RESOLVING THE LEDGER
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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 pre...
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