Workforce Optimization Tech is Driven by AI

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Workforce Optimization Tech is Driven by AI

Artificial intelligence is changing how we do our jobs. AI and machine learning can unlock contact centers’ potential through improved labor management and staff engagement. AI will bring about an exciting change for contact centers because of its capacity to quickly apply intelligence and learn the elements of any omnichannel environment.

Artificial intelligence is changing how we do our jobs. Tools and processes like an AI workforce optimization tool and machine learning can unlock contact centers’ potential through improved labor management and staff engagement. AI will bring about an exciting change for contact centers because of its capacity to quickly apply intelligence and learn the elements of any omnichannel environment.

Artificial intelligence (AI) has ingrained itself into daily life. Autonomous vehicles, highly customized Netflix and Amazon suggestions, and smart home gadgets are already becoming fixtures in our daily lives. Although AI may appear to be a relatively new technology, it has been used in contact centers for a long time.

A Little History of AI

An MIT professor originally theorized AI in 1956, and over the following 50 years, incremental improvements were made toward practical solutions. Machine learning is one of the most significant advancements in modern AI history. Through the automated refinement of extremely precise statistical models and forecasts, it has the potential to alter how contact center workforce management is conducted and produce corporate value.

Early AI models required all options to be stated in order to allow for automated decision-making. Today’s ML employs adaptable models that let a computer decide based on the information at hand, including possibilities that aren’t explicitly described.

AI More Recently

Other recent developments include learning models that sift through previous data used to produce volume and work time projections to uncover hidden trends. Additionally, AI technologies can decide which of more than 40 models will produce the best outcomes for a certain sort of labor. And these abilities have a significant impact. In fact, some experts predict that by 2035, AI will boost company labor productivity by up to 40%.

However, the objective of realizing AI and ML’s potential in the contact center is not too far off. Long before they became popular in recent years, these technologies were used in contact centers. Look at the effect they have already had:

1. Intelligence based on skill usage assessments

It can be difficult to schedule in any corporation. It might be challenging to decide how to allocate a multi-skilled employee’s time and abilities. However, predictive analysis can now be used to determine how to allocate a worker’s time among workstreams for maximum effectiveness and skill utilization.

An AI workforce optimization tool provides the ability to make skill usage assessments. These systems can determine a person’s ideal schedule that is shared across several workstreams.

2. Effective Workforce Management through Skill Use

Understanding the impact of multi-skilled personnel on the required lines, or the number of full-time equivalent workers required to satisfy customer service objectives is a typical difficulty in workforce management. The majority of current labor management solutions, however, are based on the Erlang statistical model. Unfortunately this model makes two assumptions that are no longer true in contemporary contact centers. Which are:

• Everyone has a similar set of talents
• Work tasks queue to a single skill profile

This results in overstated FTEs for some needed lines while understaffing others, which impairs a contact center’s capacity to respond to urgent customer needs.

Nevertheless, this issue can be resolved by AI-driven workforce management solutions. This is done by utilizing ML models that foresee the specific staffing needs of each necessary line. These algorithms give intelligence the ability to calculate precise FTE numbers to a given amount.

3. Optimization Using Closed-Loop Intelligence

Contact centers can use artificial learning in the form of unsupervised learning to build scheduling that continuously improves as the amount of accessible data changes or rises. This use of ML relies on a technique where the computer learns by analyzing a lot of data and then makes a preliminary assumption about the optimum course of action. These initial hypotheses are refined by comparing them to the anticipated result. Then, the results are put back into the algorithm to enhance performance automatically.

4. Intelligence and Schedule Fairness

For good reason, the importance of staff engagement has increased in recent years for contact center management. Employees who are engaged typically contribute roughly 20% more to revenue and are 44% more productive than those who are classified as satisfied. More than one-fourth of employees who feel their work/life balance is not supported say they will quit their jobs within two years, compared to only 17 percent of those who feel their work/life balance is supported. One of the most important ways to maintain employee engagement is through scheduling.

Employee engagement programs also make use of AI to give staff members a sense of ownership over the frequently overwhelming task of managing client demand. A fair workplace that replaces or improves conventional seniority-based assignment procedures can also be produced using machine learning. 

Conclusions

The potential and utility of AI for contact centers is an interesting development. Contact centers, back-office operations, and branch settings are already gaining from the technology’s capacity to quickly apply intelligence and learn any omnichannel context’s specific, decision-influencing characteristics. By implementing this technology, your staff is free to concentrate on tasks and ideas that call for a personal touch.

Workforce Optimization Tech is Driven by AI
Brian Wallace

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