Organizations will spend around $656 billion on future-of-work technologies this year, according to a June 2021 report from the IDC. Software will see the fastest spending growth over the 2020-2024 forecast period. Where? Enterprise applications, content and collaboration, analytics and artificial intelligence, human resources applications, security and software development and deployment, according to the IDC. “Emerging technologies like artificial intelligence, the Internet of Things, and augmented/virtual reality are changing how work is getting done across all industries and across the world,” Eileen Smith, program vice president of Customer Insights and Analysis for the IDC, said in the report. Companies’ workplace desires include automated decision support and virtual collaborative approaches, she added.
What are some of the practical use cases for AI in the workplace now? We explored that topic with some digital workplace pundits and companies who have deployed these kind of smart capabilities in the past year.
Enterprises focus far too much on simply doing things faster and cheaper, according to Alan Pelz-Sharpe, founder and principal analyst at Deep Analysis. And when AI is used as a blunt force tool for this kind of project it will almost always fall short, he added. “AI when used correctly is similar to RPA (Robotic Process Automation),” Pelz-Sharpe said. “With RPA you can automate repetitive tasks. With AI you can automate human tasks at scale.”
Pelz-Sharpe cited the example of personally identifiable information (PII) data in your workplace system being accessible to the wrong people. Today you can use AI to read, classify and protect at a speed and scale that was previously impossible.
Enabling Knowledge Management
Similarly with knowledge management, Pelz-Sharpe added, ideally workers want easy access to the right information at the right time to get their work done. Manually that is near impossible, but AI in systems like Guru and Microsoft Viva can automate much of that work, again at scale, according to Pelz-Sharpe.
“Scale rather than complexity is the key,” Pelz-Sharpe said. “Humans can do incredibly complex work, but they don’t scale. AI can. So with knowledge management, you have a decades-old concept that is only now able to come to fruition; as though in concept it worked, in practice it could not scale.”
Related Article: Choosing the Right AI for Your Business Goals
Digital Adoption Capabilities
WalkMe, a provider of digital adoption solutions, in July of 2021 announced enhanced capabilities available in the latest release of the WalkMe Digital Adoption Platform (DAP). It includes enterprise-grade capabilities such as integration with Microsoft Teams and enhanced auditing and monitoring, as well as community-generated templates and report builders.
It includes enhanced AI/machine learning capabilities in UI Intelligence. This is designed to automatically analyze user interactions with forms and business processes. The UI Intelligence engine learns and understands the optimal path to completion, where employees are struggling and what fields are not in use, according to WalkMe officials.
Dan Adika, CEO at WalkMe, said the company has developed machine learning capabilities it calls “Deep UI.” With Deep UI, WalkMe analyzes how humans interact with software and proactively recommends ways to improve the user experience with actions that can be taken from the WalkMe platform, according to Adika.
“For example, organizations using Salesforce Lightning can turn on WalkMe UI Intelligence and expose, in full detail, data-driven insights for all forms,” Adika said. “DAP professionals can now see which fields are redundant, understand where users waste a lot of time or make form fill errors, and chart the optimal path to form completion.”
Making Sense of HR Benefits
In August 2021, Deloitte released an AI Dossier report that highlights business use cases for AI. Report authors noted AI plays a large role in consumer relations but has not “affected HR activities to the same extent.”
However, AI can help HR in the following ways:
- Detect employee engagement trends across specific employee segments
- Gauge each group’s satisfaction with their chosen programs
- Optimize benefit offerings with predictive modeling
- Understand the financial impact of adding specific programs
- Benchmark against expected net advantage to employees most expected to enroll. “This can provide employees personalized and refined options when selecting programs that will best benefit them,” Deloitte report authors said.
- Enhance service delivery. It does so by providing intelligent benefits “mentoring” systems, which use data about employee claims history and coverage requirements. In turn, according to report authors, this provides a range of recommended programs.
- Present personalized recommendations in a web portal about employee enrollment
“These tailored solutions and recommendations can help improve employee fluency with benefits programs and packages,” report authors said, “helping ensure that employees are prioritizing the right investments and activities to achieve their goals.”
Related Article: 7 Ways Artificial Intelligence is Reinventing Human Resources
Pushing Back on Paper Pushing
Intelligent IT systems can increase analytics capabilities and simplify audit and historical document tracing efforts, according to Deloitte researchers. Enterprises are leveraging Natural Language Processing (NLP) and RPA to enable end-to-end back-office automation.
An example cited by Deloitte researchers includes government centers: AI-powered online self-service portals are designed to increase data intake capacity and reduce reliance on brick-and-mortar government centers.
Further, RPA systems with machine learning learn how to:
- Assess applications
- Understand potential actions given certain criteria
- Automate the review process
- Eliminate overhead costs
- Improve public service outcomes by displaying case status, e-notices and account balances (where applicable). “These advanced systems incorporate feedback loops to gauge service levels and continually improve performance at procedural pain points,” Deloitte researchers noted.
Zoom Meeting Transcriptions
Lots of digital workplaces are offering software in the post COVID-19 era that supports video conferencing and the hybrid-work nature of employees. Otter.ai is one example.
In May of 2021, Otter.ai, which provides AI-powered real-time transcription services, announced the launch of Otter Assistant. The feature automatically joins calendared Zoom meetings and records and shares meeting notes.
Otter Assistant includes the ability to:
- View meeting notes during the meeting
- Share and schedule upcoming recordings by connecting Otter with Google and/or Microsoft Outlook calendars
- Collaborate with all meeting attendees by highlighting, commenting and automatically sharing meeting notes
Related Article: Robert, HAL and What AI Fundamentally Helps Us With
AI in the Flow of Work
Business analytics provider Tableau, acquired by Salesforce, is “putting artificial intelligence (AI) in the flow of work” via integrations with fellow Salesforce company Slack, an enterprise collaboration provider.
Francois Ajenstat, chief product officer for Tableau, wrote in a September 2021 blog post that augmented analytics capabilities directly in Slack makes intelligent business data accessible.
The capabilities include:
- Asking questions using natural language to generate visual answers and then share with the team using “Ask Data in Slack.”
- Ask “why” questions and get interactive explanations of the value of any data point with “Explain Data in Slack.”
- Business users can run AI predictions on Salesforce reports to explore underlying patterns, identify insights and surface them in Slack with “Einstein Discovery in Slack.”
Reading Reactions During Videoconferences
So, you were thinking during that meeting…
Microsoft developers, using neural networks, created a Microsoft Teams bot in early 2021 to monitor audience reactions during videoconferences. According to developers, the bot, called AffectiveSpotlight, “leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals.” The developers used an exploratory survey (N=175) to assess the most relevant audience responses such as confusion, engagement and head-nods. AffectiveSpotlight analyzes facial responses and head gestures of audience members and “dynamically spotlights the most expressive ones.”