What’s next for generative AI? From hype to meaningful results for businesses

Generative AI has reached the peak of inflated expectations on Gartner’s Hype Cycle, a model that shows how emerging technologies evolve over time. It starts with a breakthrough, rises into hype and inflated promises, drops into disillusionment, and eventually levels off into practical use cases that actually deliver value.

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Today, generative AI is in the spotlight. Tools like ChatGPT, Copilot, and custom LLMs are being integrated into workflows, applications, and teams. But beneath the surface of the buzz is a growing question: is generative AI really bringing value to businesses, or is it just noise?

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The genAI divide: lots of investment, limited return?

According to MIT’s State of AI in Business 2025 Report, despite $30–40 billion invested into genAI by enterprises, 95% of companies are seeing zero measurable return on that investment.

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The key finding: there is a “GenAI Divide” between a small group of companies generating real value, and the vast majority who are not. Just 5% of AI initiatives are translating into meaningful financial impact. Most companies are stuck in pilot mode or have failed deployments.

Some of the reasons:

• Most AI tools are used for individual productivity (e.g. drafting emails, writing code), not improving the core business processes that drive profit & loss (P&L) impact.
• Tools like GPTs or Copilot help employees, but this doesn’t automatically scale up to enterprise-level efficiency or growth.
• Enterprise-level genAI systems often fail due to brittle workflows, lack of contextual learning, and poor alignment with real-world processes.

So, according to the report, while AI adoption is high — with 80% of organizations piloting tools and 40% deploying them — most of the impact is still superficial.

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The criticism: is the bar for “value” too narrow?

That report has sparked debate. Critics, including the Marketing AI Institute, argue that the MIT study may be oversimplifying the picture.

The main counterpoints:

• Definition of success is too narrow: Requiring P&L impact within six months misses long-term or indirect benefits.
• Other metrics matter: Improvements in productivity, lead conversion, customer satisfaction, or faster sales cycles can all be valuable, even if they’re not immediately tied to revenue.
• Small sample size: The study is based on 52 interviews and an analysis of 300 AI initiatives, but lacks transparency on how the data was evaluated.

In short, the criticism is: just because an AI initiative doesn’t move the P&L needle in six months doesn’t mean it has no value.

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So where is the real value?

This is where the hype cycle helps us. We’re likely heading into the Trough of Disillusionment, the stage where reality sets in and many early initiatives fail. That’s a normal part of the cycle. But it’s also the time when real opportunities emerge.

GenAI’s true business value lies in:

• Enhancing structured and unstructured processes: For example, many companies have years of documents, manuals, and internal data containing knowledge that is hard to leverage using business process automation. GenAI can now unlock that information and embed it in daily workflows.
• Supporting humans, not replacing them: The idea of “replacing jobs” is often overstated. AI works best when it augments human decision-making and removes repetitive tasks — not when it replaces critical thinking or customer empathy.
• Embedding AI in your business logic: Instead of using genAI as a standalone tool, companies need to integrate it into end-to-end business processes. That means thinking beyond chatbots and focusing on how AI can trigger, update, or validate business workflows.

Generative AI isn’t magic. It won’t fix broken processes or turn poor data into business growth. But when used intentionally — not just as a chatbot, but as part of a digital process — it can reduce waste, unlock value in your existing assets, and free up your team to focus on what matters.

The hype will fade, but the companies that structure their processes to use AI effectively will come out ahead. The opportunity is not about replacing people. It’s about enabling them to do more — and do it better.