Finally Focusing on What Matters: Real ROI in the Age of AI

I never know if this is going to be popular or not, but here goes….

Let’s be honest, the hype around Artificial Intelligence has been deafening for the past couple of years. Every other conversation, every industry report, every tech blog has been screaming about the transformative power of AI. And while the potential is undeniably there, I think many of us are finally taking a collective deep breath and asking a crucial question: who is actually making money from all this AI buzz in 2025?

For a long time, it felt like the focus was solely on the possibility of AI, the cool demos, the futuristic applications. Companies rushed to implement AI solutions, sometimes without a clear strategy or a tangible return on investment. We saw a lot of experimentation, a lot of pilot projects, and frankly, a lot of wasted resources. It was like a gold rush, with everyone scrambling for a piece of the AI pie, even if the pie wasn’t fully baked yet.

But I’m seeing a shift. In 2025 and approaching 2026, the novelty is wearing off, and the pressure to deliver real business value is mounting. The companies that are truly going to profit from AI this year are the ones who have moved beyond the initial excitement and are focusing on concrete applications that drive tangible ROI and achieveable business value.

So, who are these winners? I believe we’ll see significant gains for companies providing foundational AI infrastructure and tools. Think about the cloud computing giants that power AI models, the semiconductor companies designing specialized AI chips, and the platforms that streamline AI development and deployment. These are the picks and shovels of the AI gold rush. They are enabling everyone else and are seeing increased demand for their core offerings. For example, companies offering scalable GPU infrastructure are essential for training complex AI models.

Businesses effectively integrating AI to solve specific, high-value problems. The companies that are identifying clear pain points and using AI to address them efficiently are seeing real returns. This could be anything from using AI-powered analytics to optimize supply chains and reduce costs, to leveraging natural language processing for enhanced customer service and increased sales. Consider a logistics company using AI to predict delivery delays and proactively reroute shipments, leading to significant cost savings and improved customer satisfaction.

Niche AI solution providers with deep industry expertise or generic AI solutions that are becoming less attractive. Businesses are realizing the value of specialized AI tools tailored to their specific industry needs. Companies offering AI-powered diagnostics in healthcare, fraud detection in finance, or precision agriculture solutions are likely to thrive because they offer targeted value and a deeper understanding of their clients’ challenges.

Organizations that prioritize data quality and governance are finding AI models are only as good as the data they are trained on. Companies that have invested in building robust data infrastructure, ensuring data accuracy, and implementing strong data governance practices are in a much better position to leverage AI effectively and generate meaningful insights. Poor data quality can lead to inaccurate AI predictions and ultimately undermine the ROI of AI initiatives.

The era of simply “doing AI” for the sake of it is fading. Leaders are now demanding to see the numbers. They want to know how AI is impacting the bottom line, improving efficiency, and driving new revenue streams. This shift in focus is healthy. It’s forcing companies to be more strategic, more targeted, and ultimately, more responsible in their AI investments.

We’re finally seeing a maturation of the AI landscape. The hype is subsiding, and a more pragmatic approach is taking hold. In 2025/2026, the real winners in AI won’t be the ones with the flashiest demos, but the ones who can demonstrate clear, measurable returns on their AI investments and build sustainable business value. It’s about focusing on the “why” behind the AI, not just the “what.”

So, I believe as companies get back to actually REAL business requirements, where we get back to thinking about process improvements, re-engineering, transformative and disruptive business…this is where we make money! As we focus there, AI / ML / BOTs, this will naturally follow. Problem is we need to stop just making something shiny because it’s “automated”.