Imagine your HR teams could spot an issue with your workforce and predict the right thing to do, before the business itself feels the pain. HR analytics can tell you what is what by providing static reports, while predictive models can give an idea of what’s coming up. But they’re not guaranteed.
That’s where prescriptive analytics in HR comes in. Through the use of AI, workforce data and scenario planning, it allows HR teams to evaluate the potential outcomes of different actions and quickly determine the most effective solutions to talent shortage issues, retention problems and staffing needs.
This article will focus on its four primary advantages, its effect on making workforce decisions, and what an organization requires to leverage it.
The Evolution of HR Analytics and the Shift Beyond Predictions
From basic analysis and reporting to sophisticated systems, HR analytics is offering its leaders recommendations on what action should be taken next. Each of the previous levels ask a question and set the stage for the next level.
Descriptive analytics is what happened: a spike in attrition. Diagnostic analytics asks you the why questions: what do the exit interviews tell you, what do workload reports say, what do managers know? Predictive analytics deals with all the possibilities that may come up through using the model and the data points to determine when an employee is about to leave (an excellent analytics problem). Predictive analytics does not provide us with the answer to what action HR must take in view of the cost-effectiveness of each action (training, coaching managers, providing retention bonuses, etc).
| Predictive analytics | Prescriptive analytics |
| Estimates what may happen next | Recommends what action to take |
| Identifies employees at risk of leaving | Compares suitable retention interventions |
| Forecasts workforce demand | Evaluates hiring and reskilling options |
The real shift happens when HR moves from understanding workforce problems to choosing actions that support business goals.
Top 4 Benefits of Prescriptive Analytics in HR
The prescriptive analysis approach in HR will enable organizations to make good decisions on their workforce by linking information about the employees with the organization’s needs. The importance of prescriptive analysis in HR can be best appreciated when it is used by HR to address issues faced by organizations.
1. Optimizing Strategic Talent Planning and Scenario Modeling
Workforce planning becomes tough when the changing needs of the organization come quickly than the recruitment plans. Prescriptive analysis will assist the HR department in assessing their future recruiting needs and comparing various alternatives before any resource commitment. Should the firm decide to expand its business units, it may undertake an analysis of the advantages and disadvantages of external or internal recruitment and create a solution that is cost-efficient.
This matters because talent availability remains uncertain. The World Economic Forum reports that 50% of leaders expect talent availability to improve over the next 12 months, while 30% expect it to deteriorate. Skills matching and access to critical capabilities remain central challenges. By modeling different workforce scenarios, HR leaders can prepare for these changes instead of reacting after vacancies start affecting operations.
2. Enhancing Employee Retention and Engagement
The predictive analytics model can predict people who are likely to leave the company, but the score alone will not tell how that individual can be retained. The final step after identifying those people through the use of prescriptive analytics involves finding ways to retain them using actions such as coaching, redistribution of workload, and career development among others.
Let us imagine the case of a group of employees that is subject to very high turnover rates. Providing additional money to all employees in a group could be too costly, whereas a change in workload or a career path would solve the issue much better. A prescriptive analytics model will compare the costs of each solution to the costs of attrition of employees including recruitment and training.
It is important to remember that HR specialists need to understand the reason why employees leave the organization, not to take for granted that all employees have one single reason for doing this.
Also Read: What Is the Role of the CHRO in Digital Transformation? Key Responsibilities and Strategies
3. Streamlining Recruitment and Selection
There are many related decisions associated with recruitment. This includes means of advertisement, methods of compensation, applicant evaluation criteria, and interviews scheduling. Prescriptive analytics can help to compare the effectiveness of the means of advertisement, evaluate the limitations associated with recruiting, and develop means to improve recruiting.
For instance, if an organization is experiencing difficulties recruiting technical specialists, prescriptive analytics can help to compare the effectiveness of various means of advertising, referral programs, and skill-based recruiting activities. Such a tool could help in estimating the quality of applicants, cost of recruitment, and the duration of the recruitment process to develop an improved strategy.
However, the quality of these recommendations is highly dependent on how the success is measured by the system as well as the data that the system uses. According to the ILO, there are some risks associated with HR AI tools. This includes recruitment tools that try to estimate the growth mindset of an applicant based on their word frequency. Such metrics do not indicate the actual level of ability but only linguistic traits.
4. Smarter Learning and Development and Succession Planning
Budgets for training are not very effective when firms train all employees in a blanket fashion. Prescriptive analytics is capable of assessing the skills gap, career aspirations, and future requirements of the business to determine the right learning path for an individual. Prescriptive analytics will be able to help HR to decide whether to train or hire externally.
As AI shifts the skills required by the workforce, this is even more of a challenge. The ILO cites an increase in skills in cognitive, socio-emotional, digital and data science skills, as well as in AI literacy, adaptability, resilience and human agency. HR teams can adapt these findings to pinpoint capability gaps to be filled through development.
The same applies to succession planning. HR can create a skills matrix of employees relative to key roles within the organization, determine who might be best suited to certain roles, suggest development programs, and try out different succession routes. But the system shouldn’t automatically generate career paths for an employee history of performance, employee interests, and manager input still play a vital role.
How Prescriptive Data Transforms Workforce Decision-Making
Prescriptive analytics evaluates various possible actions against one strategic objective in the organization. Prescriptive analytics does not provide recommendations for the optimal course of action but considers different courses of action that can be undertaken, their impact on the organization, and then makes recommendations regarding which course of action is better based on the organizational objectives. It helps HR managers in identifying which course of action needs to be taken.
Take a department experiencing an employee attrition rate that is growing. The system might model differences between having managers coach employees and redistributing the workload by estimating the cost, potential drop in attrition risk and disruption to the business of either approach. HR leaders could then judge the potential benefits, and the investment needed, rather than going with their gut.
It is equally easy to analyze workforce activities using this perspective. A leader may look at the assumptions of a particular idea, evaluate the trade-offs involved, and judge whether implementing the idea serves organizational interests. The simplified model alone will not eliminate bias; however, the use of open standards and having a human being sit on the decision-making chair can guarantee consistency.
As per the study, only 25% of firms are taking an active approach towards managing the reconstitution of the workforce in response to the changes in the work being done. The companies which do, see an additional 4% rise in their income compared to the 2% for those that don’t.
A Roadmap for Implementing Prescriptive HR Analytics
Here are a few well-known best practices for applying HR analytics: protect the data, define your business goals and who is responsible for the results. Otherwise, the sophisticated algorithms will provide you with an undesired recommendation.
1st Step. Governance of data. For prescriptive analytics, accurate and consistent data about your workforce is needed. HR staff must collect all relevant data, update it where necessary, create standardized data definitions, and control data access. Inaccurate data can be used to provide wrong recommendations, while poor governance may reveal sensitive employee information.
2nd Step. Linking of analytics and business goal. The company should start with some problem such as growing cost of labor, lack of efficiency or skills gaps. Next, it should define what result it wants to change and then choose appropriate data and decision models.
3rd Step. Define decision ownership. Explain who will be responsible for reviewing, approving, and implementing each recommendation. For example, let us take a model which makes recommendations on a change in the compensation of an employee. It will be left up to the manager or the compensation committee to review the situation, budget constraints, and pay parity within the organization.
IBM reported that 71% of executives prefer the right to manage the outcomes of an AI model or have the authority to override them as opposed to 38% of employees. Well-defined oversight protocols help to close that divide and put the onus on HR leaders for the impact on workers’ careers.
The Future of HR Is Prescriptive
Prescriptive analytics in HR is an essential advancement in workforce intelligence since it helps organizations to go beyond reporting the past performance results and makes it possible to make recommendations for future activities to achieve organizational goals. However, the success of this approach is not dependent only on advanced modeling techniques. It is also determined by data accuracy and proper human analysis.
The first thing that the HR leaders must do is to measure the maturity level of analytics and then choose one workforce issue that needs to be addressed, and then to check the availability of data which will help them to take the right decision.
