AI in hiring currently eclipses that of any other component of people management. Using predictive tools such as programmatic recruitment advertising, algorithms both seek and attract qualified candidates. Once a person applies, algorithms, leveraging both structured and unstructured data sort and screen them automatically using machine learning techniques. Many find that identifying people through the algorithms is faster, more precise, and fairer because there is less bias.
Of course, AI has many uses in motivating people as well. Recommendation engines that help employees choose career paths that lead to high performance, satisfaction, and retention. For example, if a person with an engineering degree wants to run the division someday, algorithms can scour the data looking for patterns and suggest the optimal mix of additional education, work experience, and soft skills they should obtain—and even the order in which to obtain them. Increasingly, organizations are also using algorithms to monitor employee morale. Social analytics and continuous “social listening” tap into what people are talking about on internal and external social media to conduct “sentiment analysis.” Some organizations now combine qualitative information from polls and surveys with quantitative data from sentiment analysis to analyze positive or negative data and gain insight into employee morale across the enterprise.
Though still rare, mannequins in clothing stores use AI to augment the performance of salespeople. Internet-enabled and equipped with sensors, the mannequins use facial recognition algorithms to identify shoppers and interpret their emotional states. The AI then checks their shopping history and recent social media activity, all in a second or two. Next, it passes this knowledge and its recommendations to a human sales person. This is just one way leading-edge AI is already enhancing human performance. More commonly, in-house sales people use tools that help them craft and send emails at precisely the right time. With intentional trigger words and phrases, the AI predicts what messaging will increase the likelihood of response from customers and prospects.
Predictive retention analysis is among the most mature, implemented, and simple solutions in the field of predictive workforce analytics. Algorithms now used by organizations, predict which employees are at risk of leaving the organization. In some cases, the algorithms identify individuals even before employees have consciously formed an intent to leave. In their everyday work and behaviors, employees give off many signals about their intentions, allowing organizations to build predictive statistical models that understand and forecast turnover. Using this information, managers (or the AI itself ) can intervene to stop talent from leaving, including the use of tailored incentives, rewards and recognition, succession management, etc.
Worldwide, organizations spend more than $350 billion on workplace training each year. Senior executives currently rank learning as their third most important corporate initiative. Despite learning’s importance, however, traditional classroom training, technology-enhanced classroom learning, and eLearning have, at best, returned unexciting results for years. The greatest opportunity for improvement in workplace learning most likely lies in creating highly engaging, hyper-personalized instruction. AI promises a realistic solution to the problem of onesize-fits-all education. AI-enabled intelligent tutoring systems are embedded with tools based on cognitive science that dramatically improve learning outcomes. Today’s state of the art in AI for learning offers personalization, adaptive learning, content curation, and automated, real-time assessments. Its algorithms examine various types of learning content (e.g., video, PowerPoint, paper-based, etc.), then break the content down and classify each word, phrase, and concept. As a learner goes through the material, every key stroke, every pause, every break, etc., is analyzed in real time by algorithms that assess and predict the learner’s absorption. Systems can create new learning content from scratch, adapting the course content to each learner’s level and needs. Further, AI parses content on internal social networks to find the same words, phrases, and concepts as in the learning content. It reports on the frequency of use and who is struggling versus who is helping others learn on internal collaboration platforms. Clients have dropped quizzes and assessments from their online courses entirely because the tool assesses learners more accurately and in real time, without interrupting the flow of their learning or work.
Before you start to work with Artificial Intelligence and Predictive Analytics, it’s important to make sure you understand how HR metrics can work for you. We can certainly help you with this at HRPredict. So, what are HR metrics?
Human Resource metrics are measurements that help you to track key areas in HR data. The most important areas are listed below. In this list of HR metrics, we included the key HR metrics examples associated with those areas.
An important metric for recruitment is the ‘time to hire’. This is the number of days between a position opening up and a candidate signing the job contract. It’s an excellent way to measure the efficiency of the recruitment process and provides insight into the difficulty of filling a certain job position. There’s also the time to fill metric. This metric takes the same starting point but takes the date the candidate starts working as the end point.
As you can see there are a lot of different examples of HR metrics. While some metrics are easier to implement than others, all of them provide insights into the workforce and HR. Combining these insights will prove vital for making substantiated decisions with proven impact.
Many HR decision-makers believe that the sheer volume of data sets the foundation of AI (Artificial Intelligence). Around 90% of the enterprises incorporate AI because it’s trendy. Many lack the required skillset and tools to use AI and mitigate complexities of the huge volume of data they have, unaware of the fact that AI can help them solve many of their HR and business challengess.
Applications have evolved, and things have changed remarkably since the days of plain old reporting. Today, your personal applications can learn and understand where you could go, what you could do, who you could meet and even what you might like to eat. If you notice, all of this is predictive rather than reactive. This gives businesses a newer weapon to target potential employees, improve processes and save costs. They can now understand associate behavior, and actively deliver personalized experiences rather than the traditional ‘one size fits all’ approaches. In addition, AI can foresee relevant events ahead of time and aid decision makers to prepare for outcomes.
In short, AI strengthens the candidate and employee experience, increases engagement, and builds strong targeted communication. It accelerates the decision-making process by helping in gaining competitive advantages. Instead of getting overwhelmed by the huge volume, variety and velocity of data, HR leaders can now use that data to realize the advantages of using artificial intelligence.
Ask these questions to yourself before gearing up for AI:
1) Collect and access appropriate data: Sounds basic? Well, it is one of the most important steps to implement artificial intelligence and predictive analytics. Simply begin with the place where your data lives.
2) Formulate a hypothesis: You’ve successfully created a data inventory. Now, what’s next?
3) Narrow things down: It’s time to focus on what matters to your business. Now, that you know what data is important and what will help you achieve your business goals, keep all your eyes on it—
4) Test your data: It’s high-time to create a prototype and test your accumulated datasets.
5) Make it happen: It’s time to make your data speak in real-life business scenarios.
6) Put your data to work: The final step is to make your data speak at real-time, real-life. Create value and readiness for AI in the long run. See if your data insights are now converting into valuable and actionable business insights.
Definitely, AI has limitless potential in transforming the way you do business. It will play a huge role in the growth and success of your business, but you may encounter some challenges while implementing AI. Check out some of those high-level pain points:
Nervous about applying artificial intelligence to your business as you think you’re not ready for this? Allow us to help you achieve this milestone. Take advantage of our 5 day data modernization assessment where we take you on a journey to explore how your data can yield marvelous results.