Artificial intelligence is being discussed as though business has never encountered a technological shift before. It has.

Every generation brings its own breakthrough innovation — one that promises to redefine how companies operate, compete, and grow. The technology itself changes, yet the corporate response follows a strikingly familiar pattern. Companies rush to procure new systems, hire specialists, and announce sweeping transformation initiatives. Attention centers on which platform to select, how quickly it can be deployed, and whether competitors are moving faster.

Amid that excitement, it becomes easy to conflate adopting a technology with having a strategy.

This dynamic has played out before — from the enterprise software boom of the 1990s to the cloud migration wave of the 2010s. New technologies arrive accompanied by predictions that they will redraw the competitive landscape. Organizations invest heavily and restructure around them, assuming that early adoption will translate into durable advantage. Over time, however, access broadens. Competitors acquire many of the same capabilities, and features that once seemed revolutionary become baseline expectations.

That is when the true differentiator between companies becomes clear: acquiring a tool and building an organization capable of leveraging it are fundamentally different endeavors.

AI may well prove more powerful than the technologies that preceded it. It may move faster and penetrate more deeply into every corporate function. The magnitude of the change is not in question. But the underlying business lesson is not new — technology alone does not create lasting advantage. People do. More precisely, organizations that learn faster than their peers do.

That is the dimension of the AI conversation that businesses risk neglecting. Companies are racing to evaluate models, compare vendors, and deploy tools. Those decisions carry weight, but they are unlikely to be the factor that determines which organizations ultimately prevail. Access will keep broadening, capabilities will diffuse, and today's breakthrough will become tomorrow's standard feature.

The more consequential question is whether an organization's workforce is prepared to continue learning as the technology evolves.

That is why the most important AI investment a company can make is not solely in technology itself. It lies in cultivating a workforce that is curious, adaptable, and willing to question how work has always been done. Such a culture cannot be procured from a vendor. It must be developed over time.

At Bausch + Lomb, a company with roughly 170 years of history navigating industrial and technological change in eye health, the company has sought to embed learning into daily operations rather than relegating it to occasional training sessions.

Last year, Bausch + Lomb partnered with Coursera to launch an enterprise-wide AI learning program, grounded in the belief that AI literacy should become a core business skill — not a niche technical competency. The company also made the courses mandatory for its knowledge workers. This approach mirrors a broader pattern across large enterprises, where companies from financial services to manufacturing are increasingly treating AI fluency as an essential workplace competency rather than a specialized skill.

That decision drew some pushback, which was understandable. Mandatory training is not universally welcomed, and completing a course does not confer AI expertise. Yet if AI is poised to affect nearly every business function, providing employees with a foundational understanding of how to use it should not be treated as optional.

Training, of course, is only a starting point. Learning generates real value when people begin applying it.

Bausch + Lomb launched its VisionAI Challenge, inviting colleagues across the entire company — not only AI specialists — to identify practical ways AI could improve operations. Submissions came from manufacturing, research and development, commercial operations, finance, human resources, and other business units.

What stood out was the sheer range of contributions. Some proposals were ambitious in scope. Others tackled small, persistent inefficiencies that consume time and introduce unnecessary complexity into daily work. While those smaller ideas may not capture headlines, collectively they can make an organization faster and more effective.

The experience reinforced a principle observed throughout a long career: the people closest to the work often have the clearest perspective on how it can be improved. The challenge for leadership is to equip them with the knowledge, permission, and opportunity to act on those insights.

It is equally important to ensure that good ideas spread.

That is the purpose of AI in Action, an internal platform where colleagues share concrete examples of how they are using AI to solve problems, eliminate repetitive tasks, and improve customer service. Some examples save dozens of hours per month; others save only one or two. Each carries value — particularly when one employee's solution provides a colleague elsewhere in the organization with a better approach to a similar challenge.

Over time, those incremental improvements compound. Equally significant, the individuals who develop them evolve into teachers as well as problem-solvers.

That, too, is a lesson that long predates AI. Companies frequently approach transformation as something directed from the center: a small group selects the technology, defines the process, and instructs the rest of the organization on its use. Centralized coordination certainly has a role — particularly regarding standards, security, and responsible deployment — but lasting change rarely takes hold through corporate mandate alone.

The organizations poised to benefit most from AI will be those that empower people throughout every level to experiment responsibly, share what they discover, and help colleagues improve. That shift also requires leaders to rethink their own responsibilities.

For years, leaders were expected to provide definitive answers. Increasingly, the obligation is to foster an environment where people ask sharper questions. Leaders must establish clear expectations and guardrails while remaining comfortable acknowledging that no one yet knows precisely where this technology is headed.

AI will continue to evolve. Today's leading model will eventually be superseded, and capabilities that now seem extraordinary will become commonplace. No company can secure a lasting advantage simply by selecting the right tool at a single point in time.

What can endure is curiosity, adaptability, and an organizational culture in which continuous learning is understood as an integral part of the job.

A common narrative frames AI as a force replacing human potential. In reality, it can amplify that potential — but only when companies invest as much effort in preparing their people as they do in evaluating technology platforms.

The current frenzy will eventually recede, as it has with previous waves of technological change. AI will become more deeply embedded, more familiar, and more widely accessible. When that happens, the advantage will belong not to the company that was first to purchase the latest technology, but to the organization whose people never stopped learning what to do with it.

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