Such engagement doesn’t happen easily in most cases. For traditional companies, transforming into an AI-powered organization involves substantial work. They must, for example, fundamentally change their cultures into ones that embrace data, experimentation, and agile principles—all traits that the digital natives heavily and successfully using AI today (for example, Amazon and Google) are typically born with. They’ll also need to develop tailored analytics-education programs for all levels of employees, redesign processes, source new technical talent, and revamp their technology architecture (for instance, by embracing the cloud to ensure that they have the capabilities to support resource- and data-hungry AI systems).

However, we find that while these transformational steps are under way, there are some relatively simple ways executives can help their employees understand where they fit in and become active participants in charting an organization’s path toward AI. In this article, we share how leading organizations are getting the ball rolling.

1. Demystify artificial intelligence

With press articles often homing in on the spectacular (and sometimes unrealistic) uses and effects of AI, it’s understandable that many people have reservations about adoption of the technology in the workplace. One of the easiest steps for facilitating a practical understanding of AI is simply to demystify it by explaining how employees can use the technology to amplify their day-to-day efficiency and effectiveness (exhibit).

In general, we find that AI can offer five broad benefits to employees:

• Foresight. AI’s predictive capabilities allow employees to predict more accurately everything from potential equipment failures on the manufacturing floor to the next-best product a customer is likely to buy.
• Assistance. AI can save staff time by providing ready access to the data needed to answer questions and by automating time-consuming activities, such as claims processing, basic customer-service interaction, and inventory tracking.
• Expertise. The expertise that AI provides to employees is especially helpful when such expertise is scarce and difficult to source. For instance, a sales leader can use AI to surface and replicate the knowledge of the top sales reps, including how they choose which clients to visit, when to visit, and what to say, across a global sales team. In manufacturing, factory personnel can use AI to identify the root cause of machine failure and mitigate critical operational bottlenecks when specially trained teams of engineers are not available on site.
• Explanation. AI can help staff understand not just what customers prefer but why they prefer it. For instance, a regional director can use AI to understand how weather, route changes, and competitor price changes affect sales.
• Simulation. AI enables simulations that allow testing of nearly all potential scenarios before making a decision—a capability inherently beyond the purview of human cognition. For example, by using AI to simulate an event, a pricing analyst can understand the impact that price reductions might have on profitability in dozens of markets with varying degrees of competitiveness.

Companies can share these different opportunities with their employees as stand-alone activities or use the categories as a framework within the context of other activities designed to educate and engage staff. For example, one North American airline conducted an ideation session with its key stakeholders responsible for demand forecasting, fleet-profitability analysis, and route scheduling to discuss how they could use AI to solve critical business challenges. Session leaders shared with more than 15 participants, including directors and midlevel managers, the different uses of AI we have described. Participants then used the framework to create a list of how they might use AI within their workflows and what value it might provide. One participant noted that she spent a great deal of time managing long-term flight schedules by hand for reporting, a process that took her away from higher-value activities. Another employee highlighted how improving forecasting could help better identify which routes to add.

Once each participant created a list of challenges, they worked together to consolidate similar ideas and then pick five use cases for the analytics center of excellence (COE) to evaluate. In voting on which to prioritize, participants were asked to consider both the potential impact the use case could deliver and the feasibility (given the data and five broad categories of benefits the AI team provided). The analytics COE then used the participants’ list as a starting point, assessing potential use-case value, data requirements, feasibility, and technology needs for each and creating a plan of attack for two important use cases.

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Benjamin Cheatham is a senior partner in McKinsey’s Philadelphia office; Alex Cosmas is a partner in the New York office; Nehal Mehta is a consultant in the Washington, DC, office; and Dhruv Shah is a data scientist in the Montreal office.