Generative AI is advancing at a pace few anticipated. ChatGPT’s growth in revenue and monthly active users has more than doubled year over year, and its momentum continues to accelerate. Businesses are rapidly integrating Gen AI across functions such as customer service, document analysis, and software development. The productivity gains are undeniable.

But here’s the caution: the first answer you receive from a Gen AI tool is rarely the most accurate. In many cases, it’s not even correct.

1. Experience is the Ultimate Filter

Gen AI outputs often sound confident and polished. That can be misleading. Without prior knowledge of the subject, it’s difficult to judge whether the response is reliable or flawed.

Experienced professionals approach Gen AI differently. They don’t just accept the first result. They recognize what’s missing, ask better follow-up questions, and adjust the prompt based on years of hands-on knowledge. They understand the context that Gen AI lacks, allowing them to refine answers quickly and effectively.

This matters because many companies are now using Gen AI to replace entry-level functions. Document review, customer inquiries, and basic code generation are increasingly handled by machines. As a result, opportunities for early-career professionals are shrinking.In fact, the unemployment rate for recent college graduates ages 20–24 has climbed to 6.6%, one of the highest levels in a decade outside of the pandemic years (source: Wall Street Journal). While not the only factor, the rapid adoption of Gen AI tools in place of entry-level roles is clearly part of the equation.

More significantly, Gen AI is beginning to take on tasks that once required seasoned professionals. Companies—including ours—are reevaluating their strategies for growth and scale based on this emerging reality.

Prompt Engineering is a Core Skill

Getting quality output from Gen AI isn’t as simple as typing a question. It depends heavily on how the question is framed.

Think back to the early days of search engines. Users who understood how to refine their queries or apply filters consistently got better results. The same principle applies to Gen AI. Terminology, context, and phrasing all influence what the model produces.

Prompt engineering has become a critical skill. Those who can structure queries effectively and iterate with purpose will consistently extract more value from these tools.

Why Experience Still Matters

The rise of Gen AI has amplified the value of human experience, not diminished it. AI can provide a framework and get you 90% of the way, but identifying what needs to change in that final 10% requires judgment.

That kind of judgment isn’t taught overnight. It’s built over time through trial, error, and insight. The most effective Gen AI users will be the ones who can combine technology with that deep experience.

If your organization is adopting Gen AI, remember this: your competitive edge won’t come from AI alone. It will come from your ability to pair it with the people who know how to challenge it, guide it, and get the best out of it.

Ready to Take the Next Step?

At Evolution Analytics, we work with organizations to plan, build, and implement AI-enabled analytics systems that are grounded in real-world experience and practical governance. If you’re looking to put Gen AI to work in a way that’s smart, strategic, and scalable, let’s talk.

Contact Us to explore how we can help you get it right.

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