How AI can enhance employee autonomy and efficiency

While many fear that the advent of a digital workplace will replace workers with machines, smart technology leaders know that the use of artificial intelligence and machine learning should benefit employees, not replace them.

newly study Sloan and Boston Consulting Group (BCG) suggest that AI tools can nudge people to excel in their autonomy 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 machine learning helps companies save time and money by making workers’ lives easier, allowing them to focus on the most pressing tasks. Meanwhile, AI/ML technologies are among the few tools that can improve employee efficiency and independence at high rates.

“In addition to employee productivity and autonomy, AI/ML-focused initiatives enhance the effectiveness of employee decision-making in an organization.

Leaders can integrate AI tools with applications to help employees learn from past actions, anticipate future outcomes, and make better decisions. By doing this, leaders can provide their employees with AI-powered autonomy, giving them space to focus on higher-level tasks and less management oversight.

Transforming data entry employees into bot managers

Delayed AI can take routine, repetitive, and predictable tasks out of workers by automating things that machines should probably do better in the first place, says JB Thubder, vice president and principal analyst for the future of work at Forrester.

“There are whole groups of people who are hired as data entry people, but data entry is not a great human task. It’s tedious, but it’s also prone to a lot of mistakes,” he says. “The best case scenario is giving that worker new skills and tools, including automated process automation bots that can help with data entry.”

This worker is retrained to become a bot expert, and this bot knows how to handle exceptions for situations that come up, as well as perform QA and act as a subject matter expert to teach algorithms to be more effective.

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Autonomy contributes to the employee’s digital journey

It’s important to think about how freelancers can contribute to the company’s digital transformation efforts, says Anand Rao, global AI leader and U.S. innovation lead for the emerging technology group at PwC.

“While digital transformation journeys are usually thought of in a company-wide context, an individual’s digital journeys are just as important,” he explains. “By upskilling employees, leaders give their workers more digital freedom and autonomy. This ultimately leads to company-wide digital efficiency, an environment primed for innovation.”

Rao explains that artificial intelligence and machine learning technology help employees improve efficiency by providing them with better and more accurate data to make better decisions, ultimately leading to a deeper understanding of their work.

“Without AI/Machine Learning, no human being can gather and analyze enough data to do their job effectively,” he says. “But with AI/ML technologies, employees can get this data at breakneck speeds, allowing them more time to work on innovative and creative solutions for their companies.”

It points out how AI/ML technologies also increase the average competency and efficiency of the employees by getting knowledge and insights from the best workers in the organization in the AI/ML system.

“Getting ‘aware’ of subject matter expertise is one of the main benefits of an AI/ML system that enables junior employees to perform better,” says Rao.

Shaping an employee-centered AI/ML strategy

Responsibility for strategy development typically rests with AI, emerging technology leaders, and CIOs, says Rao. However, with any transformation effort, it is important to engage every C-suite leader when implementing new technology to ensure there is buy-in across the board to increase technology adoption.

“HR teams or talent teams focused on ‘the future of work’ initiatives are also key stakeholders in developing a strategy,” he adds. “If leaders do not embrace AI/ML capabilities, they risk becoming obsolete and left behind as the rest of the world continues their digital transformation journeys.”

This means that leaders must take charge and enact guidelines and ramifications when using AI/ML technology.

“Without governance, there is a potential for harm to emerge through disinformation based on inaccurate data. Regulations help provide stability and security to businesses, and build trust between their employees and consumers,” says Rao. “A strong and complex data strategy will ensure companies control their data, while continuing to encourage innovation within its organization.”

The psychology of independence

In general, autonomy, Thubder notes, has a psychological effect: How do I feel about the work I do? Can I do this without someone micromanaging me? Can I do a certain amount of work on a freelance basis?

“It allows employees to get into a state of flow where they are engaged in the work and don’t have to deal with so many interruptions,” he says. “A lot of times 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 the organization’s expert is on a particular topic and then calling them, or querying through a bot to find out how to complete a particular task.

“Increasingly this is showing up in a lot of different job categories, for example in field service or frontline worker,” Thrader explains. Field Service Technicians may be able to pull off [a] Some kind of planning or some kind of reference that tells them what steps to take.”

When trying to decide the next best course of action when they’re trying to fix some complex machine, AI and automation are increasingly becoming part of that picture. “Tools are getting more and more complex, and they can help you understand what to do next,” Thubder says. A lot of these instances of AI are just a few moments of assistance and automation being built into the technology we already use. And it just creates these subtle moments of improvement in your day.”

Building trust management and involving human resources and information technology

From Rao’s perspective, one of the critical elements in adopting AI/ML systems is “trust management”, which means that an AI/ML system needs to build trust with human users; They must be designed in a way that engenders interpretability, credibility, and fairness.

“Good AI/ML systems have a process whereby humans can trust machines and vice versa,” he says. “While change management focuses on human-human interactions, trust management focuses on human-AI interactions.”

Pthrider agrees that deploying AI to help employees is not just an IT problem or an HR issue.

“Business leaders are the ones who manage the people in all the 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 upgrade their skills by learning new tools.”

Enterprise techs can ensure that all of this is done in a safe, efficient and effective manner.

“It’s really business plus HR plus IT,” Thubder says. “All of these people are going to be important in this effort.”

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