It’s 2025: Are You Too Late to the AI Party?

The constant waves of hype about artificial intelligence have left many business leaders asking themselves an increasingly urgent question: “Have we missed our chance?” With headlines trumpeting AI successes and competitors announcing ambitious AI initiatives, it’s easy to feel like you’re falling behind. But before you rush to implement AI solutions out of FOMO (fear of missing out), let’s examine why “too late” is a relative concept and why thoughtful preparation may be more valuable than speed. 

When we think about technology adoption, we often imagine a simple race where the first to implement wins. But AI adoption isn’t a sprint—it’s more like preparing for a marathon where we don’t know exactly where the finish line is. The reality is that different industries, companies and use cases are at vastly different stages of AI maturity, though many LinkedIn “AI gurus” gloss over that fact. 

Consider this: while tech giants have been developing AI capabilities for decades, many other (very) successful companies are just beginning their AI journey. And they’re not necessarily at a disadvantage. Why? Because they can learn from others’ experiences, avoid common pitfalls and build on established best practices. Take the example of a well-known financial services firm, for instance. Despite being a huge financial institution, they waited until 2023 to launch their AI-powered platform for market research. Rather than rushing in early, they observed the market, learned from others’ experiences and developed a solution that directly addressed their clients’ needs while maintaining their reputation for reliability and trust.

Starting your AI journey today actually comes with several distinct advantages. The technology is more mature and accessible than ever before. Early adopters had to build everything from scratch, dealing with immature tools and uncertain outcomes. Consider a well-known streaming platform’s recommendation system – their early attempts in the 2000s required extensive custom development and yielded mixed results. Today, a small e-commerce business can implement more sophisticated recommendation systems using pre-built solutions from vendors like Amazon, Google or Microsoft.

There’s also a wealth of case studies and best practices available, allowing you to learn from both the successes and failures of others. New adopters can now avoid many pitfalls by learning from these documented experiences.

Perhaps most importantly, the focus has shifted from pure technical capability to practical business value.

One of the most overlooked truths about AI adoption is that the technology itself is often the easiest part. The real challenges—and opportunities—lie in your organization’s foundation. Let’s explore what matters most.

Your AI initiatives will only be as good as your data. The foundation of successful AI implementation begins with organized and accessible data. Consider the cautionary tale of a major European retailer that invested millions in an AI-powered inventory management system, only to discover their historical sales data was fragmented across multiple systems and contained significant inconsistencies. It took them 18 months to clean and standardize their data before they could effectively use their AI system.

In contrast, Ochsner Health System’s success with AI-driven patient care predictions came from years of careful data collection and standardization. Their early investment in electronic health records and data quality processes paid off when they implemented AI; as a result, the program saw a 44% decrease in cardiac arrests and other adverse events outside of the ICU during a 90-day pilot period.

AI doesn’t fix broken processes; it amplifies existing ones. Success with AI requires clear documentation of current processes and a deep understanding of pain points and bottlenecks. Consider how UPS approached AI adoption: before implementing their ORION routing system, they spent years mapping and optimizing their delivery processes. This groundwork ensured that when they did implement AI, it enhanced already-efficient processes rather than automating inefficient ones.  

Similarly, Mayo Clinic’s success with AI came after developing an in-house AI enablement function that provides consulting services, education and even a medical AI degree program for Mayo Clinic staff. There is also a lawyer in the group who consults on regulatory issues. The team provides guidance on tools, data, regulatory issues and other related use cases within Mayo Clinic that may have already been developed. 

As AI becomes more integral to business operations, proper governance becomes crucial. What’s particularly interesting is that we’re still in the early stages of regulatory development for AI. Major regulatory frameworks are just beginning to take shape. The European Union’s AI Act, proposed in 2021, represents one of the first comprehensive attempts to regulate AI systems, but it’s still evolving. In the United States, various agencies, including the FTC, FDA and NIST are only now developing guidelines and standards for AI use in different sectors.

This developing regulatory environment means organizations that adopted AI early must adapt as new regulations emerge, with regular audits of AI systems and established procedures for updating systems to meet new requirements.

The fact that we’re in the early stages of AI regulation means you still have time to develop thoughtful, comprehensive governance frameworks that can evolve with the technology and regulatory landscape.

Perhaps the most important realization is that successful AI adoption isn’t about copying what others are doing—it’s about understanding your unique business needs and opportunities. Consider how Domino’s Pizza approached AI adoption. While competitors rushed to implement chatbots and voice ordering systems, Domino’s first focused on understanding their customers’ ordering patterns and pain points. This led them to develop DOM Pizza Checker, their AI-powered order quality checking system, which addressed a specific pain point they identified in their operations.

The truth is there’s no universal “right time” for AI adoption. The right time comes when you have a clear understanding of what you want to achieve, when your data and processes are sufficiently mature and when you have the necessary governance frameworks in place.

The companies that succeed won’t necessarily be the ones who adopted AI first but those who built strong foundations and made thoughtful, strategic decisions.

Are you too late to the AI party? The answer is a resounding no.

Focus on getting the basics right—data, processes and governance—and align your AI initiatives with your business strategy. Remember, your deep understanding of your business is your greatest asset in this journey. 

The AI party isn’t ending—it’s just getting started. And with thoughtful preparation and strategic implementation, you can ensure your organization not only joins the celebration but thrives in the AI-enabled future.