Despite the hype and investment, artificial intelligence isn’t delivering results at scale for most organizations. The problem isn’t ambition—it’s execution. According to multiple industry surveys, IT leaders are running into hard, stubborn barriers that prevent AI from moving beyond experimentation into something that delivers real business value.
Here’s what’s actually holding them back.
1. Data and Infrastructure: The Foundation Is Cracked
You can’t build AI on bad data and outdated infrastructure—and that’s exactly what many organizations are trying to do.
- Poor data quality is a core issue, with 37% of U.S. IT leaders citing it as a major roadblock. Without strong governance, security, and sustainability practices—highlighted by Hitachi Vantara—data becomes more of a liability than an asset.
- Things are only getting harder: data volumes are expected to triple by 2026, putting even more pressure on systems already struggling to keep up.
- Infrastructure limitations are another killer. 44% of organizations name this as their #1 AI scaling challenge. It’s not just about servers—it’s about sluggish data processing, siloed storage, and inefficient pipelines.
- Qlik’s research backs this up: 74% of leaders are weighed down by high infrastructure costs, disconnected data, and slow ingestion speeds.
2. Talent Gaps: No People, No Progress
AI isn’t just about machines—it’s about the people who build and guide them. And right now, that talent is in short supply.
- 85% of tech leaders blame AI delays on a lack of skilled professionals. For 45%, it’s their top challenge.
- According to Kyndryl, 71% of leaders say their workforce is flat-out unprepared for AI, with many employees resisting adoption due to fear of job loss or simple inertia.
- Only half of organizations have structured training programs, and 36% rely on self-teaching—which is no way to build enterprise-grade capabilities.
- EPAM stresses the need for secure experimentation environments (a.k.a. sandboxes) where teams can build without risking critical systems.
3. Security and Trust: The Risk is Real
AI opens up new doors—but also new vulnerabilities.
- In India, 54% of IT leaders rank data security risks as the top barrier to AI adoption. Globally, the same number see security as the biggest infrastructure concern, which is 17% above the global average.
- On top of that, trust in AI remains shaky. Qlik found 42% of executives don’t fully trust AI outputs. Age plays a role: leaders under 45 are twice as likely to trust AI as those over 45, hinting at a generational trust gap.
4. Culture and Strategy: Everyone’s Not on the Same Page
Technology can’t fix organizational misalignment. AI success depends on clear vision and full leadership buy-in—and both are often missing.
- Kyndryl reports a split between CEOs and IT leaders on how AI affects the workforce. While CEOs report employee resistance, tech leaders are often focused on enablement.
- Kainos points to the need for psychological safety and curiosity in the workplace—two cultural traits that are essential but often overlooked in AI planning.
- Then there’s the business case problem: 59% of organizations struggle to identify valuable use cases, and only 7–14% see ROI from generative AI projects, according to Auxis.
- A.Team shows the disconnect clearly: while 96% of leaders plan to invest in AI, only 36% have successfully deployed it in production.
Bottom Line: AI Isn’t Just a Tech Challenge
The data is clear: AI failure isn’t about lack of tools or ambition. It’s about systemic issues—weak data practices, outdated infrastructure, a shortage of skilled talent, low trust, and strategic misalignment.
To succeed with AI, organizations need more than a roadmap. They need real investment in people, clear alignment across leadership, and infrastructure that can handle the scale. Otherwise, AI will stay stuck in the pilot phase—full of promise, but empty on impact.