Applying ML and AI to Improve Performance in the Workplace


When leveraged for positive impact on a distributed workforce, data-driven analytics offer keen and actionable insights that present major benefits to employees and leadership teams alike. Artificial Intelligence (AI) and Machine Learning (ML) can help businesses analyze large amounts of data to draw meaningful conclusions and make sense of complicated information. There is no comparison between what a human and a machine can do in the context of business intelligence.  The implications of technology as it relates to business intelligence are far-reaching. 

Leading indicators provide valuable insights

With insight into leading indicators of activity related to desired outcomes, it is possible to evaluate quantitative, qualitative and behavioral elements of employee performance, and influence results.  By definition, a leading indicator is an input that will impact and influence an outcome. Outcomes are measured after the fact so become the basis for a lagging indicator.  

Predictive modeling and other technologies enable a better understanding of daily inputs (leading indicators) and that knowledge can impact outcomes.  In other words, leading indicators offer a way to predict future outcomes and track progress in a more meaningful way, so there’s an opportunity to effect change. Better business intelligence, possible because of advancements in AI and ML, will enable an entirely new level of performance and management.  

For example, the way people spend their workday, how much time they spend on particular tasks, and their level of engagement with colleagues, could be indicators of employment longevity. New data from the Prodoscore Research Council identifies patterns and performance fluctuations indicative of employees likely to leave or stay at an organization.  By focusing on the rate of change of those key indicators, managers can detect patterns and address them immediately.  It could mean working to save an employee, reallocating tasks, or investing in cross-functional training.  Overall, with the help of BI, leaders are offered advanced notice so they can make better informed decisions and, in this example, potentially mitigate surprise resignations.

Transparency and the role of leadership

As long as leadership stays in alignment with people and processes, never forgetting the human element, the objectivity, consistency and equitability of ML and AI can help to appropriately address the complexity of the workforce. 

We are human, afterall. Data is only a complimentary measurement - as we embrace the role of data intelligence in the workplace, the interdependency of technology and humans needs to be top of mind. One cannot exist in a meaningful way without the other.  

Additionally, it is critically important that technology does not disrupt workflow. It cannot be  unnecessarily invasive or complicated for business leadership to interpret and act upon. For use coaching to higher levels of contribution, simplicity is key. 

HR and operational initiatives rooted in data present a more effective training process and opportunities for continuous performance evaluation.  Ongoing and proactive feedback often results in higher retention based on employee satisfaction and performance.

Business intelligence data also presents transparency between employees and managers, and organization-wide.  Creating transparency regarding direct employee benefits with business intelligence helps to eliminate psychological bias associated with performance appraisals, along with potential concerns about individual data privacy. But ML and AI algorithms must be applied in conjunction with trust and belief in the workforce. This honest approach will mitigate stress, create deeper engagement, and allow for greater alignment and higher productivity. Loyalty and commitment to success will then be recognized across the company.

In fact, Prodoscore data suggests that, when implemented at an organization where employees are made aware of the software, productivity improves. Ninety days after implementing Prodoscore, productivity increases 20%, on average.  Keeping employees informed of the application and involved in the process presents significant upside. So, again we’re reminded of the importance of transparency, and open and honest communication.  

On the whole, the most effective development of an employee should be based on proven methodologies that are constantly evolving and can be seamlessly collected from multiple data sources (cloud-based business applications) to enable leadership with true insights not simply gut instinct. It’s too often that key business decisions are made based on a “gut feeling.” 

Productivity intelligence to drive peak performance 

Prodoscore has introduced a new category of technology for distributed workforce management, a business intelligence solution that provides visibility into productivity and daily engagement.  Using ML, AI, as well as elements of Natural Language Processing, the software analyzes and scores organizational performance to identify opportunities for improvement. Complex algorithms deliver a simple score that can be interpreted in seconds. Leading indicators that drive success, or don’t, can be identified, and adjusted.  Deeper analysis demonstrates quantitative, qualitative and behavioral elements about the cadence of the workforce that are actionable and informative.