How is AI Transforming Workforce Management?


Artificial Intelligence is transforming the way we work. AI and machine learning have the power to unlock potential in contact centers through workforce management optimization and employee engagement. AI will be an exciting shift for contact centers—it’s ability to learn the factors of any omnichannel environment and rapidly apply intelligence is already changing the industry.

In 2019, Artificial Intelligence (AI) has become a routine part of our lives. Today, autonomous vehicles, hyper-personalized recommendations on Netflix and Amazon, and smart home devices like Alexa and Apple HomePod are already becoming commonplace in our day to day lives. Though AI may seem relatively new, the technology has actually been around for a long time as have its applications for contact centers. 

AI was first theorized in 1956 by an MIT professor, and advances towards useable solutions were slowly made over the next 50 years. One of the most important developments in AI’s recent history has been machine learning (ML). It stands to change how contact center workforce management operates and creates business value through the automated improvement of highly accurate statistical models and predictions. 

With early AI models, all possible choices had to be specified to enable computerized decision-making. Today, ML uses flexible models that enable a computer to make choices based on available data, including options not explicitly defined. 

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Other recent advancements include learning models that find hidden patterns in the historical data used to generate forecasts of volume and work time. AI tools can also determine which of a series of more than 40 models will lead to the strongest results for each type of work. And the impact of these capabilities is huge—Accenture predicts that AI will increase business labor productivity by up to 4Opens a new window 0 percent by 2035Opens a new window .

Unlocking the promise of AI and ML in the contact center isn’t a far-off goal, though. These technologies were implemented in the contact center long before they became buzzworthy in recent years. Take a look at the impact they’re already having: 

1. Skill Usage Assessment-Based Intelligence

In any organization, scheduling can be a challenge. Determining the best use of time and skills for a multi-skilled employee can be difficult. Figuring out how to best divide an employee’s time across workstreams for maximum efficiency and skill usage can now be done through predictive analysis. 

AI solutions for workforce management provides skill usage assessment tools. These solutions can identify an optimal schedule for individuals shared across various workstreams. 

2. Skill Use Efficiency in Workforce Management

In workforce management, a frequent challenge is understanding the impact of multi-skilled employees on the required lines – or the number of full-time equivalent (FTE) workers needed to meet customer service objectives. Most of today’s workforce management solutions rely on a statistical model called Erlang, which has two assumptions that are no longer valid in today’s modern contact centers. These are:

a) All individuals share a common skillset, and

b) Work tasks queue to a single skill profile

This causes FTE overstatements for certain required lines while leaving others understaffed, which affects a contact center’s ability to respond to real-time customer demands. 

AI-driven workforce management solutions can solve this problem by leveraging ML models that predict the unique staff requirements of each required line. These algorithms allow the intelligence to determine accurate FTE numbers to a value that is. 

3. Closed-Loop Intelligence Optimization 

Artificial learning in the form of unsupervised learning enables contact centers to implement scheduling that constantly improves as available data changes or increases. This application of ML leverages a method in which the machine learns by processing large amounts of information and then, makes an initial guess about the best decision. These initial guesses are fine-tuned through a process of comparison to the expected outcome, and the results are fed back into the machine to improve its performance automatically. 

This is how modern workforce management tools solve the schedule optimization challenge when faced with the many scheduling unknowns inherent in an omnichannel environment. They typically do not need to know the exact skill usage estimates or efficiencies before they can start optimizing schedules—closed-loop intelligence can predict worker needs by skill for them. 

4. Schedule Fairness Intelligence

In recent years, employee engagement has grown in importance for contact center leaders—and for a good reason. Typically, engaged employees contribute about 20 percent more revenue and are 44 percent more productiveOpens a new window than employees who are categorized as satisfied. And scheduling is one of the most critical ways to ensure employees are engaged—more than one in four employeesOpens a new window who feel they have no support for work/life balance say that they’ll leave within two years, while only 17 percent of those who feel their work/life balance is supported are planning to leave. 

AI is also used by employee engagement solutions to give employees a degree of ownership in the often overwhelming process of meeting customer demand. ML can also be used to create a fair workplace that replaces or enhances traditional seniority-based assignment processes. Some examples of this in action include:

  • Adaptive Assignment: When workforce management AI is combined with adaptive intelligence, work schedules can be assigned using the uniquely identifiable metrics, attributes and preferences of each employee.
  • Preference Persona Assignment: As robust personas are managed and updated by employees, an AI machine monitors constantly for changes in employee preferences and adjusts the schedule accordingly. Details such as, shift availability can be customized by staff, and the machine will automatically take into account their preferences when scheduling. 
  • Policy Assignment: Intelligent ML policies have been integrated into modern workforce management solutions that create schedules and manage assignments in a balanced way while meeting customer demands. There are algorithms designed in the technology to balance the needs of both the employees and the business.
  • Personalized Fairness Assignment: Often, employees want to volunteer to work specific days of the week, weekends or holidays, while their colleagues may want to be rotated through the assignments routinely. ML workforce management tools can monitor assignment history, fairness credits, volunteerism, work rules, and business need to manage the assignments fairly. 

AI’s utility and promise is an exciting shift for contact centers. The technology’s ability to learn the unique, decision-influencing factors of any omnichannel environment and rapidly apply intelligence is already benefiting contact centers, back-office operations, and branch environments. Adopting this technology frees your staff to focus on those activities and thought processes that require a human touch.

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