Technology tools such as artificial intelligence (AI), machine learning (ML) and cloud-based analytics platforms, along with aggregated "big data" organized into informational dashboards, may have cracked the code for improving worker productivity.
Data about how employees work and behave can be analyzed, predicted and subsequently used to drive decisions to allocate resources, monitor performance and make the workplace better. These solutions have evolved to shape the way workers work.
Vadim Tabakman is the "technical evangelist" at Nintex, a Bellevue, Wash., firm providing end-to-end process management and workflow automation. He said AI and ML are used in many ways to improve performance by learning employee work patterns and habits.
"One of the more critical areas is in pattern matching," he said. "Since companies are collecting data all over the place, there needs to be a way to process that data. Building dashboards is not enough anymore. Finding trends and patterns is critical to understanding the data you have collected. AI and ML give companies the ability to detect patterns quickly so they can pivot fast to counteract obstacles or negative trends."
Imagine a company automates an approval process that is currently manual. After automation, the company notices that it isn't seeing the improvement it expected in approval times, a key performance indicator. "Since the company is collecting the data around when requests go out and when approvals come in, and there could be thousands of these happening. Manually detecting delays is hard," Tabakman said. "AI and ML can take all that data and notice an overall trend toward a slowdown in approvals or a slowdown within a team, office or even an individual, so that the business gets notified sooner and resolves the issue."
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Sid Bhambhani is the CEO of the software firm Summatti, based in Waterloo, Ontario, Canada. The company's technology is used to boost worker performance. One way to do that is to monitor the daily conversations that customer service and relationship agents in a contact center are having with customers through e-mail, phone or chat.
"In such environments, conversations have always been recorded for quality assurance purposes, so the stigma of it being a Big Brother kind of activity is long gone," he said. What's more, the platform is always transparent about how AI is being used to monitor the agents' work, and that has helped build confidence in the tool.
"We work with organizations that are using the insights they gain from the real-time analytics of the customer/agent conversations to provide in-time coaching tips on improving their performance, highlighting areas of strength and looking for coaching opportunities to grow an agent's skill set," Bhambhani said. "This goes to show that technology, when implemented with the right outcomes in mind, doesn't have to be intrusive and focuses on the positive aspects of growing and developing one's skill set."
Johnathan Hodge is the COO of Skedulo, a San Francisco-based technology platform provider for intelligent mobile-workforce management. He noted that one of his company's key objectives is to improve the experience, effectiveness and overall work performance of the mobile worker.
"These workers can have complex schedules, requiring them to be in many places in a single day delivering a variety of complex services to their customers," Hodge said. "[We] optimize their daily schedules using AI to help make their days as effective and efficient as possible. This optimization is not just about getting more work done; it's also about respecting the amount of travel and the time required. It's about ensuring, where possible, that a repeat visit is conducted by the same employee. It's about matching skills, qualifications and other attributes to the work so the employee is able to do their best work. Of course, there are many variables to consider when trying to make a mobile worker as effective, efficient and happy as possible during their workday."
This is an iterative process, he said. "We track the mobile worker throughout their day—their location and the work they are undertaking. We do this both because it's valuable for the employee and helps the employer create a great customer experience. We use this data to further optimize the scheduling process. The payoff is improved cost effectiveness of managing the schedules of a mobile workforce, elevated work performance from the mobile employees themselves and happier end customers."
Mitch Collier is the vice president of product management for StayWell in Portland, Ore. The company studies people's behavior patterns to improve well-being.
The firm emphasizes education as the first step toward a healthier life and works with electronic health record partners to incorporate patient education materials into the clinical workflow to make patient education available at the point of care. "By using predictive search and AI-engineered tools, we're able to provide recommended patient care based on input from providers, enabling physicians to provide trusted, easy-to-understand patient education for better treatment adherence," he said. "Physicians report that this integration also saves them time and arms them with additional tools to meet patient needs."
Jim Romeo is a technology writer in Chesapeake, Va., focused on business and technology topics.