The numbers are getting worse
A new S&P Global Market Intelligence survey of more than 1,000 enterprises in North America and Europe finds that 42% of companies scrapped most of their AI initiatives in 2025 — up sharply from 17% the previous year. On average, organizations abandoned 46% of their AI proof-of-concepts before reaching production. VentureBeat covered the findings on March 15, noting that organizations succeeding with AI are taking fundamentally different approaches than those failing.
The most cited obstacles: cost overruns, data privacy and security concerns, and the persistent inability to move pilots into production. Nearly two-thirds of enterprises admit they cannot make that transition — even as they increase generative AI budgets.
The failure pattern is consistent: engineering teams build prototypes that product or operations teams cannot use. Data scientists deliver demos that fall apart when real data arrives. AI systems get deployed without clear rules for when they can act autonomously and when they need human review. And when the project collapses, the budget disappears and the scepticism grows.
Why AI projects fail in production
The analysis from VentureBeat identifies three recurring organizational problems. First, AI literacy is confined to engineering. When only the team that builds it understands how a system works, collaboration breaks down. Operations managers cannot validate what they cannot interpret. When something goes wrong — and it will — nobody outside engineering knows how to respond.
Second, autonomy rules are either too strict or nonexistent. Organizations default to two broken extremes: every AI decision requires human sign-off (killing the efficiency gains) or the system runs without any oversight at all (until something expensive goes wrong). What's missing is a clear framework defining when AI can act, when it must ask, and who is accountable for each decision.
Third, no one owns the handoff. When a pilot transitions to production, someone needs to own it operationally. When departments develop their own AI approaches in isolation, the result is inconsistent quality, duplicated effort, and systems that work in demo conditions but not in the messy reality of production data.
Laava's perspective: this is an engineering problem, not an AI problem
These failure patterns are exactly why Laava was founded. The market is not short on AI enthusiasm or vendor promises. What it lacks is engineering discipline applied to the full problem: not just the model, but the process, the data, the integration, the guardrails, and the handover.
The 46% pilot abandonment rate cited in the S&P Global report is not a technology failure. It is a scoping failure. Projects start too broad, measure too late, and assume production will work the same as the demo. A pilot that processes invoices for one supplier is a concrete, testable hypothesis. A pilot that "automates procurement" is not.
The autonomy problem is equally solvable with the right architecture. Laava builds agents in shadow mode by default: AI processes the data and prepares an action, but a human approves before execution. This builds organizational trust while generating a real-world record of the system's accuracy. When the error rate is low enough and the business unit is comfortable, autonomy can expand incrementally. No big-bang launch, no catastrophic failure.
The cross-functional handoff problem comes down to documentation and ownership. Every Laava engagement delivers infrastructure as code, architecture documentation, and a handover session for the client's internal team. The goal is not dependency — it is a system the client can own and operate after we leave.
What you can do differently
If your organization has had an AI pilot fail or stall, the first question to ask is not "what model should we use?" It is: "what is the exact process we are automating, who owns it, and how will we know if it is working?" Those three answers define whether a pilot has any chance of reaching production.
Laava's Roadmap Session — free, 90 minutes — starts with exactly those questions. If AI cannot solve the stated problem, we say so. If it can, we define a Proof of Pilot narrow enough to test in four weeks, with measurable outcomes defined before a single line of code is written. That is the difference between the 42% who abandon their projects and the ones who do not.