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The AI plateau: What smart CIOs will do when the hype cools
The AI plateau marks a shift from hype to practical results. CIOs who focus on governance and measurable value can navigate this period successfully.
Generative AI has reached a turning point in its maturity curve.
After years of soaring expectations and relentless hype, the enterprise AI market is beginning to transition into a phase of practical implementation -- a shift often referred to as the AI plateau. This phase, while misunderstood by some as a slowdown, is a natural progression in the adoption of transformative technologies. Much like the internet after the dot-com boom, generative AI (GenAI) is moving beyond experimentation and novelty into a realm where organizations can realize its true value.
For CIOs, this transition presents challenges and opportunities. Inflated promises of AI are giving way to the hard realities of scaling, governance and delivering measurable business outcomes. Yet, those who navigate this plateau wisely can reimagine workflows, improve customer experience and increase operational efficiency.
To navigate this period effectively, CIOs can take the following steps:
- Focus on business problems, not AI.
- Adopt a two-speed approach.
- Build minimum viable products, then iterate.
As AI enters a more operational and economically accountable phase, CIOs who focus on governance and workflow redesign -- not hype -- can position their organizations for lasting value.
Understanding the AI plateau
The AI plateau is not a sign of GenAI's failure but rather a shift from the initial excitement and experimentation to the practical realities of implementation, said Graeme Thompson, CIO of Informatica. During the early stages of GenAI adoption, organizations were captivated by its potential -- often driven by the hype surrounding tools like ChatGPT. However, as the technology matures, enterprises are now grappling with the complexities of scaling AI tools, integrating them into existing workflows and using them to meet measurable business outcomes.
This phase requires organizations to recalibrate their expectations. The focus is shifting from deploying AI for novelty or experimentation to embedding it into core business processes. Challenges, such as data quality, governance and vendor costs, are emerging, requiring CIOs to narrow their use cases and strengthen oversight.
"I don't know that people aren't finding value. It's that no amount of value could possibly hope to match the [hype]," said Mike Bechtel, futurist and professor at the University of Notre Dame.
Lessons from past tech cycles
The AI plateau is not an unprecedented phenomenon. History has shown that transformative technologies often go through similar cycles of hype, disillusionment and eventual stabilization. The dot-com boom of the late 1990s, for example, saw soaring expectations followed by a dramatic bust.
Yet, the internet emerged stronger, becoming the backbone of modern commerce and communication. Similarly, AI is now entering a phase where the initial excitement is giving way to the hard realities of implementation. For example, nearly two-thirds of organizations have not yet begun scaling AI across the enterprise, according to a 2025 McKinsey survey. However, the technology's long-term potential remains undeniable.
How smart CIOs will navigate the AI plateau
Many CIOs are shifting from broad, experimental efforts to more deliberate, strategic actions. They're focusing on high-impact business problems and balancing quick wins with longer-term projects.
1. Focus on business problems, not AI
As generative AI moves beyond the hype phase, smart CIOs are pulling back from broad, mandate-driven AI efforts and refocusing on specific business problems. Early on, many organizations told every department to use AI to boost productivity. That approach created energy, but it also produced long lists of ideas that competed for attention and resources.
At the plateau stage, CIOs are becoming more selective. Instead of experimenting with every possible use case, they are selecting a smaller number of use cases that clearly support business goals and can be scaled. The question is no longer whether a team can use AI, but whether it should.
"Unfortunately, right at the start, we had a mandate that said every group needs to look at AI as a way to improve productivity," Thompson said. "They all came up with ideas, but the best idea in, say, legal may not be as good as the 19th best idea in marketing. So why do that one?"
This shift reflects a maturing AI strategy, where business value and alignment take priority over experimentation. Smart CIOs know it's better to implement a few projects well than to chase every shiny use case.
2. Adopt a two-speed strategy
As GenAI moves closer to production, CIOs are finding they can't treat every AI initiative the same way. Some projects are small, tactical and quick to deploy. Others touch core systems, require cleaner data and demand tighter governance. Trying to move all of them at the same pace often leads to delays and stalled deployments.
That divide becomes more visible as organizations move from pilots into production. Early efforts often succeed because they rely on limited data and narrow use cases. Problems surface later, when companies try to scale those projects, said Joe Locandro, CIO of Rimini Street, a third-party support provider for enterprise software. Data needs work. Integrations need automation. Governance gaps become harder to ignore. What initially looked simple suddenly takes much longer.
To manage that reality, CIOs should take a two-speed approach that separates fast, short-term AI projects from larger, long-term efforts, Locandro said. Smaller initiatives help teams learn and deliver quick results. Bigger projects require more planning and investment, especially when they span multiple systems.
"There's tactical, short-term things that you can do," Locandro said. "I do some projects that cost $60,000 … take about 12 weeks, and they bring immediate benefit. And then there's some bigger ones, where I might need to look at my financial ERP system or do some agentic AI on a Workday system."
3. Build minimum viable products, then iterate
A key challenge CIOs face with GenAI is avoiding long, drawn-out planning cycles that try to solve everything at once. As AI technology evolves rapidly, lengthy projects risk producing outdated tools. Where possible, CIOs should instead start with minimum viable products -- focused initiatives that address specific business needs quickly -- and improve them over time.
This iterative approach helps prevent over-engineering large AI transformations that can slow progress and miss critical market opportunities. Bechtel recalls the early internet era when enterprises often overbuilt tools with extensive planning. He shared an example of a nine-month effort to design an enterprise-grade online marketplace for selling meats and cheeses, which aimed to be a full-featured, long-term platform.
"By the time that thing came out of blueprint and into code, the world had already passed it by," Bechtel said.
This example shows why flexibility matters. CIOs who start small and iterate quickly can keep pace with rapidly changing technology, offer value sooner and avoid wasting time on projects that may quickly become outdated.
Key takeaways
Generative AI's shift into the plateau phase signals a new era of practical, accountable adoption. For CIOs, success no longer depends on chasing hype but on applying AI thoughtfully to real business challenges, balancing speed with scale and embracing an iterative mindset.
Those who focus on delivering measurable value, fostering flexibility and maintaining strategic governance can lay the foundation for sustained innovation and competitive advantage. The AI plateau is not a dead end, but rather a launchpad for the next wave of enterprise transformation.
Tim Murphy is site editor for Informa TechTarget's IT Strategy group.
