When OpenAI unveiled ChatGPT, it quickly became the fastest-growing consumer application in history. The generative AI tool fueled ecstatic predictions about exponential leaps in productivity, accuracy and ingenuity. In short, the new development was greeted as a miracle by many.

In the years that have followed, our hopes and expectations for what AI can achieve remain as lofty as ever. To be sure, ChatGPT is an important development and one that has fueled a mini-revolution in content generation. But we’ve also come to understand that this generative AI is hardly a miracle.

Why Miracle AI Seems Less Miraculous These Days

B2B marketing expert Krzysztof Wąsowski recalls his astonishment at first seeing DALL-E create generative images. But only a few months later, as Wasowski touches on, the world had become oversaturated with uncanny, repetitive and mediocre AI-generated content.

In short, says Wasowski, “That cool AI tool you discovered last month? Yeah, everyone’s using it now. Your competitors, that marketing intern, even your tech-challenged uncle. The barrier to entry has completely collapsed.”

Not only is everybody using this technology, but there’s also growing evidence that generative AI is deeply flawed. A 2024 article from Wired predicted that “more and more evidence will emerge that generative AI and large language models provide false information and are prone to hallucination—where an AI simply makes stuff up, and gets it wrong.”

Disappointment In AI

These problems have no easy fix, either. Generative AI tools have already proliferated at a furious pace. So has the mediocre and repetitive content they produce. Of course, generative AI is merely emblematic of where we are on the continuum of technological advancement.

At the moment, a lot of us are asking the same question: Is that all there is?

Even as companies like NVIDIA and Alphabet invest billions in a bold AI-powered future, the current technology is generally falling well short of our shared expectations. The article from Wired warns that we should prepare ourselves for “the Great AI Disappointment.”

But for business owners, this issue is bigger than mere disappointment. Companies both large and small have joined the rush to invest in AI technology over the last few years. We’re all hoping to adopt that miracle AI tech that maximizes our output, minimizes our costs and turns our efficiency up by orders of magnitude. And many of us have spent a lot of money to seize that miracle.

Squaring Expectations With Reality

Unfortunately, for many businesses, expectations have already come face to face with reality. A recent article from Fortune reports that, according to S&P Global Market Intelligence, a startling 42% of surveyed businesses had pulled the plug on the majority of their AI initiatives as of mid-2025. That’s up from 17% in the previous year.

It remains to be seen if this trend will continue into 2026, but I do hear from a lot of businesses that are less than satisfied with the results of in-house AI implementation. Many are still struggling to see a return on their investment, even two or three years into these initiatives.

At a certain point, it’s hard to know whether to plunder ahead and hope for the best or to cut your losses and move on.

Knowing When To Hit The Reset Button

If that describes your situation, this may be the right time to ask yourself a few key questions:

Are you getting your money’s worth out of your investment in AI?

The truth is that, in general, anything that isn’t either saving you money or improving profitability is a technology you simply don’t need. As a 2025 article from CNN Business points out about the current generation of AI tech, “If it’s 100% accurate, it’s a fantastic time saver. If it is anything less than 100% accurate, it’s useless.”

If you can’t trust your AI technology, then you aren’t getting the ROI you deserve.

Does your current AI technology have the capability to scale with your needs?

What is perhaps most disappointing about much of the miracle technology we’ve adopted over the last few years is the relative stagnation of practical innovation. The emergence of generative AI may have felt like a major inflection point, but I think it’s fair to say that this technology has not evolved along with our needs and expectations. In a lot of ways, it feels like we’ve already reached the ceiling on what some of these tools can do.

Do your current AI tools address actual operational issues?

Are you using AI-powered tools because they provide practical solutions to issues relating to efficiency, productivity or data analysis? Unless the answer is yes, your AI tools may just be window dressing. In the rush to adopt the emergent technology, many of us implemented AI-powered solutions that don’t actually speak directly to our operational issues. Now might be a good time to reevaluate your technology.

AI Doesn’t Have To Be Miraculous To Be Useful

Here’s the good news. You don’t need miracle technology.

You just need AI solutions that actually do what they promise and that match your business needs. In the right hands, AI-powered tools can provide all kinds of practical solutions for your business by automating repetitive administrative tasks, extracting key data points from vast volumes of information, delivering insights using advanced data analytics and much more.

This is why I’ve seen many businesses shifting their strategy on AI adoption. Rather than investing heavily in the hope of a tech-powered miracle, many businesses are outsourcing key parts of their operation to vendors who know how to leverage this technology. This approach can be a relief and a reset for businesses that see the value in AI but have been perhaps jaded by promises of something miraculous. (Full disclosure: My company offers services like these, as do others.)

In other words, if you’re looking for a miracle out of your AI, you might be disappointed. If you’re looking for practical tools that can improve and streamline certain aspects of your business operation, you can still get your money’s worth.

See full article on Forbes here