Businesses lag behind employee use of AI, McKinsey study finds

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Everyone uses AI these days — 91% of employees to be exact. But if your employees use (and like) AI, does that automatically give your business a competitive edge?

Not quite. Individual use doesn’t promise strategic gain for your business. In fact, McKinsey & Company’s findings indicate that companies actually lag behind in capturing value in AI use, as employees adopt it much faster. 

Plus, Salesforce recently found that 55% of employees use unapproved AI tools at work. 

We’ll cover some key findings from the recent McKinsey report and explore ways businesses can start to leverage AI in the workplace. 

Employee AI use compared to organizational implementation

Most employees use AI in some capacity. McKinsey categorizes usage in three ways: 

• Non-users: 9%
• Light users: 70%
• Heavy users: 20%

The majority (light users) agree that AI will benefit them in multiple work aspects, specifically: 

• Communication: 81% agree
• Creativity: 75% agree
• Critical thinking and decision-making: 67% agree
• Ability to collaborate: 65% agree

Bottom line? Many employees feel positively about AI and use it regularly. 

But check out this contrast: 91% of employees use AI regularly, but only 13% of companies use AI for multiple uses inside the organization (these are early adopters). Most companies (60%) have only experimented with AI here and there, while 22% haven’t implemented AI at all. 

Early adopter companies align AI to business goals

If we look at the 13% of companies who use AI for various functions in the workplace, these companies share a couple of key strategies. In fact, 63% agree that their AI strategies align with their business strategies

We can see striking examples of this in company success stories with AI, like: 

• Walmart: Generative AI retail search (“shopping genie”), AI-summarized reviews, categorical suggestions for events over individual items. 
• BMW: Practices generative AI for over 600 use cases, including models for company operational data and decision making, along with customer service alerts for maintenance and sales chatbots. 
• Mastercard: Security use case, leveraging AI models to detect and prevent fraudulent activity; customer service use case for AI chatbots.  

On the other hand, only 17% of AI experimenting companies (companies that don’t use AI for multiple use cases), have that business-AI alignment. This gives us insights on how you can start to capture AI value company-wide. 

3 strategies to capture the full potential of AI value in your business

Not sure where to start in company AI strategy? Keep reading for these actionable tips. 

1. Establish clear AI guidelines and risk management strategies

AI brings countless benefits like productivity and budget savings, but not if you don’t protect your company data from its risks. 

Unfortunately, as much as 38% of companies don’t have formal policies for employee use of AI software that isn’t supplied by the employer. Meaning? There’s a lot of room for risky practices, like 49% of employees entering company data into AI tools without proper security protocols or vetting. 

2. Determine domains for AI transformation

Start looking at AI potential across business domains like: 

• Customer service: Gen AI agents that analyze customer trends and data for better service; AI-powered search functions
• Marketing: Personalization (think Netflix and Spotify recommendations)
• Software development: Self-writing code and models to shorten ideation to prototype timelines
• Performance management: AI models can track and analyze employee productivity and make personalized improvement plans accordingly

Domain-based AI adoption helps you capture multiple use cases for AI, which in turn helps reach tangible business goals. 

3. Strategize employee upskilling and development needs

While McKinsey found that most employees use AI, that doesn’t necessarily mean they know how to use it strategically. Plus, generative AI platforms like ChatGPT are better leveraged with skills like prompt engineering and data-driven decision-making. 

McKinsey suggests that businesses should allocate five times as much investment in people (recruitment, upskilling, career development) as they do in technology.

Read the full report from McKinsey here.