Over the past two years, COVID-19 pandemic has reshaped the workforce in many ways. More people work from home or other remote locations, meaning when and how they do their jobs has changed dramatically. And temporary and "gig" workers have also become a larger part of the workforce, presenting challenges and opportunities for organizations.

With more people working at multiple organizations simultaneously, and opting to change jobs as part of the Great Resignation, companies have been scrambling for workers - and for new ways to manage a hybrid, part-time, and contracted workforce that wants more flexibility and a better work-life balance.

Legacy workforce management systems in the past performed simple scheduling and reporting tasks. But many of those tools weren't set up to handle a dynamic workforce, flexible schedules and even a skills gap around managing hybrid workforces. (The latter is spawning the use of remote training and supervision tools via augmented reality (AR) platforms.)

The problem is real, and "it's big," according to Ritu Jyoti, group vice president for Worldwide Artificial Intelligence (AI) and Automation Research at IDC.

Siloed software

"Organizations are looking for connected capabilities," Jyoti said. "They're looking for an end-to-end solution. They want something that works on a public cloud, in a multi-tenant environment, with the modern web as well as mobile support. Right now, people are stitching things together here and there."

That's been a problem at Amazon, for instance. It uses various types of software and algorithms to track employee time and attendance, oversee worker performance, and keep a record of employee disability leave. But those systems don't always work well together.

Last year, Amazon learned a hard lesson when employees began receiving termination notifications - even though they hadn't been formerly fired or quit. A manual patch the company deployed to enable communications between its time and attendance monitoring algorithm and its employee-leave system failed to integrate the two systems.

"We're in the process right now of fully implementing a patch that connects those two systems together," Kelly Nantel, an Amazon spokesperson, said recently.

Reports about Amazon's software algorithms or "bots" being used to hire and rate employees prompted accusations that those same automated systems were "firing millions of people with little or no human oversight." Not true, said Nantel. Overall, a large percentage of Amazon's 1.4 million workers are terminated for job abandonment, not performance issues, according to Nantel.

She argued that the company's workforce management technology supports and enhances the experience of job candidates and employees. It's not meant to replace managers, but to aid their decision-making with data and information, according to Nantel.

But those systems don't always work well together, she acknowledged. "We're not unique to some of those challenges, and when you're a company as big as Amazon and you're scaling and growing as fast as we are, we certainly have found some situations where our technology and our systems haven't kept pace," Nantel said.

"We're in the process right now of fully implementing a patch that connects those two systems together," she added. "We know there are challenges, but we also take our lumps where we think we need to. But, we also bristle as the assertion that... we manage solely by robot."

One issue is that some workforce management tools are embedded in ERP system software, others are standalone applications and cloud services. And at a large enterprise, there can be many different personnel management and training applications, many of which do not talk with each other.

AI/ML-enabled Workforce Management Software learns as it goes

The use of AI/ML-based management software - those algorithms and bots - is expected to jump from$150 billion to more than$500 billion in the next five years or so, according to IDC.

"Organizations are looking for tools that give them the whole lifecycle of workforce management," Jyoti said.

Digital workforce management software was already in use before the pandemic, mainly to help manage trucking fleets, retail workers, service workers, and other "task-oriented" jobs. For example, the gig economy enabled flexible hours for delivery services, which enabled same-day delivery for retail products and groceries. Delivery trucks were no longer pre-packed days in advance.

By 2023, 60% of the top 2,000 businesses worldwide will deploy AI- and machine learning-enabled platforms to support the employee life cycle from onboarding through retirement, according IDC. And by 2024, 80% of those companies will use AI/ML-enabled "digital managers" to aid in hiring, training, and firing workers. Even so, IDC expects only one in five will realize value without human engagement.

Not surprisingly, a cottage industry is growing up around the problem of disparate workforce management systems. Jyoti pointed to several start-ups and established software or service providers offering comprehensive, AI-powered workforce management platforms that can provide guidance and/or recommendations, forecasting, and employee training capabilities.

The list of vendors includes start-ups such as Legion, Augmentir, Five9, Eightfold.ai, and Reflexis as well as established companies such as Kronos, a workforce management and human capital management cloud services provider. Many of the start-ups are better equipped to implement APIs that connect to existing HR and ERP systems because their platforms are more malleable than those of established providers.

"From an innovation perspective, these are the companies that will be the movers and shakers in the industry. These are some of the companies with AI-powered workforce management," Jyoti said. "It's all about how they're looking at human capital management. It needs to be connected and preferably running on the cloud. Legion, for example, connects to Genesys Cloud platform. Genesys is known for its contact center solutions. Basically it's more dynamic, data driven, and AI-powered."

Augmentir's workforce managment platform tracks worker performance and can suggest changes to increase worker efficiency.

The start-ups have already won some big-name customers, including AthenaHealth, Cisco, Colgate-Palmolive Co., Dollar General Corp., Claires, Pizza Hut, and Six Flags Entertainment Corp.

Retail and manufacturing are two of the biggest verticals gravitating toward new cloud-connected, AI-driven workforce managment platforms, because their employees tend to have the most variable work schedules.

Jyoti explained the kinds of issues the platforms are designed to resolve: "Which person can I send to this location today? Which person is not showing up for work and how can I reschedule for that? How do I do automated scheduling? How do I automate time and attendance?"

Workforce management service provider Legion, for example, is focused on clients with hourly employees in manufacturing and retail environments.

One of the biggest uses for AI/ML-powered workforce management software is demand forecasting. Retail stores sell online and in brick-and-mortar stores, and offer same-day delivery of products. If a company has 100 stores and three retail channels, that's 300 buying channels from which customer demand can come.

"Before machine learning, it was hard to do accurate forecasting," said Sanish Mondkar, CEO and founder of Legion. "You want to make sure you're employing enough labor to meet demand - no more and no less. If you're running 100 retail stores, you have to have sales associates in each store at the right time. You may have different days of the week and hours with peak demand."

At the same time, a variety of state and local regulations govern how companies can schedule work. San Francisco, for example, has predictable pay laws that dictate how early a company must publish work schedules - and how many changes the company can make. Workforce management software has to take that into account.

AI and machine-learning algorithms also must account for feedback from employees about whether they like their schedules or how reliable they are in showing up at certain days and times. 

"It's common for employees to work more than one job these days," Mondkar said. "Our customers are trying to solve those three problems: efficient operations, regulatory compliance, and increasingly so, how make sure employees are happy and can be retained longer."

High employee churn rates lead to scheduling, training challenges

For contract workers, the churn rate, the hiring rate, and the worker-task alignment rate is very high, meaning task requirements can change dramatically day to day, requiring an agile software platform. 

Legion's workforce scheduling tool.

Companies also need training platforms that can keep up with a leaner workforce - and more new hires.

Over the past two years, employee turnover was one of the big issues faced by Hunter Industries, a manufacturer of landscape irrigation and lighting products with about 3,500 employees. Simply put, the company had fewer workers to do assembly-line work, oversee operations, and maintain machinery.

Hunter isn't alone; 82% of manufacturers cite workforce shortages as the current top business risk and they expect ongoing workforce recruiting and retention issues. As a result, virtually every manufacturing segment is interested in a digitally enabled workforce, according a 2021 study by Deloitte.

At Hunter, the company had been using a basic app on Apple iPads that revealed whether a worker had completed electronic tutorials or properly executed a multi-step manufacturing task. But that learning management system offered no insights into how long it took employees to complete training or tasks, whether they ran into problems along the way, or whether they had suggestions on how to improve a manufacturing process.

"Let's say a simple procedure requires a checklist. It was just paper-based and an employee could go in and complete the checklist - but there's no time tracking or task tracking analytics with that," said Yunior Murillo, senior operations training supervisor at Hunter Industries.

Even worse, front-line workers often needed help from maintenance workers when troubleshooting equipment breakdowns - and maintenance workers were in short supply, too.

The next challenge: Training

In 2020, Hunter Industries began testing AI-based workforce management software from Augmentir. The software enables the training of front-line workers, offers remote support tools for machine maintenance and troubleshooting, and allows employee feedback.

So far, Murillo's team has created hundreds of video and electronic training modules to get new employees up to speed - and help veteran workers follow step-by-step processes to repair equipment that breaks down or needs maintenance.

The Augmentir app allows Murillo's team to create a checklist and use instructor-led training that feeds completion data to the in-house learning management system.

"Currently, we're focused on employee accountability and providing employees training where it's needed on the production floor. Later..., we'd like to connect it to our automation software, and then connect [that] into our learning management systems," Murillo said.  "Something we're really looking forward to is this dashboard in terms of skills and certifications. That will tie more into the learning management system."

Since first piloting Augmentir's software, Hunter Industries has increased the number of seats to 50 and plans to double that number this year. "With Augmentir, I can see if the training our team is working on is being effective through all the analytics breaking it down," Murillo said. "That's what I'm looking forward to."

Field service management has also suffered from a lack of trained employees and companies are turning to AI-powered software, combined with augmented reality and computer vision systems, to enable remote help.

Symphony IndustrialAI sells a smartwatch-type wearable that includes a camera and allows employees to follow a digital checklist while managers supervise their performance remotely. The watch can be worn by quality control staff, front-line workers, or maintenance and repair staff, and organizations can upload step-by-step instructions on how to perform tasks.

The smartwatch is particularly useful for highly regulated industries, such as pharmaceutical and chemical production and healthcare, where the smartwatch platform provides an image and electronic audit trail of the work performed.

"Let's say I have to install a new compressor, and the guy that's been doing that for past 20 years just retired. So, now a new person comes in and they're the only one available to do it. How do you know exactly what to do?" said Dominic Gallello, CEO Symphony IndustrialAI. "When someone goes out to perform maintenance, I want them to do it right, and our product provides best practices and an audit trail to do that."

In December, Symphony IndustrialAI acquired Google Glass industrial partner Proceedix; the latter company's digital work instructions and inspection platform works like smartwatch technology, but it uses AR eyewear.