AI for the right reasons: avoiding the AI bubble

The Bank of England recently warned about a rising AI bubble, fuelled by hype. This risks a dangerous market correction, similar to what we saw during the dot-com boom. Hype alone doesn’t deliver results and businesses need to be cautious about plunging millions into hype AI as found by MIT 95% of GenAI pilots ultimately fail.

 

Instead, businesses need to focus on innovation that is confidently going to drive value and ROI first before taking these big AI swings.

 

It is easy to be dazzled by the latest large language models (LLMs), text-to-speech (TTS) or text-to-video (T2V) models and the potential benefits they promise without thinking and planning for how to effectively implement them to streamline workflows or improve processes. This can lead to heavy and risky investment in AI without meaningful returns.

 

Success with AI comes from automation that solves real business problems, delivers measurable outcomes, and improves the customer experience.

 

 

Technology Is Ready: Now Focus on Application

Frontier models such as OpenAI’s ChatGPT-5 and Google’s Gemini are already capable of powering practical automation tasks at scale. Beyond simple admin work, AI can streamline decision-making and transform raw data into actionable insights.

 

The temptation to adopt this technology quickly is strong, but businesses must prioritise careful implementation and integration into existing workflows. They should pursue incremental gains, thinking critically about how AI can help and what they need it to achieve.

 

The technology is ready. What matters now is adoption driven by clear business context and objectives. AI needs to be built around how people already work, not bolted on as a separate tool.

 

Too often, businesses start by asking, “What can AI do?” rather than “What is our business problem?” This mindset leads to unnecessary projects focused on the wrong things. As the saying goes, if you’re a hammer, everything looks like a nail. The same applies to AI, if you start by asking what it can do, you’ll find endless tasks it could perform, but not necessarily those that it should.

 

 

Turning Potential into Actionable Outcomes

The main reason AI projects fail isn’t due to technological limitations. Instead, it is because many companies deliver little to no measurable impact on their bottom line. Too often, businesses invest in areas that neither enhance efficiency nor improve the customer experience. What looks great in an AI lab does not always translate to better processes.

 

AI failure is more often caused by organisational issues, not technical ones. As the tech is very strong already, as we know. Gaps in data quality, lack of cross-department collaboration, or unclear ownership can all derail AI initiatives before they start.

 

Successful AI integration happens when technology is embedded into practical processes, automating tasks, running regular compliance checks, and streamlining workflows. Solving important problems that other technology was unable to solve. The goal should always be tangible results, not chasing the newest or shiniest tools.

 

 

Metrics Matter

According to the MIT’s GenAI Divide Report only 5% of custom-built AI tools make it into productive workflows. Businesses are now entering a new period of reflection, one focused on measurable metrics that will define success with clear performance gains and operational impact, not headline-grabbing pilots.

 

It’s easy to claim your business is embracing AI, but what truly matters is whether it’s easing organisational pain points.

 

If expected returns aren’t materialising and measurable, it’s worth analysing which assumptions didn’t hold up. Is it the data, the process, or the problem AI was asked to solve in the first place?

 

And these are questions that businesses will have to start asking more and more often.

 

The meaningful impact of AI lies in automating real business processes that have been measurably costly in the past such as, automating document management, running compliance checks, and boosting productivity. Most importantly, all of this must have a positive impact on the customer. The true value of AI lies in efficiency, accuracy, and explainable success.

 

There is a lot of discussion of explainable AI, understanding what steps AI models actually take to produce results. Another aspect of AI explainability should be understanding what value AI actually creates for the business or their customers.

 

 

What Smart Companies Will Do Next

Smart companies are setting out clear plans for how AI will support their business creating a defined “AI playbook.”

 

The first step is auditing existing AI projects to understand what benefits they’re delivering, whether in efficiency or cost savings. Once this is clear, businesses must determine which initiatives represent legitimate use cases and which are simply shiny distractions.

 

Next, leadership teams that make the most of AI and minimise the impact of a potential bubble popping will be those who have a clear AI roadmap. A strategy that identifies where automation can remove bottlenecks, improve compliance, or help employees make faster, data-backed decisions.

 

Finally, once priorities are identified, AI should be fully integrated into daily operations, embedded in the business’s infrastructure rather than treated as a side project for headlines.

 

The focus must shift from hype to genuine, results-driven applications. This transformation must be led by informed decision-makers who understand exactly where and why AI works best for their organisation.

 

 

Real Intelligence Lies in Application

Where awe once justified AI adoption, authority and strategy must now define its future. Only then can we slowly deflate the bubble this hype cycle has created for businesses.

 

This amazing technology, which at its essence mastered the art of guessing the next best word in a sentence, may not always be the best tool to optimise customer interactions or supply chains.

 

Eventually, the race for the flashiest AI tools will lose momentum. When the dust settles, the real winners won’t be those who chased the newest AI models, they’ll be the companies that pursued purposeful innovation. The true winners will be those who thought logically, applied AI with intent, and built smarter, streamlined operations that deliver measurable results.

 

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Tamas Hevizi is Chief Strategy Officer at Tungsten Automation

 

Main image courtesy of iStockPhoto.com and Just_Super