How the STEP framework can streamline AI usage in the workplace

The truth is, AI-enabled digital tools don’t operate like the technological tools of the past. Number one, they’re widely available and easy to use. Number two, they spread like wildfire and keep users from learning from each other or establishing patterns of expertise. Finally, they’re designed to constantly change. Instead of new technology being a fixed product that teams can learn to use with a catch-all manual, AI tools require their users to be creative and adaptable in their work. With this in mind, it’s understandable that both organizations and their employees can be unsure how to implement these tools or effectively manage these new processes. Here’s where the research comes in! Our department chair Paul Leonardi uses his studies on 10 companies with AI tools at their helm to show how his solution, the STEP framework, can help organizations better manage their employees while using AI.  

Leonardi’s STEP framework is broken into four stages: segmentation, transition, education, and performance. During segmentation, organizations focus on creating new processes that highlight when and where AI can be helpful, along with where it can’t be. The process is broken down into three sections: determining when AI can’t or shouldn’t be helpful, when AI can and should be used, and what tasks can be entirely automated. The next stage, transition, shows how organizations actually implement their process changes, usually by either deepening or upgrading their roles. Deepening roles involves allowing employees to devote more time to certain tasks than they were previously able, while upgrading roles means transferring employees over to more critical roles than the ones they previously performed. In the third stage, education, learning opportunities both inside and outside of the organization are implemented to create and maintain knowledge of generative AI tools. And finally, the performance stage shifts the parameters for employee reviews so that they take the rapid pace of AI change into account. 

Each stage of the STEP framework aims to make the implementation of AI as seamless as possible. This can be seen in the way each stage focuses on either its most efficient use of time and resources, or likelihood of increasing employee retention. The following section provides the most salient results from each stage, as studied throughout Leonardi’s three-year project: 

• Segmentation: One of the studied organizations showed that its staff could free up to 5 hours of work for other tasks after using segmentation for their AI tools. 
• Transition: Deepening work roles, or switching employees’ focus to more complex tasks, was the most common strategy for AI use in all ten companies sampled. 
• Education: At one of the studied organizations, employees who were given learning opportunities associated with generative AI tools were around 30% less likely than those not given such opportunities to leave the organization. 
• Performance: At one of the studied organizations, employees reported a 72% increase in their satisfaction with their evaluation system in comparison to the prior two years. 

Surprises: Real-world results of implementing AI at work

One of the most surprising findings in this study was that implementing AI doesn’t mean phasing employees out. Due to the way AI tools adapt and how quickly they do so, it might be natural to assume that AI would eventually replace or overshadow even the best employees, and research also confirms the prevalence of this assumption. 360 Learning listed displacement of jobs as the first of three common concerns about AI ethics, and this survey from the American Psychological Association states that 75% of their sample size worried that new forms of technology would take over some if not all of their jobs in the next ten years. However, in Leonardi’s research, only one of the ten companies he studied eliminated jobs in response to AI efficiency. The rest retained their employees, and even improved their satisfaction with their performance, while using the STEP framework. 

Each of the stages of this framework helped the studied organizations retain their employees and improve their satisfaction within their evolving roles. Using STEP met needs that all of these organizations had surrounding AI: empowering their employees to be more proactive with their responsibilities, showing leaders how to prioritize their employees’ skills by working in tandem with AI, and providing a more continuous way to manage ongoing technological changes. 

For managers, this article highlights four tips that they can bring into the workplace: 

• Trust their employees to experiment. 
• Create conditions for learning and incentivize helping
• Rethink workforce planning from focusing on tasks to skill set
• Reimagine their own roles 

While this article doesn’t specifically highlight tips for employees, they can also play an instrumental role in improving the effectiveness of the STEP framework and its results. Here are three ways that employees can use the above tips: 

• Be creative with the ways that they use both AI and traditional methods
• Track their AI usage and report this data to their higher ups
• Form collaborative groups within their organization

By being proactive about the ways in which AI is used in the workplace, employees can also allow the STEP framework to function at its best. 

What’s next? 

• Read the full paper
• Check out more research highlights from Technology Management