What AI leaders get right that others miss

The gap between AI leaders and laggards isn’t technology. It’s conviction, structure, and scale.

Over the past few years, we’ve supported dozens of companies on their AI transformation journeys. Some have made real, measurable shifts in how they operate. Others, despite similar ambitions and resources, haven’t.

Why do some organizations lead in AI while others barely see the impact? 

When we take a closer look at the ones that succeed, we can uncover some patterns. Here’s what we believe they get right that others miss.

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1. Leaders believe AI will reshape their industry, not just make things more efficient

These organizations have a vision that goes beyond short-term gains. They genuinely believe that new technology will redefine how their industry works. That belief influences how they invest.

When AI is treated as a transformation, it changes how companies design roles, decision-making, and metrics. It becomes more than an upgrade; it becomes a shift in how the business creates value.

Take marketing, for example. The best leaders don’t use new tools just to make campaigns faster or cheaper. They use them to reinvent how people discover, choose, and connect with brands. Instead of asking how technology can improve creative output, they ask how it will change the entire customer journey. That mindset leads to bigger moves: rethinking how products are launched, how experiences are personalized, and how relationships with customers evolve in an increasingly digital world.

2. Leaders move AI ownership out of IT into the business

Although most companies have started rolling out digital tools, they’re still in the early stages of truly changing how they work. Existing structures often aren’t designed to support what these tools make possible. The most successful companies move ownership out of IT and into a cross-functional team that brings together people from across the business.

This shift matters because it allows departments like HR, marketing, finance, and operations to collaborate around shared goals. It helps spot opportunities across silos, align priorities from the top, and test new ideas from the bottom. It creates a setup where impact can be measured across the whole organization, not just within isolated functions. A cross-functional AI steering committee or center of excellence could be a great example of that.

3. Leaders prioritize domains, not individual use cases for AI

We often see organizations take one of two approaches. Some focus narrowly on a few large AI opportunities. Others open things up and allow broad experimentation of use-cases across the business. In my opinion, neither approach leads to sustained success.

AI leaders take a more strategic route. They define focus areas based on business domains where AI can make a real difference. Within those domains, they scale use cases that reinforce each other. This creates shared infrastructure, better learning, and more impact from each investment. 

Take customer experience, for instance. When teams across service, marketing, and retail all work toward improving the same outcome, their tools and insights naturally reinforce one another. The effect multiplies.

4. Leaders look at second-order effects and measure beyond efficiency

Every AI initiative should start with a clear business need. But that’s only the starting point. It’s just as important to think through the second-order effects of what happens after implementation.

One company we worked with introduced digital assistants in customer service. The first results looked impressive: faster response times, higher efficiency. But something unexpected happened. Sales during service interactions dropped. By optimizing purely for speed, the system ignored the moments where a conversation could deepen a customer relationship.

The company retrained the assistants to balance speed with commercial awareness, helping agents recognize when a customer might be open to an additional product or offer. Once that shift was made, satisfaction, loyalty, and sales improved together.

That’s why the best leaders don’t stop at short-term efficiency. They measure impact through growth, engagement, and lasting business outcomes.

5. Leaders invest in the foundations, not just the front end

Many companies treat new technology as something you can simply place on top of existing systems. The leaders know that real progress depends on fixing what’s underneath: the data, processes, and internal systems that make innovation work at scale.

They connect fragmented data sources so teams across markets and departments can work from the same trusted information. They improve the way data flows through their organization to make it accurate, traceable, and usable. They modernize their internal platforms, from planning to supply chain to content systems, so that information and insights can move freely between them.

These aren’t the most visible investments, but they decide whether technology remains a promising experiment or becomes part of how the business runs every day.

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