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AI has ushered in a lot of never-happened-before moments, but one of the most transformative is its impact on enterprise IT. Companies that have historically been cautious and committed long-term to what they buy are on pace to spend $4.25 trillion on technology in 2026, market researcher IDC predicts<\/a>. It\u2019s almost all driven by AI.<\/p>\n New research from venture capital firm Madrona <\/a>shows that 74% of 150 enterprise IT professionals it surveyed plan to expand their AI budgets in the next 12 months, and the rest plan to hold spending steady. Yet these same enterprises say that fewer than half of their AI pilots ever make it into full production.<\/p>\n That\u2019s actually an improvement. Last year, MIT famously reported that 95% of enterprise AI projects<\/a> had failed in terms of ROI. Fewer than half succeeding is a pretty low bar, but it\u2019s better than a 5% success rate.<\/p>\n But the most telling finding from Madrona\u2019s report is that, even when an enterprise does roll out the AI tech, it doesn\u2019t commit to it long term.<\/p>\n Some 77% of enterprises re-evaluate their AI vendors every six months or even on a rolling basis. \u201cThis creates a \u2018fast in, fast out\u2019 dynamic that is fundamentally different from traditional enterprise SaaS, where multi-year contracts provided a moat of inertia,\u201d Madrona writes in the report. \u201cIn enterprise AI, switching costs are lower and the re-evaluation cadence is relentless.\u201d <\/p>\n This has widespread implications for all those fast-growing annual recurring revenue (ARR) numbers startups report. Enterprise trial budgets are what fueled the initial AI boom of 2025. This year was supposed to be the year these big customers settled in and started committing long term to AI startups<\/a>. Enterprise contracts are what allow so many AI startups to claim astronomically fast revenue growth \u2014 think the phenomenon of startups going from $0-$10 million in three months<\/a>.<\/p>\n Yet, for the first time ever, enterprise revenue remains insecure, even after a startup\u2019s AI product graduates out of a pilot phase and gets adopted by a company.<\/p>\n Part of the issue is that many AI startups haven\u2019t fully landed on a good way to price their AI wares for enterprises. New research<\/a> from VC firm Andreessen Horowitz that surveyed 50 technical AI buyers, found that more than half of them want AI fees tied to the work produced or other outcomes, rather than to usage like the number of tokens consumed.<\/p>\n Charging for usage like tokens is basically a SaaS-era business model. Once an enterprise knows it needs email, or HR software, or cloud storage, it\u2019s merely a matter of how many employees or how much data it must pay for.<\/p>\n For AI, pricing \u201caround the recognizable work\u201d is what helps the startup prove its worth to the customer. When the fees revolve around, say, how many reports are processed, or tickets closed, or leads generated, this makes the product \u201ceconomically valuable to both sides,\u201d writes a16z partners Tugce Erten and Sarah Wang.<\/p>\n All of this means that AI has potentially ushered in a new era of enterprise experimentation. That opens doors to startups \u2014 enterprises are more willing to try their tech \u2014 but it also means an enterprise contract no longer secures long-term revenue. When or if enterprises will revert to their long-term buying habits remains to be seen.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n