputs or data, and a set of parameters or rules. Following these parameters, the AI processes the provided data and draws conclusions from it in an attempt to find solutions for the problem.
There are many different routes to creating parameters that mimic human intelligence. They can be fairly simple, like rules-based systems or predictive analytics, or deeply complex mathematical problems such as pattern recognition, neural networks, machine learning, and deep learning.
Much of the hype around AI is driven by the fact that, as a society, we create and store more data than ever before, and we now have more accurate mathematical models and cloud computing power to be able to analyze it all. The most powerful AI systems combine these techniques, delivering conclusions that are accurate enough that we can run them with little to no human intervention.
In short, today’s AI technology is getting better at emulating human decision making. That can sound scary, but it’s crucial to understand that AI is a system designed to imitate intelligence using human inputs, parameters and intervention–not sentience, or true cognition, in its own right. AI systems generally do not act on the conclusions they draw from data without humans’ involvement. It’s only because of rules and data provided by humans that AI can make decisions at all.
AI for Human Resources
Most business users are familiar with technologies like business intelligence and analytics. Analytics is a key part of the AI ecosystem since AI (and analytics) represents a way to automate the generation of better quality and more accurate insights about our people. Analytics and automation, while not futuristic, is attainable, valuable, and delivers pragmatic AI to enable any business problem.
AI is just one more way that technology supports business objectives. As mentioned above, automating processes and insight generation are the main ways that we can use AI to work more efficiently, productively, and accurately.
This technology exists at many levels of sophistication within Human Resources and people analytics. Many workers use AI in its most basic form, as analytics; for example, to assess the diversity of their talent pool. More complex AI might move towards using data to make predictions, for example around employee turnover rates and risk of exit.
To advance to a sophisticated level, AI solutions use machine learning or deep learning to accomplish these tasks. AI is all about using data to make decisions — and obviously, sound decision-making is essential to reaching business goals!
Practical applications of AI-driven people analytics
A common fear is that AI will entirely automate and take over jobs, rendering human workers obsolete. And of course, it is possible that some highly repetitive jobs could be replaced by AI. However, it’s more likely that AI will help people work better, faster, and smarter.
No matter the specifics of its use, AI automates processes, decision-making, and insight generation, which creates efficiency. That will lead to AI becoming integral to operations at many levels of the business world. However, most businesses are not building self-driving cars and voice assistants, so how can we apply AI to the HR analytic problem?
You don’t need to fundamentally reinvent your business to adopt AI nor should you focus on experimental or aspirational AI use cases. The sophistication of the AI an organization adopts can be additive, pragmatic, and deliver value now. Here are two examples.
Example 1: Sabre uses AI to retain their top talent
One of the key technologies behind AI is machine learning and its more advanced cousin, deep learning. So, how do we apply machine learning to your HR business problems? One pragmatic use of AI would be to predict attrition with high-performing employees—and take action to retain these valuable workers.
Sabre, the leading technology provider to the global travel industry, used Visier to accomplish exactly that. When they realized their rate of regrettable attrition had jumped from 5% to 9% in less than a year, they used Visier to find insights in their data to address the problem.
Sabre combined lagging indicators, like compensation ratios and team makeup, with Visier’s predictive analytics to create a scale for high-performing employees at risk of exit. With this approach, they were able to to predict which top talent had more than a 15% chance of moving on to another company, then create specific engagement plans for the employees at risk of leaving.
The results? Regrettable attrition dropped by 1.5%, with Sabre’s team working on getting it back down its original 5% level.
Some people would look at this example, and describe it as analytics. But here, complex machine learning algorithms culled hundreds of data points from tenure, compa ratio and employee engagement score — not only is this volume of data far beyond what a human could realistically manage and process, the algorithm also uses the data to increase the accuracy of its prediction. In this case, AI was needed to scale beyond human processing power and make more accurate predictions.
Example 2: AI helps Pitney Bowes generate value from their data
Another example is how we can use AI to manage and clean the data we already have about our people. Most organizations have five, if not more, unconnected systems that store and generate information about their people.
Too often, this data is unstructured, full of inaccuracies, and lacking consistency. The result is a fragmented data ecosystem, which makes it challenging to do appropriate strategy planning and people management.
This was the problem faced by Pitney Bowes, a technology company that provides solutions in the areas of ecommerce, shipping, mailing, and financial services. The company’s senior leaders understood the power of data, and increasingly wanted to use it to address questions related to all phases of the employee lifecycle, from hiring and onboarding through to performance and retention. The challenge was that their workforce data was located in many disparate systems and littered with inconsistencies.
To fix the issue, Pitney Bowes adopted Visier’s full suite of people analytics solutions. Now, they’re able to connect data from these formerly disparate sources, and HR and business leaders can access it themselves on a daily or weekly basis to answer their own questions.
This kind of data integration and visibility–a single source of data truth–helps organizations arrive at a clear, more complete picture of their people. Furthermore, this quick and easy as-needed access to their data can free up to 80% of HR analysts’ time to do value added analysis on their most important asset: people.
Build better career experiences
The more deeply we understand our data, the more effectively we can use it to improve people’s working lives. There are many hurdles to translating data into meaningful insights, and AI is a way to overcome them. It will help leaders and people analytics practitioners become more engaged with data and be a tool to help enable our goals like: managing talent, improving employee experience, increasing engagement, and supporting diversity and inclusion.
HR professionals and people analytics practitioners should look past the hype and futuristic connotations to see AI for what it is: a tool. This field is all about creating fulfilling, healthy and positive career experiences. Our work is inherently human-centric, and soft skills and interpersonal sensitivity are just as important to our success as hard data and maximum efficiency.
In HR, we work towards creating workplaces employees love, helping people achieve career satisfaction, and hiring diverse, talented, and inclusive workforces. As AI matures, it complements human intelligence and will become a powerful tool to help us get there.