The Future of Work: Intelligent Training That Adapts to Employee Performance

The rapidly changing nature of work today has ushered in a new, data-driven era in human resource (HR) management and development – fueling the need for intelligent training informed by the power of people analytics.

Google is meeting this need for its own workforce with its use of people analytics, which now informs almost all of the company’s human resources (HR) practices including development. For example, the company compares data from employee surveys with performance data to design the types of training experiences their employees want and need.

Google also used a combination of qualitative and quantitative data to determine if one of its levels of management was making a real difference, comparing managers’ performance ratings and feedback gathered from employee surveys with employee productivity metrics. The results showed that great managers at the level they were evaluating led more engaged and productive teams. Then the company reviewed the comments from the original surveys along with managers’ performance evaluations and double-blind interviews with employees to identify what characteristics make for great managers – valuable information that can be incorporated into training and development efforts.  

Intel also has been using people analytics in their organization and through the data analysis found out certain attributes that make employees leave or stay loyal to the company. During a six-month trial it reduced attrition by 20 percent.

When it comes to HR development, today’s business environment has not only created a need for intelligent training but training that can be consumed quickly at the moment it’s needed. Today’s workers want answers fast and have little patience for training that cannot be immediately applied. Just-in-time training (JITT) is one way to meet this need by providing easy access to up-to-date microlearning content (training delivered in short, easy to complete chunks). With JITT:

  • Employees can focus on training that is immediately relevant and as a result, can more quickly apply what they’ve learned.
  • Employees can quickly improve their performance, which can lead to greater financial rewards and recognition in the workplace and higher levels of engagement.

More and more companies are adopting JITT to meet the needs of their changing workforce and are reaping its benefits, including higher productivity and greater engagement. But, they shouldn’t stop there. We believe these and the other benefits of JITT can be multiplied exponentially when combined with insights gained through the use of people analytics.

Just-in-time Training Powered by AI Technologies

People analytics combined with machine learning (ML) give a powerful boost to just-in-time training. Machine learning is a type of artificial intelligence that uses algorithms and logic to find patterns in the data and then uses those patterns to make predictions. As the patterns change, the predictions change.

When analyzing training data provided by individual users – the types of content they are consuming, where they are struggling and where they are succeeding – together with data on their performance, patterns begin to emerge. We begin to see clearer connections between the training we provide and its real impact on performance. With deeper, more accurate insights, we can create more effective training experiences tailored to the individual. Further, the more data we provide the model, the more patterns emerge providing new insights we can use to create an intelligent system, one that learns as the employee learns, allowing us to keep up with his or her evolving development needs.  

Combining JITT with smart technologies that incorporate ML and predictive analytics improves overall performance by fostering continuous improvement opportunities unique to the individual.  

 

How to Develop Smart Just-in-time Training System

Choose the right platform

Developing a JITT system starts with an investment in smart technology – a platform that offers both analytics and the modeling capabilities ML requires. On top of that, the platform has to be flexible and offer employees an opportunity to chose the most convenient way to learn – whether it’s via a mobile or a desktop application.

Identify your Key Performance Indicators

For JITT to have a measurable impact on your company’s bottom line, it must be driven by your company’s KPIs, which help to identify the specific types of performance data you will need to mine from within your company. For example, if your KPI is based on revenue, you will need to collect sales data for individual employees. If your KPI relates to some elements of customer service, you would want to gather any available data that you use to measure employee success in that area.   

Collect the Data

The data you need will come from two sources:

  • Data from an online learning system capable of tracking the progress of all participants. (Note that while we highly recommend the adoption of JITT, if you do not yet offer on-demand training, you can still employ an analytics approach as long as the training you provide is offered online in a system that can track training activities at the individual level.)
  • Employee performance and business data, which is needed to provide the feedback the system needs for the iterative modeling that ML requires.   

Run the Analysis

With the right technology and programming, you can use data on productivity, continuous engagement, recruiting, retention, performance to reveal information that can help you take your company’s performance to the next level. Here are just a few examples:

  • Identifying ideal performance standards for a given type of work activity will allow you to continuously analyze performance data against those standards and use the results to keep adjusting the training to maximize effectiveness (i.e increase the number of employees reaching the desired performance level). This type of analysis can also help identify the ideal sequence of training activities based on performance standards, more accurately predict average onboarding time, and reveal the time it takes for new hires to reach the desired level of efficiency.
  • You can also use correlations. For example, by comparing the amount of time it takes for new hires to get up to speed with the specific courses they have taken, your system can predict the most effective courses that will result in the shortest route for successful onboarding.
  • Analyzing your employee performance data together with data on individual training activities can reveal the specific courses completed by the most successful employees and the sequence in which they were completed. This information can, in turn, be used to improve the overall training program by reordering the training content and channeling specific training to employees struggling in a given area.  

Endless Possibilities for Continuous Improvements

In addition to allowing us to learn from and duplicate successes, ML gives us the ability to analyze the data we already have and additional data we collect within our learning system in all sorts of ways to gain new insights that can help us create highly customized learning experiences. We can identify the behavior and training patterns of specific workers in different demographic groups, with different levels of experience, and in various business situations and use those results to deliver the right training to the right people at the right time – JITT multiplied by the power of analytics. 

Just imagine what’s possible if all of your employees are able to operate at their unique and full potential – potential that continues to grow with training content they can immediately apply to improve on the work that they do. That’s the Future of Work for companies that want to continue to grow and innovate.

Subscribe to the Rallyware blog below to stay on top of new developments in the Future of Work and learn about new ways to unleash the full potential of your people.  

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