From online tools such as Grammarly to robust options like MyAnalytics in Microsoft 365, workplace analytics became increasingly ubiquitous in 2019. The progression of machine learning has made it easier to use these analytics tools, and less expensive to put them in play even across an enterprise.
“AI is quickly shifting from a novelty that we all feel is too far off to be useful to a real tool that we need to take seriously,” said Jason David, CEO of Software Portal. That includes artificial intelligence focused on workplace productivity.
But are the insights from these analytics adding value on an enterprise level — and if not, when will they? It’s important to think about how these tools may or may not be offering managers valuable insights or freeing up resources for higher-level work; a tool’s ease of implementation is only the first hurdle, and it might not be one worth clearing if that tool’s business value is unclear.
The potential for AI applied to workplace productivity to make employees’ lives easier goes far beyond basic features like predictive text in email, said Matt Cox, the senior director of technical operations for ITSM at SolarWinds.
“Take an automated IT service management solution, for example,” Cox said. “With an AI-driven solution that integrates with email and other chat apps, employees can send a request using their preferred communication tool, while the ticket is sent directly to a single platform.” Machine learning (ML) can then find patterns in these requests and use that to determine solutions, which can be sent back to the employee via an automated message. Meanwhile, employers can track common problems and learn more about where their systems aren’t performing well.
“This integration of various communication channels fueled by AI creates a cohesive, streamlined service experience that allows workers to have their technical issues addressed in real time,” Cox said.
ML-powered automation also has productivity benefits in digital marketing, said Sean Clancy, director at SEO agency Edge Marketing.
“AI could stand for assistive intelligence as much as artificial intelligence,” Clancy said. “We use it to automate many processes, mainly in research and data gathering services.” These processes can be done overnight, giving it access to the network’s full processing capabilities and adding new productivity to hours when work typically wouldn’t be done.
Software Portal's David points out that the AI currently in use in the enterprise space has productivity upsides in several other areas as well, including recruitment, hiring, training and onboarding new employees.
But enterprise AI’s “bread and butter” is in its increasing ability to streamline daily processes, he said. “When I start my workday, the first couple of hours are spent reading and responding to emails, listening to phone messages and touching base with my team,” he said. “With AI, many of these tasks would be taken care of. After all, many of these emails are simple forms adhering to the processes we've set in place.”
However, AI’s applications for productivity are still changing and developing quickly, which has its downsides as well. One of those is the sheer magnitude of options that are out there, which makes it difficult to determine which are truly effective and which are the best fit for any particular enterprise scenario.
“AI really does help a lot with both workspace productivity and meeting business goals, but you just have to find the right tool that fits your business,” said Joy Corkery, content marketing lead at Latana. However, finding that tool can reshape workplace productivity, Corkery said.
Some workplace analytics tools may have so narrow a focus that they can’t have much impact on productivity.
“I think AI features like autocomplete or Grammarly have limited impact,” said Tom Taulli, author of Artificial Intelligence Basics: A Non-Technical Introduction, who pointed to the narrow use case for that tool and others. A full-suite analytics tool like MyAnalytics is likely to be a lot more effective because it involves frequently used software for enterprise and robust datasets.
But the potential is clearly there. Taulli pointed to robotic process automation (RPA) — which essentially automates tedious and repetitive tasks — as the biggest factor for productivity in this space, and one to watch going forward. It’s an area where Microsoft has made a big play with its Power Automate software.
“There should be no surprise that RPA companies like UiPath, Blue Prism and Automation Anywhere are growing incredibly fast,” he said. “And they are starting to deploy AI, such as computer vision, to scan and interpret business documents and contracts.”