While many people fear the rise of a digital workplace will replace workers with machines, smart technology leaders know that utilizing artificial intelligence and machine learning should benefit employees, not replace them.
A recent study from MIT Sloan and Boston Consulting Group (BCG) suggests AI tools can drive individuals to excel in their independence by helping them learn from past actions.
These tools can also help individuals deepen relationships with coworkers, customers, business partners and other stakeholders.
Automation powered by AI and ML helps companies save time and money by making workers’ lives easier, allowing them to focus on more pressing tasks. Meanwhile, AI/ML technologies are among the few tools that can improve employee competence and autonomy at high rates.
“In addition to employee productivity and autonomy, AI/ML focused initiatives also enhance the effectiveness of an organization’s employee decision making.
Leaders can integrate AI tools with applications to help employees learn from past actions, project future outcomes, and make better decisions. By doing so, leaders can provide their employees with AI-enabled autonomy, giving them room to focus on higher-level tasks and less managerial oversight.
Turning Data Entry Employees into Bot Managers
J. P. Gownder, vice president and principal analyst for the future of work at Forrester, says delaying AI can take rote, repetitive, predictable tasks off the plates of workers by automating things that probably should be done better by machines in the first place.
“There are entire groups of people who have been employed as data entry people, but data entry is not a great human task. It’s boring, but it’s also prone to lots of errors,” he says. “The better scenario there is to give that worker new skills and new tools, including robotic process automation bots that can help to do the data entry.”
That worker is retrained to become a bot master, and that bot master knows how to handle exceptions to situations that come up, as well as performing quality assurance and acting as a subject matter expert to teach the algorithms to be more effective.
“Adding technology to human workers gives them better tools to be more productive, to do less boring, repetitive stuff, and to use their creativity and their judgment, and indeed their expertise about the process, to actually have a more fulfilling job,” Gownder says.
Autonomation Contributes to Employee’s Digital Journey
Anand Rao, global AI lead and US innovation lead for PwC’s emerging technology group, says it is important to think of how autonomous employees can contribute to a company’s digital transformation efforts.
“While digital transformation journeys are typically thought of in a company-wide context, individual’s digital journeys are just as important,” he explains. “By upskilling employees, leaders are giving their workers more digital freedom and autonomy. This ultimately leads to company-wide digital proficiency, an environment ripe for innovation.”
Rao explains that AI and machine learning technology help employees improve competence by equipping them with better, more accurate data to make better decisions, ultimately deepening their understanding of their work.
“Without AI/ML, no human being could gather and analyze enough data to effectively do their job,” he says. “But with AI/ML technologies, employees can get this data at lightning-fast speeds, leaving them more time to work on innovative and creative solutions for their companies.”
He points out how AI/ML technologies also raise the average proficiency and competency of employees by capturing the knowledge and insights from an organization’s best performers in an AI/ML system.
“The ‘cognification’ of subject matter expertise is one of the main benefits of an AI/ML system that enable novice employees to perform better,” Rao says.
Forming an Employee-Focused AI/ML Strategy
Rao says responsibility for developing a strategy typically sits with AI and emerging technology leaders and CIOs. However, with any transformation effort, it’s important to engage each leader of the C-suite when implementing new technology to ensure there is have buy-in across the board to increase tech adoption.
“HR teams or talent teams focused on ‘future of work’ initiatives are also key stakeholders in developing a strategy,” he adds. “If leaders do not adopt AI/ML capabilities, they run the risk of becoming outdated and being left behind as the rest of the world continues their digital transformation journeys.”
This means leaders must assume authority and enact guidelines and ramifications when using AI/ML technology.
“Without governance, there is potential for harm to manifest through disinformation based on inaccurate data. Regulation helps provide stability and security to the business, and builds trust among their employees and consumers,” Rao says. “A robust and sophisticated data strategy will ensure businesses have control over their data, while still encouraging innovation within their organization.”
The Psychology of Autonomy
Gownder notes that autonomy, generally speaking, has a psychological impact: How do I feel about the work I’m doing? Can I do it without someone micromanaging me? Can I do a certain amount of work on an independent basis?
“It allows employees to get into a flow state where they’re involved in the work and not having to deal with lots of interruptions,” he says. “Oftentimes with AI, you’re talking about things like chatbots that might help with self-service.”
This could include an employee typing in a question about who at the organization is the expert on a certain topic and then connecting to them, or querying through the bot to find out how to complete a certain task
“This comes up increasingly in a lot of different job categories, for example in field service or a frontline worker,” Gownder explains. “Field service technicians might be able to pull up [a] kind of schematic or some sort of reference that tells them the steps that they should take.”
When trying to determine the next best action when they’re trying to fix some complex machinery, AI and automation are increasingly part of that picture. “The tools are getting more sophisticated, and they can help you to understand what you should do next,” Gownder says. A lot of these AI instances are just little moments of assistance and automation that get woven into the technology we already use. And it just creates these micro moments of improvement in your day.”
Building Trust Management, Involving HR, and IT
From Rao’s perspective, a critical element for the adoption of AI/ML systems is “trust management”, which means the AI/ML system needs to build trust with the human users; they need to be designed in a manner that engenders explainability, believability, and fairness.
“Good AI/ML systems have a process for humans to trust the machines and vice versa,” he says. “While change management focuses on human-human interactions, trust management focuses on human-AI interactions.”
Gownder agrees deploying AI to aid employees is not merely an IT issue or an HR issue.
“It is business leaders who are managing people in all roles that are affected,” he says. “HR manages learning and development, but also things like trying to understand what skills the organization has and how they can help employees level up their skills by learning new tools.”
The organization’s technologists can make sure all this is done in a secure, safe, and effective, performative way.
“It’s really business plus HR plus IT,” Gownder says. “All those folks are going to be important to the effort.”