About AI

AI in Talent Acquisition and Onboarding

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.

AI in Employee Engagement and Performance Management

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.

AI in Employee Retention

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.

AI in Learning

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.

So, what are HR metrics exactly?

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.

Time to hire (time in days)

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.

• Cost per hire (total cost of hiring/the number of new hires)
  Like the time to hire, the ‘cost per hire’ metric shows how much it costs the company to hire new employees. This also serves as an indicator of the efficiency of the recruitment process.
• Early turnover (percentage of recruits leaving in the first year)
  This is arguably the most important metric to determine hiring success in a company.
  This early leaver metric indicates whether there is a mismatch between the person and the company or between the person and his/her position. Early turnover is also very expensive. It usually takes 6 to 12 months before employees have fully learned the ropes and reach their ‘Optimum Productivity Level’.
• Time since last promotion (avg time in months since last internal promotion)
  This rather straightforward metric is useful in explaining why your high potentials le
• Revenue per employee (revenue/total number of employees)
  This metric shows the efficiency of the organization as a whole. The ‘revenue per employee’ metric is an indicator of the quality of hired employees. Check this Business Insider article to view how the top 12 tech companies in the world score on this metric.
• Performance and potential (the 9-box grid)
  The 9-box grid appears when measuring and mapping both an individual’s performance and potential in three levels. This model shows which employees are underperformers, valued specialists, emerging potentials or top talents. This metrics is great for differentiating between, for example, wanted and unwanted turnover. There are other qualitative and quantitative ways to measure employee performance including Net Promoter Scores (NPS), management by objectives, number of errors, 360-degree feedback, forced ranking, etc.
• Billable hours per employee.
  This is the most concrete example of a performance measure, and it is especially relevant in professional service firms (e.g. law and consultancy firms). Relating this kind of performance to employee engagement or other input metrics makes for an interesting analysis. Benchmarking this metrics between different departments and managers/partners can also provide valuable insights.
• Engagement rating.
  An engaged workforce is a productive workforce. Engagement might be the most important ‘soft’ HR outcome. People who like their job and who are proud of their company are generally more engaged, even if the work environment is stressful and pressure is high. Engaged employees perform better and are more likely to perceive stress as an exciting challenge, not as a burden. Additionally, team engagement is an important metric for a team manager’s success.
• Cost of HR per employee (e.g. $ 600)
  This metric shows the cost efficiency of HR expressed in dollars.
• Ratio of HR professionals to employees (e.g. 1:60)
  Another measure that shows HR’s cost efficiency. An organization with fully developed analytical capabilities should be able to have a smaller number of HR professionals do more.
• Ratio of HR business partners per employee (e.g. 1:80)
  A similar metric to the previous one. Again, a set of highly developed analytics capabilities will enable HR to measure and predict the impact of HR policies. This will enable HR to be more efficient and reduce the number of business partners.
• Turnover (number of separations/total population in the organization)
  This metric shows how many workers leave the company in a given year. When combined with, for instance, a performance metric, the ‘turnover’ metric can track the difference in attrition in high and low performers. Preferably you would like to see low performers leave and high performers stay. This metric also provides HR business partners with a great amount of information about the departments and functions in which employees feel at home, and where in the organization they do not want to work. Additionally, attrition could be a key metric in measuring a manager’s success.
• Effectiveness of HR software.
  This is a more complex metric. Effectiveness of, for instance, learning and development software are measured in the number of active users, average time on the platform, session length, total time on platform per user per month, screen flow, and software retention. These metrics enable HR to determine what works for the employees and what does not.
• Absenteeism (absence percentage)
  Like turnover, absenteeism is also a strong indicator of dissatisfaction and a predictor of turnover. This metric can give information to prevent this kind of leave, as long-term absence can be very costly. Again, differences between individual managers and departments are very interesting indicators of (potential) problems and bottlenecks.

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.

HR Roadmap

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.

Why should you invest in AI?

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.

How to start with AI?

Ask these questions to yourself before gearing up for AI:

• Are you done being overwhelmed by the mountains of data and thinking of exploiting competitive advantages with it but don’t know how to do it?
• Do you want to understand you’re your talent pools better and increase your retention rates with innovative use of your business data?
• Are you looking up for improving your employee focus and drive greater connections to the overall purpose of the organization with your employees?
• Do you want to explore more and identify many other/new sources of value creation for your HR function?
• So, step zero is to find and identify the key business problems and know your business priorities. Continue reading if any of the above-mentioned goals sound like you and that if you have enough business data to accomplish (any of) these goals.

Guide to follow if you want to implement AI in your business:

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.

• Check the type of data that you’ve captured so far – structured or unstructured
• Evaluate if there’s any governance in place
• Identify how to find high quality data
• Categorize each data (by adding metadata, tags etc.)
• Start small. Don’t try to document each and everything. Just focus on collecting and accessing those data points that can make you solve your business priorities and issues.

2) Formulate a hypothesis: You’ve successfully created a data inventory. Now, what’s next? 

• Try to correlate your accumulated data with your business goals and challenges; Think how it will help to achieve your business objective
• Organize the given data to manageable chunks
• Map out your findings
• Stick to your priorities and try to work with what you have got
• Understand what data you’re allowed to stock up and use. Consider data ethics.

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—

• Catalog it for future purpose
• Don’t indulge yourself in analyzing everything at the initial stage itself; give it a time
• Concentrate on the datasets that matter to you
• Be 100% accurate to achieve success.

4) Test your data: It’s high-time to create a prototype and test your accumulated datasets.

• Ask as many questions you want to ask at this stage
• Program the algorithms to find answer to the queries. Use relevant data
• Look for the pattern and behavior
• If you think you’re not capable enough, partner with someone (like us….well, we’d prefer us) who can bring fresh insights and experience
• Demonstrate something tangible from your data-Its value and worth
• Make the prototype speak
• Document the usage and outcomes of the prototypes
• Get more people involved like a data scientist, etc.

5) Make it happen: It’s time to make your data speak in real-life business scenarios.

• Integrate the prototype into their existing business process
• Use your findings to enhance the existing process
• Operationalize and standardize the data insights to share with the entire organization.

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.

• Monitor the process and start from step One to sharpen your data
• Identify other cases where you can apply data technology
• Check if you’re all set to use various components of AI such as Bots, NLP, intelligent automation, predictive analytics
• Know where to use your algorithms for better results
• Take a human-centered approach to AI and add value to your organization.

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:

• Lack of technical know-how
• Noisy datasets
• Expensive human resources
• Weak computation speed

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.