Business Success With AI Adoption Depends on Organizational Strategy, Not Just Technology

The real AI advantage begins when entrepreneurs stop limiting their own potential

Kirk Drake, founder of CU 2.0, says the main obstacle to AI adoption is not cost or complexity but the assumptions entrepreneurs bring to the technology. Most organizations already have the knowledge AI needs but have never organized it. Businesses that document their values, workflows, and brand voice before introducing AI get consistent results. Those that wait risk falling years behind competitors who started with small, incremental experiments. Artificial intelligence has rapidly become one of the defining business conversations of the decade. Yet despite growing investment and widespread discussion, many organizations continue to approach AI with hesitation. Research found that while organizations remain optimistic about AI's long-term value, turning experimentation into measurable business outcomes often depends less on the technology itself than on organizational readiness and leadership execution. Kirk Drake, founder of CU 2.0, believes many business leaders are focusing on the wrong challenge. Through CU 2.0, he works with organizations on AI strategy, digital transformation, and leadership development, helping executives understand how emerging technologies can be applied in practical business settings. From his perspective, the greatest obstacle to successful AI adoption is rarely technical capability. Instead, it stems from assumptions that AI is too expensive, too complicated, or too difficult for smaller organizations to implement. "The barriers entrepreneurs see are often barriers they've created themselves," Drake says. "Most businesses already have the knowledge AI needs. They simply haven't organized it in a way that allows the technology to understand it." According to Drake, one of the biggest misconceptions is that AI removes the human element from customer relationships. He argues the opposite can be true. Businesses have always wanted to deliver more personalized experiences, but most lacked the time and resources to do so consistently. AI, he explains, creates opportunities for personalization that would have been impractical even a few years ago. He points to organizations that build structured brand guidance, departmental communication styles, and individual workflows before introducing AI into daily operations. Those foundations, he says, allow technology to reinforce a company's personality rather than replace it. Drake believes this is where many entrepreneurs unintentionally limit themselves. They expect AI to define their business identity instead of recognizing that the technology reflects the quality of the information it receives. Businesses that have never clearly documented their values, workflows, or brand voice often mistake inconsistent AI output for technological weakness when, in reality, those inconsistencies already existed within the organization. "The technology is simply exposing gaps that were already there," Drake explains. "If you don't understand your business well enough to explain it, how can you expect AI to replicate it?" He also encourages leaders to rethink what successful AI adoption actually looks like. Many assume implementation requires major budgets, dedicated technical teams, or months of preparation. Drake argues that meaningful progress often begins with something much simpler: developing better prompts, documenting existing knowledge, and allowing AI to analyze work that already exists. He notes that entrepreneurs frequently underestimate how much information their organizations have already created. Existing emails, websites, presentations, procedures, and customer communications often contain enough context for AI to begin identifying patterns, generating documentation, and supporting daily work. Drake suggests that AI adoption should be viewed as a learning journey rather than a technology project. Teams that develop familiarity through everyday experimentation gradually build confidence before tackling more sophisticated implementations. In his experience, those incremental improvements compound over time, creating lasting operational advantages. That philosophy is rooted in a personal lesson. Reflecting on the early internet era, Drake recalls dismissing the significance of websites before eventually recognizing how transformative they would become for business. Looking back, he considers that hesitation one of the most valuable lessons of his career. "I promised myself I would never make that mistake again," he says. "Even if it means investing an extra hour every week to understand where technology is going, that small investment can shape the next twenty years of your business." The pace of AI development makes continuous learning increasingly important. Every new capability builds upon previous understanding, meaning businesses that begin developing practical experience today are often better positioned to adapt tomorrow. Organizations that delay entirely may eventually face the much more difficult challenge of catching up after competitors have accumulated months or years of experience. Drake believes entrepreneurs ultimately face a decision that extends beyond software selection or operational efficiency. AI can be viewed as another business expense or as an opportunity to expand knowledge, strengthen leadership, and unlock capabilities that were previously beyond the reach of smaller organizations. "The future belongs to the people who stay curious," Kirk Drake says. "AI is ultimately another skill you can learn. The decision to embrace that learning will shape not only your future, but the future of everyone your business serves."

Bausch & Lomb CEO: the AI hysteria is nothing new | Fortune

AI is being discussed as though business has never encountered a technological shift before. It has. Every generation has its breakthrough technology - the innovation that promises to change how companies operate, compete and grow. The technology changes, but the corporate response is remarkably familiar. Companies rush to buy new systems, hire specialists and announce ambitious transformation plans. They focus on which platform to choose, how quickly they can deploy it and whether they are moving as fast as their competitors. In the excitement, it's easy to mistake adopting the technology for having a strategy. We've seen versions of this before. New technologies arrive with predictions that they will redraw the competitive landscape. Companies invest heavily, reorganize around them and assume that early adoption will translate into lasting advantage. In time, however, access broadens. Competitors acquire many of the same capabilities, and features that once seemed revolutionary become standard. That's when the real difference between companies becomes clear. Acquiring a tool and building an organization capable of using it are two very different things. AI may be more powerful than the technologies that preceded it. It may move faster and reach further into every function of a company. I don't discount the magnitude of the change. But the underlying business lesson is not new: Technology does not create lasting advantage on its own. People do. More specifically, organizations that learn faster than everyone else do. That's the part of the AI conversation that businesses risk overlooking. Companies are racing to evaluate models, compare vendors and deploy new tools. Those decisions matter, but they are unlikely to determine which organizations ultimately succeed. Access will continue to broaden, capabilities will spread and today's breakthrough will become tomorrow's expected feature. The more important question is whether an organization's people are prepared to keep learning as the technology changes. That's why I believe the most important AI investment a company can make is not simply in technology. It's in building a workforce that is curious, adaptable and willing to challenge how work has always been done. That kind of culture cannot be purchased from a vendor. It has to be built over time. At Bausch + Lomb, we've tried to make learning part of how we operate rather than something reserved for an occasional training event. Last year, we partnered with Coursera to launch an enterprise-wide AI learning program because we believe AI literacy should become a core business skill, not a niche technical capability. We also made the courses mandatory for our knowledge workers. Some people questioned that decision, which was understandable. Mandatory training is not always welcomed, and completing a course does not make someone an AI expert. But if AI is going to affect nearly every business function, giving people a foundation for understanding and using it should not be optional. Training, of course, is only a starting point. Learning creates value when people put it to work. We launched our VisionAI Challenge and invited colleagues across the company - not just AI experts - to identify practical ways AI could improve how we operate. Ideas came from manufacturing, R&D, commercial operations, finance, HR and other parts of the business. What stood out to me was the range. Some ideas were ambitious. Others addressed small, persistent problems that consume time and make work more complicated than it needs to be. Those ideas may not make headlines, but collectively they can make a company faster and more effective. The experience reinforced something I've seen throughout my career: The people closest to the work often have the clearest view of how it can be improved. The challenge for leaders is to give them the knowledge, permission and opportunity to do something about it. It's equally important to help good ideas travel. That's the purpose of AI in Action, a platform where colleagues share practical examples of how they are using AI to solve problems, eliminate repetitive work and improve how they serve customers. Some examples save dozens of hours each month; others save only one or two. They all have value - especially when one person's solution gives someone elsewhere in the company a better way to approach a similar problem. Over time, those improvements add up. More importantly, the people who develop them become teachers as well as problem-solvers. That's another lesson that predates AI. Companies often treat transformation as something directed from the center: A small group selects the technology, defines the process and tells the rest of the organization how to use it. Central coordination has a role, particularly when it comes to standards, security and responsible use. But lasting change rarely happens by memo. The companies that benefit most from AI will be the ones that equip people throughout the organization to experiment responsibly, share what they learn and help others improve. That also requires leaders to reconsider our own role. For years, leaders were expected to have the answers. Increasingly, our responsibility is to create an environment where people ask better questions. We need to set clear expectations and guardrails, but we also need to be comfortable acknowledging that no one yet knows exactly where this technology will lead. AI will continue to evolve. Today's leading model will eventually be replaced, and capabilities that seem extraordinary now will become familiar. No company can lock in a lasting advantage simply by choosing the right tool at one moment in time. What can endure is curiosity, adaptability and a culture where learning is understood to be part of the job. People often talk about AI as though it is replacing human potential. I think it can amplify that potential, but only when companies spend as much time preparing their people as they do evaluating their platforms. The current frenzy will eventually subside, as it has with other waves of technology. AI will become more embedded, more familiar and more widely available. When it does, the advantage will not belong to the company that was first to buy the latest technology. It will belong to the company whose people kept learning what to do with it. The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

Stop Measuring AI Adoption. The Capability Gap Inside Your Team Is the Real Reason You Are Falling Behind.

The instinct to standardize AI enablement through committees and best practices breaks down at this pace of change -- what actually works is protected, facilitated time (three hours minimum, no multitasking) where every operational group experiments in the context of their own work and shares what they learned. AI is not rolling out in a smooth curve. It is moving in step changes, where what felt like strong performance a few months ago quietly becomes the baseline, often without any clear signal that the bar has moved. And it has moved again since I started writing this article. The pace of this shift is catching most teams off guard. Just in the last few weeks, we've gone from AI tools that help with individual tasks to systems that can operate autonomously across your entire computer. Agentic platforms like OpenAI's Operator, Perplexity's computer use, OpenClaw and Anthropic's Claude Cowork are now executing complex workflows from start to finish. The implications for how we think about work are immediate and hard to overstate. Many tasks that used to take hours can now be done in minutes. A workflow that requires coordination across teams can be handled by one person. The volume of AI innovation shipped in March alone outpaced anything we've seen before, and Anthropic is scaling revenue at a pace no company has hit before. Today, a lot of the conversation still focuses on adoption: who is using AI, who is not and how quickly teams are rolling it out. But that framing misses what is actually happening. Capability is no longer evenly distributed Some people are fully engaged with these tools. They are testing ideas, building custom GPTs, creating skills in Claude and developing real instinct for where AI actually helps. Others use it more cautiously, keeping it at the edges of their work. There is also a third group emerging. These are the people going much deeper, using open-source tools, pushing on the edges of what is possible and running "scary" experiments -- Zuckerberg building an AI version of himself, for example -- sometimes faster than the organization can keep up with. All three groups are using AI, but they are not operating at the same level. That difference compounds quickly by changing how work gets done, how fast it moves and how people experience their roles day to day. The work itself is changing For years, most roles were built around execution. The focus was on delivering the work -- following the process and getting the output across the line. AI is now compressing much of that layer. The work is shifting toward evaluating, connecting and deciding what matters. You can get to a first version quickly now, which changes where people spend their time and how they approach the work. In some cases, it goes further. You can begin to build systems that reflect how you think, how you write and how you approach problems. That starts to change how individuals operate, not just how fast they move. But most organizations are still structured around the previous version of the job. That raises broader questions about how organizations are structured and what roles are actually needed, which I'll come back to in a future article. What actually helps teams keep up When companies think about AI enablement, the instinct is to standardize quickly -- creating rules, forming committees, defining best practices and rolling out a consistent approach. That approach breaks down. It assumes you can define how new capabilities should be used and then push them into the organization. In practice, that model no longer holds up. A more effective approach is to build structured time into how the company operates so every operational group can engage with these tools directly and regularly, within the context of their own work. The premise is simple: people know their own jobs best. At our company, we started with "AI Days" -- a small group would step away from their day-to-day to focus on their own work and explore how AI could drive efficiency or create new value for the business. Over time, we realized everyone should be able to participate, so we introduced more frequent sessions we call "AI Fridays." These are small, facilitated groups, usually no more than ten people, with an AI implementation expert in the room the entire time. People are expected to commit fully. No multitasking, no checking email. We block off a minimum of three hours because anything less isn't enough to engage meaningfully. We close with a short, structured share-out: What did you try? What worked? What didn't? During these share-outs, you start to see how differently people approach the same tools. People from different operational groups are working toward the same goal, but with very different backgrounds and ways of thinking, and that tends to produce outcomes you would not get otherwise. Simply seeing how others approach their work can be surprisingly inspiring and starts to shift how you think about your own. It also helps normalize failure in a very real way. This work takes time, and people do get stuck or go down paths that do not lead anywhere useful right away. When that happens in a group setting, it becomes part of the process rather than something to avoid. Seeing others push through it, or even laugh it off, makes it easier to stay engaged instead of stepping back too early. There is also a broader shift happening. Most people are used to tools like Google, where every interaction starts fresh. AI builds context over time and responds differently as you continue working with it. Getting comfortable with that iterative loop -- where you refine, adjust and build on what came before -- takes practice. Over time, these sessions create a rhythm where people update their sense of what is possible and get comfortable with how quickly things change. It's common to spend hours building something and then see a new capability replace part of it not long after, and that becomes part of how the work evolves. Where leadership makes the difference Leading in this environment means paying attention to how quickly expectations are shifting, including your own. I've seen this happen inside our own business. You spend time with the tools and start to see the work differently. The issue is when that shift stays in your head. The team ends up trying to catch up to a standard they cannot see, and that is where the disconnect starts. There is still a baseline expectation that does not change. If something is being shared or used to make a decision, it needs to be understood and owned. That applies regardless of how it was produced. There are a few behaviors that tend to make the difference: * Own it. If expectations are shifting, leadership needs to take responsibility for driving that change. * Create real space for experimentation. This work requires time and focus, not something squeezed in between other priorities. * Set clear, meaningful milestones. The goals should be ambitious enough to push teams to actually change how they work. * Tie outcomes to incentives. People need to see that leaning into this shift leads to real upside, not just more work. There's a shift that comes with this. Something you spent years learning can now be done differently, and sometimes faster, than you would have done it yourself. That takes a bit of getting used to. You work through it, figure out what works and what does not, then push that back into the team. That's when it starts to change. People pick it up, build on it and take it further than you would have on your own. That's where it starts to get interesting, and where it actually becomes pretty exciting.