After years of heavy investment into AI, companies are eager to see a return on their investment in 2026.
If we take a look at the narrative, there are signs that this may be true. This quarter, executives have noticeably backed away from using the term “pilot” when discussing AI initiatives at the organization. This suggests companies are finally moving beyond experimentation and into scaled deployment.
However, if we take a closer look, the situation is more nuanced. AI experimentation hasn’t gone away, but it’s being reframed in an interesting example of how the language we use to discuss the technology is evolving in lock-step with the tools.
Why companies are rebranding AI pilots
AI engineering firm Solvd recently surveyed 500 U.S.-based CIOs and CTOs at organizations generating more than $500 million in annual revenue.
The recently published report from Solvd points to a paradox at the heart of enterprise AI adoption in 2026.
Although companies may be eager to see a return on their AI investments, the data shows they aren’t moving forward prematurely.
The complexity of using AI at scale across the enterprise means that experimentation without immediate or proven return on investment is now the norm, not the exception. Even so, a striking 90% of respondents say they expect to increase investment in innovative AI initiatives over the next year.
What’s changed is not the appetite for experimentation, but the expectations around how it’s managed.
A focus on governance and quality control
The data from Solvd highlights a positive trend for the AI industry in many ways. Companies are demonstrating a widespread commitment to taking due care with things like governance, even if this does mean extending the experimentation stage.
To illustrate, of the 500 companies surveyed, every one reported that they have begun establishing formal AI governance structures. This represents an increase of 38% in just one year.
Based on this, we can conclude that enterprises are no longer treating AI as a loosely coordinated innovation effort, but as a discipline requiring oversight, accountability, and alignment with business objectives.
The survey also highlighted a widespread aversion to failure and risk mitigation. 72% of respondents said their company is likely to shut down an AI project within the next year if it fails to meet key performance indicators.
This shows us that experimentation is alive and well but enterprise leaders are putting stricter performance thresholds in place.
An era of responsible experimentation
When enterprises pull the trigger on large investments, the pressure is usually on to deliver returns as quickly as possible.
The data from Solvd underscores a broader evolution in enterprise AI strategy in which increased investment is accompanied by increased discipline. Companies are no longer asking whether to experiment, but how to do so responsibly.
Although leaders recognize the importance of governance, execution challenges persist. Eighty percent of respondents reported experiencing at least one AI project failure due to lack of visibility or oversight. That statistic highlights a critical gap: while governance frameworks may exist on paper, many organizations are still struggling to operationalize them effectively.
Despite recognizing the importance of governance and expert execution, leaders are aware that pressure from the top is mounting, with 82% of executives reporting that their boards are increasingly questioning the spend on AI initiatives.
A shift in the language around AI
Experimentation is a key part of due diligence, but executives need to justify the investments to the board by demonstrating measurable value.
This is part of the reason why executives are likely to avoid terms like “pilot” in a widespread shift in how AI is discussed internally and externally. While experimentation in the enterprise is still ongoing, it needs to be presented as more strategic, more integrated, and more outcome-driven, even if they are still exploratory in nature.
This helps to maintain the support of the board and secure continued investment without rushing through important groundwork.
This means that projects are being framed with clearer objectives, tied more closely to business metrics, and evaluated with greater rigor. At the same time, organizations are building the governance and oversight mechanisms needed to manage a growing portfolio of AI initiatives.
How CIOs and CTOs can adapt in 2026
The survey highlights a new set of challenges that CIOs and CTOs need to address this year
If 2024 and 2025 were defined by rapid experimentation and hype, 2026 is shaping up to be the year of accountability.
Although the board is keen to see innovation on the one hand, skepticism and the pressure to deliver mean that clear and strategic communication needs to be part of the strategy.
While failures remain commonplace and visibility gaps continue to undermine progress, tech leaders need to make tough calls on when to pull the plug on certain initiatives and pay close attention to how progress is presented.
With this in mind, the disappearance of the word “pilot” says less about a retreat from experimentation and more about a rebranding of it. Enterprises are still testing, iterating, and learning. But they’re just doing so under a different name, and with higher stakes.
