In the AI Race, Strategy Outpaces Speed

AI is a top priority for almost every business leader right now – and for good reason. It has the potential to make all of our lives easier, both personally and professionally. But turning that potential into real impact isn’t automatic.

According to a recent study from Qlik, while 94% of companies are increasing spending on products and services supporting data readiness for AI, only 21% have fully embedded AI into their operations. Of those that have embedded AI, MIT research found that 95% of generative AI pilots internally are failing.

The gap here reveals a common challenge: while the enthusiasm for AI is there, the execution falls short without real goals. Too many organizations race to adopt AI without first defining what success looks like, aligning teams on objectives, or preparing people for how their work will change.

AI becomes a shiny new initiative but not a strategic asset. Despite heavy spending, only 25% of organizations report seeing a clear ROI from their AI initiatives, and 75% are still waiting for the payoff, according to Boston Consulting Group.

In order to change this trajectory, companies need to shift their mindset. That means starting with clarity, not code. Success with AI doesn’t come from the tech alone, it comes from asking better questions and investing in what’s around the tech.

Starting with the Problem, Not the Tool

It’s easy to get swept up in the excitement of AI’s promise. Leaders want to show they’re embracing innovation, but too often, they start with the tool instead of the problem. The teams that see the biggest impact start with pain points. Where are people stuck? Why aren’t they being efficient?

Inside most companies, the challenge isn’t that people aren’t working hard enough; it’s that they’re buried in low-value work. Manual updates. Duplicated projects across disconnected tools. Endless meetings to stay aligned. Hunting down the latest version of a plan. In today’s environment of smaller teams and tighter resources, these inefficiencies aren’t just frustrating, they’re unsustainable.

According to researchers at MIT, knowledge workers spend nearly 50% of their time searching for information, switching between tools or duplicating work. These inefficiencies slow teams down, create ambiguity, and eat up more time that could be spent on strategic thinking.

This is where AI can shine: not by replacing people, but by reducing the overhead that keeps them from doing their best work. I see teams using AI not just to move faster but also to work smarter by removing repetitive tasks so they can focus on high-value collaboration. The most successful rollouts begin with a clear understanding of what’s broken and how AI can unblock people, not just processes.

Breaking Down Silos with Shared Intelligence

One of the biggest reasons AI efforts stall is that they’re treated as departmental experiments. The product team might be testing one tool, marketing another, and leadership something else entirely without any alignment on goals, tools, or principles. Even worse, frontline teams may not understand why AI is being introduced or how it supports their work. That’s a recipe for fragmented adoption and inconsistent results.

The companies I see with the most traction take a different approach. They treat AI as a horizontal capability, not a vertical initiative. For instance, McKinsey found that companies that see high returns on AI are nearly three times more likely to have enterprise-wide coordination in their AI strategy.

That means building shared understanding across teams, aligning stakeholders early, and embedding AI into workflows, not just layering it on top. Cross-functional collaboration is key. In fact, a recent McKinsey report found that organizations with strong returns on AI are significantly more likely to have central governance and enterprise-wide visibility into how AI is used.

It’s not just about the tools you pick; it’s about how you design systems that work together. This includes having a unified view of where AI is being deployed, clear data pipelines that power insights across functions, shared definitions of success and consistent training and onboarding for teams.

When companies invest in this kind of coordination, AI doesn’t just streamline tasks, it becomes a multiplier across the organization.

From Individual Efficiency to Systemic Change

Whenever there’s a big shift in how the world works, people are rightfully skeptical. Most people didn’t want to be the first on an airplane, but today we board flights without a second thought. When the internet became popular, there were plenty of voices who doubted it would last. History shows us that every major transformation—from air travel to the web—faced resistance before it became indispensable.

AI is at a similar inflection point because adoption isn’t like rolling out a software update. It changes how people work and, in many cases, how they think about their roles and how they provide value for their organizations. That’s why successful adoption is as much about change management as it is about functionality.

Resistance often stems not from skepticism about technology, but from lack of context. When AI arrives without explanation, people have valid questions: Will this replace my job? How do I know it’s accurate? What am I responsible for now? How can I trust it?

In fact, one third of US workers report feeling overwhelmed by the rise of AI in the future of the workplace. To overcome that uncertainty, leaders must invest in transparency and training, not once, but continually. That includes explaining the purpose of AI tools in the context of team goals, clarifying when to rely on AI versus human judgment, creating feedback loops to adjust how AI is used in practice and empowering managers to coach and support their teams through change.

The best companies treat managers not just as team leads, but as translators and trust builders, helping their people navigate new ways of working and feel confident doing so. Trust is the ultimate key, when people understand the “why” behind AI, they’re far more likely to experiment, adopt, and help shape its evolution.

We’re not just changing tools, we’re changing how we work

The companies that will thrive in the AI era are those that create holistic strategies designed to amplify human ingenuity, not replace it

That shift carries enormous potential, but also real responsibility. Done well, AI isn’t about automating everything. It’s about amplifying what people do best: judgment, creativity, and collaboration while stripping away the friction that gets in the way and increasing the individual’s impact within the organization.

In order to realize that potential, companies must move beyond enthusiasm to strategy. That means starting with the right problems, aligning teams across functions, and investing in the people side of transformation. Just as importantly, companies need to invest in environments that cultivate learning and experimentation alongside continuous improvement so they evolve as AI continues to evolve around us. When people feel empowered to test, adapt, and iterate, AI adoption becomes not a one-time rollout, but a living system that gets smarter over time.

The companies that do this won’t just keep pace with AI, they’ll build systems and cultures ready to thrive with it.