Bob Dylan warned us in 1964: "The times they are a-changin'." Sixty years later, AI is proving him right in ways he never could have imagined. While some companies are still debating whether to embrace it, their competitors are already three steps ahead.
Every week, we talk to companies eager to harness AI’s potential. They’ve seen the success stories, understand the competitive advantages, and know they need to move fast. Yet many find themselves stuck, unsure how to shift from enthusiasm to execution. Like the senators and congressmen Dylan sang about, they’re standing in the doorway, blocking the halls of progress.
The pattern is predictable across industries:
Meanwhile, competitors who’ve figured out how to navigate these challenges pull further ahead. Here’s what the team at Quantum Rise has learned about why AI initiatives fail and what it takes to make them succeed.
While every organization faces unique challenges, four obstacles appear again and again:
Too many companies jump straight to AI without first clearly defining the underlying business problem they’re trying to solve. Without a clear picture of what success looks like, even the most advanced tools will miss the mark. They may be over-engineered for simple issues or not powerful enough for complex ones.
Even with the right solution, companies often fail to answer employees' basic questions: What's changing? Why now? How will this improve my work? Without clear answers from leadership, confusion and resistance are common.
When AI initiatives span multiple departments but lack sponsorship from someone who oversees both, projects tend to fragment. Teams build competing solutions, waste resources, and create confusion about direction.
Companies need more than data scientists, they need business users who can work with AI on a daily basis. When employees struggle more with accessing the technology than benefiting from it, momentum dies.
Here's what surprises many leaders: technology itself is rarely the true blocker. When AI tools fail, it's usually because they were misconfigured or poorly designed, symptoms of not understanding the business problem in the first place.
The real challenges are human ones: mindset issues stem from poor communication, structural problems arise from lack of ownership, and readiness gaps reflect insufficient training and support. When you address these human factors, the technology usually falls into place.
The most successful AI implementations embrace "human in the loop" approaches. AI can take you from 0 to 60 by processing data and surfacing insights. But getting from 60 to 100 takes human judgment and context. When employees understand this partnership, resistance often gives way to enthusiasm.
Many teams believe they need to have everything in place before beginning with AI. They want all their data cleaned, every process documented, and complete buy-in from every stakeholder. And while well-intentioned, this perfectionism can quickly become paralysis.
The most successful companies take a different approach, borrowing from the principles popularized by Eric Ries in The Lean Startup:
The companies seeing real AI success aren't the ones waiting for perfect conditions. They're the ones learning, adjusting, and building momentum while others are still planning. By the time the "perfect conditions" crowd is ready to start, these early movers have already worked through the challenges and are building a solution at scale.
If there's one factor that predicts AI success above all others, it's this: CEO-level vision and executive alignment. Without this alignment, predictable problems emerge: projects stall or scatter into fragmented experiments and teams duplicate work and implement conflicting tools. Employees pick up on the lack of direction, which breeds skepticism and slows adoption.
Sometimes it requires a closed-door strategy session where leadership finds common ground. What problems are we trying to solve? How does AI fit into our broader strategy? What does success look like? Until these questions have clear, shared answers, progress will remain out of reach.
The path forward is clearer than most companies realize.
To build momentum with AI, start by clearly defining one business problem that matters and get executive alignment on why solving it matters now. Communicate consistently about what’s changing and why, then start with the simplest solution that creates value. Use what works, learn from what doesn’t, and scale only after you’ve proven success.
Most importantly, remember that AI works best as a partnership with human intelligence, not a replacement for it. When teams see AI as a tool that enhances their capabilities, adoption accelerates and results follow.
The times are indeed changing. The question is whether you’ll recognize the shift and adapt, or be left wondering what happened as the waters rise.
As Bob Dylan once said: you better start swimming or you’ll sink like a stone.
Ready to see what this could look like for your team? Let’s build a plan that actually works.
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Kevin Scott, Head of Client Delivery
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