Artificial intelligence is becoming an increasingly familiar part of everyday business. From automating administrative tasks to supporting customer service and software development, AI tools are changing how organizations approach problems that once required considerable time, money, and personnel. According to the 2026 EY AI Sentiment Report, 84% of respondents worldwide said they had used AI during the previous six months. Meanwhile, McKinsey & Company’s State of AI Global Survey found that approximately 88% to 89% of organizations regularly use AI in at least one business function.
These figures point to a significant shift in the business landscape. As AI systems become more accessible, companies of different sizes have opportunities to improve existing processes and explore new ways of working. However, adopting the technology is not necessarily straightforward. Integrating AI into established systems can introduce technical complications, require employee training, and challenge long-standing assumptions about how a business should operate.
For many organizations, the greatest obstacle may not be the technology itself, but the willingness to experiment with it.
Younger generations entering the workforce could help accelerate this transition. A Gallup survey on generative AI adoption found that 51% of Gen Z respondents used generative AI daily or weekly. Growing familiarity with these tools may give younger workers a different perspective on how technology can be incorporated into everyday responsibilities.
Jack Zeid, a 20-year-old intern at healthcare company Affinity Direct, is one example of how that perspective can translate into practical business changes.
Rather than viewing AI as a tool that simply provides answers to questions, Jack believes its value lies in helping people complete complex tasks more efficiently. He describes a common mistake as treating AI like an oracle that can answer anything. He says, “It is more useful to approach the technology as your workflow partner, a companion that can do a vastly faster job than you can.”
His experience at Affinity Direct provided an opportunity to put that philosophy into practice.
The company had already experimented with telehealth services across five states, establishing a foundation for delivering healthcare services digitally. However, expanding that model nationwide required further development of the technology and systems supporting the business.
“When I joined, I already could see a vision of this model being taken online, more so through the AI route,” Jack says.
With experience in software development, he began exploring how AI could help connect the company’s existing infrastructure with healthcare professionals and pharmacy partners operating across the United States. Instead of treating every improvement as a separate development project, he used large language models and other readily available AI tools to accelerate work across several parts of the business.
These efforts included rebuilding existing services, introducing peptide and weight-loss programs, and improving subscription management, payment processes, patient portals, communications, and marketing infrastructure.
The significance of this approach was not simply that AI could generate code or assist with individual tasks. It allowed Jack to move between different areas of development more quickly, helping the company explore changes that might otherwise have taken considerably more time and resources.
According to Affinity Direct, the results became visible within the first month of launching its expanded offering. The company reported attracting customers in 40 states, building a subscriber base of approximately 160 people, and generating hundreds of orders.
Brian Zeid, co-founder of Affinity Direct, says the growth was achieved without a marketing budget.
“All this was done without spending any money on marketing. It was all organic,” he says.
Brian adds that the business was already profitable before these developments. For him, the achievement was therefore not about rescuing a struggling operation, but about finding ways to increase the momentum of an established company.
“To accelerate an already accelerating company is kind of impressive, to be honest,” he says.
The experience also changed how the company approached new ideas. Introducing a more technology-focused way of working was not universally embraced at first. Established businesses often develop routines and processes that have served them well for years, making significant changes difficult to justify without a clear understanding of the potential benefits.
Over time, however, Brian noticed a difference in the conversations taking place within the business. Instead of encountering repeated objections about what could not be done, he increasingly heard discussions about what might be possible and how existing processes could be improved.
That shift matters because technology adoption involves more than purchasing software or introducing another platform. Businesses must also reconsider their assumptions, identify areas where improvement is possible, and give people the freedom to test alternative approaches.
Brian is clear that AI was not responsible for creating Affinity Direct’s underlying success. The company had already developed industry relationships, operational knowledge, and an understanding of its customers’ needs over many years. Those foundations remained essential.
Jack’s contribution was to approach the existing business from a different technical perspective. By using AI to support development and problem-solving, he helped identify opportunities to extend the company’s capabilities without having to rebuild everything from the ground up.
This distinction offers a useful lesson for other established organizations. AI does not necessarily deliver its greatest value by replacing an entire business model. In many cases, the opportunity lies in strengthening the systems, relationships, and expertise that a company already possesses.
Healthcare provides a particularly relevant example. Making services more accessible requires careful coordination between technology, providers, pharmacies, and patients. AI may help businesses improve the processes connecting these elements, but successful implementation still depends on appropriate oversight, reliable systems, and attention to patient needs.
For business leaders, the question is therefore not simply whether AI can make operations faster. It is whether their organizations are prepared to identify where the technology can create meaningful improvements and test those possibilities responsibly.
Affinity Direct’s experience illustrates what can happen when an established company combines existing industry knowledge with a willingness to explore new technical approaches. Its foundations were already in place, but AI offered another way to build on them.
As adoption continues to spread, the competitive advantage may not belong exclusively to the companies with the largest technology budgets or the most sophisticated systems. It could increasingly favor those prepared to experiment, learn from the results, and adapt their operations accordingly.
The opportunity is not to adopt AI for its own sake. It is to recognize where new tools can help a business do more with what it already has, then have the confidence to discover what else might be possible.
