The buzz around artificial intelligence (AI) has never been louder. From boardroom ambitions to tech vendor pitches, there’s a pervasive message: go big now or risk being left behind. But that aggressive, all-in style of adopting AI often overlooks a sober truth: many AI initiatives don’t make it. In fact, they fail, stall, or never deliver value. Below, we explore why that is—and how a more measured, realistic approach might serve organizations better.
The sobering data on AI project failure
Here are just a few of the hard numbers companies should keep in mind:
- One survey found that 42% of companies abandoned most of their AI initiatives in 2025 (up from 17% in 2024).
- Another piece reports that 46% of proof-of-concepts (POCs) get scrapped before reaching production.
In short: the high-stakes, go-big push for AI comes with very high risk of non-completion or non-value.
Why many AI projects stumble
What causes this alarming dropout rate? Let’s unpack some of the core reasons.
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Unrealistic scope + high expectations
When organizations adopt AI like a silver bullet, they often set ambitious goals—transform entire business models, deliver magic results, leapfrog competitors. But AI is rarely a plug-and-play fix. Many projects falter because they try to solve too much, too fast, without laying groundwork.
When the expectation is “AI will change everything” but the problem is not clearly defined, the project is vulnerable.
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Data and infrastructure issues
AI’s power comes from data—and many organizations underestimate how much work goes into preparing that data. Poor quality data, inconsistent formats, missing data, or data spread across silos can stall AI initiatives.
Also, the “go big” mindset often assumes infrastructure and integration are already solved—when they’re not.
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Lack of clear business case & ROI
Hard as it is, many AI projects succeed only when tied to a tightly-defined business outcome (cost reduction, revenue growth, productivity gains). Without a measurable objective, it becomes easy for projects to drift.
When you go big without pinpointing what success looks like, you risk spending heavily and achieving little.
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Organizational bystander syndrome / change resistance
Even the smartest model won’t deliver value if the organization doesn’t adopt it. Resistance from users, lack of training, disconnected teams, unclear ownership—all can derail AI even when the tech works.
If you go big without aligning people and processes, you’re likely to fail.
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Slip from pilot to production
Getting an AI model to work in a lab or small test is one thing; scaling it into production across real-world operations is another. Many projects get stuck in pilot limbo.
The leap to full deployment often involves integration complexity, governance, performance issues and more.
Why “Go Big or Go Home” is especially risky
Given the above challenges, here’s why adopting a ‘go big or go home’ posture can backfire:
- Large upfront commitments amplify risk. If you invest heavily in a broad-scope AI initiative and it stalls, losses are large.
- Poor flexibility. Big bets often lack pivot potential; you’re locked into one path rather than iterating and learning.
- Management distraction: Big AI programs demand leadership attention, resources and culture change. That diverts focus from other business priorities.
- Illusion of progress: When you go big, the visibility is high—and so is the pressure. Projects may look good publicly (pilot dashboards, flashy demos) but internally may be failing.
- Cascading failures: If foundational issues (data, governance, process alignment) aren’t addressed, scaling will exacerbate the problems rather than fix them.
In effect, a ‘go big’ strategy is like building a skyscraper without checking the foundation. If the foundation is weak, the building may collapse—or remain uninhabitable.
How a Technology Broker Can Help Build a Prudent Approach
For many organizations, building this kind of disciplined roadmap internally can be daunting. That’s where a technology broker with AI solutions engineers can add enormous value.
At My Resource Partners, our seasoned technology brokers offer a FREE AI Assessment designed to:
- Assesses your readiness: Our AI solutions engineers can evaluate your current data environment, technical maturity, and business priorities to establish a realistic baseline.
- Co-develop your roadmap: We help craft a phased AI strategy that follows the steps above—starting with achievable pilots, defining measurable outcomes, and planning for scale.
- Connect you with the right AI providers: Instead of forcing a one-size-fits-all vendor, our advisors match your roadmap and budget with trusted AI providers whose tools, pricing, and expertise align with your goals.
- Ensure cost-effective execution: Our advisors often have visibility into multiple solutions and can negotiate better pricing or integration terms, reducing risk and saving you 35% versus going direct.
- Support long-term governance and scaling: With an ongoing partnership, we can assist in monitoring performance, compliance, and expansion as your AI initiatives grow.
Leverage our FREE AI Assessment to learn how your organization can avoid common pitfalls—like overinvesting too soon, choosing the wrong platform, or underestimating integration complexity. Instead, they move forward with a structured, well-supported AI roadmap that aligns ambition with practicality and budget discipline.
