The preference for AI-native solutions
While 80% of survey respondents believe we’re in an AI bubble, there is still a lot of belief in the long-term power of AI.
When we dug in, we got much more nuance. We had executives admitting that AI would transform their businesses long-term, even despite the current skepticism of existing solutions. If you compare this to the cloud era, few CIOs believed the cloud would become dominant in those early days. The fact that half of our survey respondents already believe this about AI in 2025 suggests we’re at the start of a much faster adoption curve. This means CXOs want to buy into the value AI will create, so founders can’t just bolt AI onto existing products and call it a day. If execs believe this is the defining tech of the next decade, they’ll favor AI-native solutions.
With software development and customer support being the top areas impacted by AI, founders in these spaces need to be pragmatic. They’ll have more competition and have to stand out more boldly. There’s also an opportunity to think about the issues that will come up because of the AI impact in these areas and try to get ahead of them. How will generated code be audited, tested, debugged, and refactored? For customer support, how can sensitive data from certain verticals (healthcare, banking) be protected?
AI budgets are growing, and teams are deploying multiple use cases
Over 68% of technical leaders expect their AI budget to grow by 10% or more in 2026, which is higher than the McKinsey’s January 2025 report where this number was 55%. As AI capabilities increase weekly, we expect to see budgets rise in tandem, even before the end of the financial year. We also had over 45% of respondents say that AI is impacting their headcount plans. This supports our thesis that AI will expand to budgets beyond technology, mainly by going after outsourced services or internal payroll. Certain roles, like data validation, entry-level software engineering, and quality assurance, are prime candidates for AI labor. The lesson for founders here is to demonstrate how enterprises can reallocate budget from traditional labor costs (both outsourced and internal) into AI solutions that deliver superior ROI
Over 80% of the leaders shared that AI is already in their production environments. People shared the many ways their organizations are using AI – from document intelligence, knowledge retrieval, and security automation, to analysis of semi-structured data, support chatbots, and more. This shift from exploration to execution shows that if you can solve a real pain point, even in a narrow domain, buyers will be listening.
Shadow IT and the evolution of ‘build vs. buy’
One CIO said they are already managing over 1000 applications, and aren’t particularly looking forward to how AI will increase their burden. These leaders need to trust the security, data governance, and access controls they’ll need before approving the trendy AI tools and quick-fix solutions that individual contributors will inevitably want to adopt. The key takeaway is that startups should make a concerted effort to align with existing IT and security workflows, whether through seamless integrations or strategic partnerships, and proactively address this alignment from the outset.
The old “build vs. buy” debate generated the most vibrant discussion when we applied it to AI. Several leaders emphasized that a key question IT teams will face is how well an application will be supported after it’s purchased or developed. As CIOs grapple with a surge in application requests (potentially fivefold!), they’re unlikely to approve solutions that offer only isolated features. Instead, they will prioritize platforms that demonstrate strong product vision and established best practices, are intuitive to adopt, and offer access to a vibrant ecosystem of experts who can extend and enhance the platform as needs evolve.
