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AI-Based Strategic Workforce Planning: Unlocking the Benefits
AI-Based Strategic Workforce Planning: Unlocking the Benefits
Kishor Venkatesh R

Content Developer

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AI-Based Strategic Workforce Planning: Unlocking the Benefits

30 Jul 2021

ML has become a significant development in AI’s history. With ML, machines use flexible models to make choices based on available data. ML creates value by improving highly accurate statistical models and predictions.

Recent advancements include learning models that find hidden patterns in historical data. The impact of AI is already enormous. By 2035, it will increase business productivity by up to 40%.

Advantages of AI/ML to Workforce Planning

Predicting where the industry is never going to be easy. Here is our take on how AI/ML will shape workforce management.

Suggests actions based on interactions

Machine Learning (ML) analyses how managers or stakeholders handle different tasks, identifies a strong pattern and then automates them. For example, if you have performed an action ten times already, it will automate the same action from now on.

AI could alert you to flight risks, give you weekly reports, and suggest actions after analyzing user search data from search engines. For example, AI in forecasting will look at data streams relevant to your business, such as public holidays, weather, or events in our vicinity, to create more accurate talent forecasts.

Optimizes scheduling

Unsupervised learning enables contact centers to implement scheduling that improves over time. With ML, machines learn by processing information and then makes an initial guess about the best decision, which is fine-tuned by comparing the results with the expected outcome.

The results are fed back into the machine to improve its performance. This is how modern workforce management tools solve the schedule optimization challenge when faced with many unknowns inherent in an omnichannel environment. The closed-loop intelligence can predict worker needs and then optimizes schedules.

Creates a merit-based workplace

Scheduling is critical to employee engagement. One in four employees who feel they have no support will leave within two years, while only 17% who think they received support will leave in that time. Engaged employees are 44% more productive and contribute 20% more revenue.

AI and ML give employees a degree of ownership in the overwhelming process of meeting customer demand. It creates a fair workplace that replaces the traditional seniority-based assignment process.

  • When adaptive intelligence combines with AI, uniquely identifiable metrics, attributes and preferences of each employee, it can auto-assign work schedules.
  • AI machines constantly monitor for changes in employee preferences and adjust the schedule accordingly. Staff can customize shift availability and let machines take into account the preferences when scheduling.
  • When policies integrate into machines, the algorithms balance the needs of employees and the business, manage assignments and meet customer demands.
  • ML can monitor assignment history, fairness credits, work rules, and business needs to keep work rotation fair.

Streamlines the hiring process

AI/ML-powered talent intelligent software can relieve HR from going through numerous resumes, thereby reducing blunders and recruitment-related ambiguities. The software can analyze all the resumes based on keywords, location, skills, and experience.

Tell the system the position to fill, and it will recommend the right candidate. Natural language processing (NLP) drives predictive language analysis, allowing faster shortlisting of candidates and speeding recruitment.

Increases efficiency in skill assessment

While scheduling is a challenge, determining the best use of employees with multiple skills can be difficult. However, predictive analytics will give you insights into dividing time across the workstreams for maximum efficiency and skill usage.

Another frequent challenge is to assess the number of full-time equivalent (FTE) workers needed to meet objectives. Today’s statistical model assumes that all individuals share a common skillset, and tasks queue to a single skill profile. This causes FTE overstatements, leaving other processes understaffed, affecting response time and ability.

AI-driven workforce planning solutions leverages ML models that predict the unique staff, including FTE requirements where required.

Estimates employee morale

Over time, AI and ML will identify performance patterns. Coupled with face recognition technology, it could recognize gender and measure employees’ emotional traits. With data gathered from various points, companies can use the insights to develop a bond with employees and empower them to discover their potential.

Eliminates biased appraisals

Staying unbiased during appraisals is a challenge to most managers. AI/ML algorithms execute and conduct employee assessments via regular, fair performance appraisals. It can also provide you an estimated career path for your employees and assist them in career advancement activities like training.

Makes prediction models better

AI and ML can potentially know your business better – whether it is predicting your future ROI, employee engagement level, project completion problems, and others. Generally, it would take months to get sight of this.

Gives RoI by reducing costs and increasing sales

Better forecasting and business optimization mean that you can have the right staff in the right place and time, helping you deliver a better service and experience to customers and maximize sales.

On the other hand, it can reduce overtime spending with better absence management. Data-driven software can slash costs and balance planned costs against results to create a profitable resource plan.

At its core, data will help you work smarter when it increases sale uplift, slashing unplanning absences, 100% employee engagement, etc., increasing the RoI.

The Future

AI is becoming more ubiquitous across enterprises and industries. With adaptive and deep learning capabilities, AI-powered workforce planning software will encompass more than just workflow optimization.

The integration of artificial emotional intelligence would give greater insight into human nature. Enterprises must realize the potential and escalate efforts in integrating AI into workforce planning and other business functions.

Draup’s talent intelligence platform analyzes employee information, personality traits, hard and soft skills to suggest candidates for a profile after referring to the hiring opportunity index and engagement guidelines.

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