How business leaders are feeling about AI in 2024

I've spent the past year meeting with executives, entrepreneurs, and business leaders, and talking with them about AI: the challenges they’re facing, the opportunities they see, and their perspective on where we’re headed.

They run businesses across the U.S. and around the world. They come from a variety of backgrounds, and represent a range of industries and business sizes. And, as I found, they sometimes disagree on the future of AI.

Plenty of reports have been released this year on AI adoption (I link to a number of them at the end of this email). Here I offer a different vantage point:

These in-depth conversations have given me a window into how the business world is grappling with AI, from application and integration to the accelerating pace of change. Consider this a cross-section of early adopters and early majority business leaders.

Now that we’ve set the stage, here's what I’ve learned from those conversations:

We're just getting started

The overwhelming majority of businesses are just getting started with AI. The most common segments are:

1. I’m curious about AI, but I haven’t spent a lot of time experimenting with it yet.
2. I use AI all day long, but my team doesn’t. How can I get them on board?
3. I have a small team of internal champions who are testing and experimenting.

We're just exploring AI tools and assessing our workflows right now, one leader said. But we don’t have any standardized process yet, and want to take a more structured approach.

Based on this feedback, ideas like company-wide adoption, large-scale use cases, and ground-up business transformation all sound far off for most businesses.

Interestingly, there's also a stark “us and them” contrast, with most leaders I spoke with acknowledging they’re among the early adopters, and that the vast majority of individuals and businesses are even further behind the adoption curve. (Note: A U.S. Census report supports this.)

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Execs struggle to identify high-impact use cases

Leaders are grappling with how to integrate AI into business operations. They're eager to boost efficiency and productivity but often struggle to identify practical use cases.

There's a clear desire for practical examples in their specific context, and use cases that can demonstrate tangible ROI.

Overwhelm is another common theme — unsurprisingly, busy professionals lack the time to learn about, experiment with, and teach their teams about AI.

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For teams drowning in mundane tasks, AI seen as potential lifeline

Operational efficiencies and productivity top the list of desired outcomes. The focus isn't on augmenting skills, driving innovation, or optimizing performance. Instead, leaders are primarily looking to AI to free up their teams from repetitive, time-consuming tasks.

The goal is to create breathing room for more strategic work, focusing on quick wins and gradual scaling, rather than immediately pushing for new capabilities, enhanced creativity, or innovative solutions to business challenges.

The AI Intuition Gap

There's a perceived divide between those who "get" AI and those who don't. There seems to be something intangible about AI adoption — that it might come more naturally or intuitively to some than others.

It's not just about learning to use new technology; it's reimagining processes and developing a collaborative relationship with AI. It requires consistent practice and adaptability, which can be frustrating for those expecting straightforward, predictable results. This requires a blend of creativity, critical thinking, and technical understanding that managers are finding difficult to convey through traditional training methods.

Some leaders expressed frustration at the difficulty of bridging this gap within their own organizations, noting that hands-on experience seems crucial for true understanding.

(Note: This is why I’m a fan of actionable frameworks that teach new ways to think about and approach AI.)

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Saying the quiet thing out loud

Most leaders are viewing AI as a tool to enhance human capabilities rather than replace workers entirely. But getting everyone else on board can be tricky. There's a push to encourage adoption among less tech-savvy team members, while also addressing the elephant in the room: underlying anxiety about job security and potential displacement.

It’s going to be a balancing act as leaders aim to build excitement while assuaging fears.

AI budgets = 🤷

When it comes to investing in AI adoption, we're in uncharted territory. Many companies don't have established budgets for AI initiatives. Instead, internal champions who control their business units’ budgets are stepping up to fund pilot projects and early experimentation.

More widespread adoption and funding will come down to tangible value, making it crucial to demonstrate clear ROI.

Aside from proving real impact, some are also noticing pressure from boards and company leadership to demonstrate that they are doing “something" about AI.

🔥 Hot takes

And lastly, some “hot takes” or unique perspectives I heard on the time we find ourselves in…

• Small and medium-sized businesses might be the real AI winners, able to implement solutions faster than their enterprise counterparts that are struggling with bureaucracy and rigid oversight.
  
  
• A lot of people discount AI because they haven't spent enough time to understand its basic capabilities, said one executive. Often, they try a few basic prompts or unimaginative applications, receive unimpressive results, and dismiss the technology. They continued: Sure it's frustrating that investing hours into experimentation is necessary, but if anything, the tech is advancing at a discomfortingly fast pace.
  
  
• The launch of more advanced AI models (like GPT-5) will likely serve as a wake-up call, dramatically increasing demand for AI services as businesses realize their lack of preparedness.
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For more on the state of AI:

• Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey (U.S. Census Bureau, March 2024)
• The State of AI in Early 2024 (McKinsey, May 2024)
• Generative AI in Real-World Workplaces (Microsoft, July 2024)
• The State of Generative AI in the Enterprise, Q3 Report (Deloitte, Q3 2024)

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Question(s) to consider:

Reflect on your own AI comfort level: What uncertainties, reservations, or assumptions about AI are you weighing right now?

What's the potential downside of moving too fast with AI adoption? What's the potential cost of moving too slowly?