What’s Blocking AI Success for Today’s Biggest Companies?

Budgets are rising, pilots are multiplying, and every boardroom slide has a generative AI vision tucked between growth projections and risk disclosures. But the outcomes are nowhere near the hype.  

Industry-wide failure rates paint an unflattering picture: roughly 80% of AI projects fail; 60% of companies will abandon most AI initiatives in 2026; half of AI proofs of concept never reach production; only 48% progress beyond pilot; and 95% of generative AI pilots stall before delivering measurable value. Meanwhile, global AI spend is expected to hit $630 billion by 2028 — a number that makes the current success rate look less like innovation and more like mass inefficiency. 

Companies aren’t missing better models. They’re missing the organizational foundation needed to make AI work at scale, and the real blockers to success are hiding in plain sight. If you have dirty socks under your bed, be assured that AI will find them. 

The Hidden Structural Crisis 

The biggest misconception in enterprise AI is that velocity equals readiness. If you build fast enough, experiment widely enough, and invest aggressively enough, value will magically appear. At least, that’s the logic. 

But enterprises are treating AI like a destination, or a finish line you can sprint toward. In reality, AI is a capability that demands tight integration between data, identity, and infrastructure. When those foundations aren’t aligned, the entire ecosystem strains under the weight of its own ambition. 

This misalignment has created three invisible chokepoints: 

• Lack of AI Ready Data and Context 

• Uncontrolled identity sprawl 

• Legacy infrastructure that is unfit for modern AI workloads  

Together, they form a quiet but powerful drag force. Companies race ahead, but true and effective capability never catches up. 

Lack of AI Ready Data and Context 

Most enterprises have migrated to their new Lake environment and cleaned up much of their legacy systems, however data is still proliferating. Even new concepts like data products are being mass created without structure or governance, repeating data management mistakes or omissions from our past that we are still trying to recover from. Data Quality and trust are still a barrier to AI success. 

These large lake environments are just now starting to look at semantic layers, realizing that context is king when it comes to AI Chatbots.  But will they be able to solve the solution themselves, when at the end of the day they are built on a database structure? 

This is how it plays out: 

• Generic Chatbot responses, not representative of your organization or industry 

• Bad data feeding the model causing data to drift to be irrelevant, biased or hallucinations  

• Unable to scale AI, worried about accuracy, explainability, and regulatory exposure.
   

The result is a closed loop of failure that starts with untrusted data, leads to untrusted AI outcomes, and ends with pilots that never scale.  

Breaking that cycle requires more than dashboards or discipline. It demands modern, unified, automated Data Management (AIforAI) platforms that can deliver accurate, governed, and reusable data products at the speed the business actually operates.  

Identity Sprawl 

AI doesn’t just multiply insights; it multiplies identities. Models rely on users, machines, service accounts, APIs, workloads, and data pipelines, with each one as an identity that must be verified, governed, and secured. 

In hybrid environments, these identities spread fast. They become high-value targets. They outrun the capacity of traditional security operations. And they introduce new risks every time a model trains, retrains, calls an API, or requests a dataset. 

Part of the problem is cultural and structural: 

• Siloed ownership separates identity and data strategies that should be inseparable. 

• Legacy governance frameworks were built for slower software cycles, not real-time AI workflows. 

• Department-level incentives reward localized wins instead of scalable, enterprise-wide impact. 

• Teams resist changes that challenge longstanding processes or ownership lines.

If companies can’t secure identities, either human or machine, they can’t responsibly scale AI. Ultimately, risk becomes a ceiling on ambition.  

Legacy Infrastructure 

Many of today’s infrastructure and governance models are built for a world where stability was the priority and change was the exception. AI flips that model on its head. These new technologies require fast iteration, experimentation, and constant model updates. Unfortunately, traditional IT emphasizes control, documentation, approvals, and predictability.  

The clash between these two philosophies produces friction everywhere: 

• Organizations with the strictest governance often have the least AI-ready data. 

• Compliance-first frameworks create bottlenecks that slow or stall scaling. 

• Modernization initiatives (identity migrations, workload shifts, cloud transitions) come with the expectation of zero disruption, an impossible constraint. 

The result is infrastructure designed for consistency that struggles to support systems defined by agility. 

The Path Forward 

Most enterprises are following a comforting but flawed formula: fix the data, tighten the governance, and then, finally, build the AI.  

It feels orderly. It feels strategic. It feels like progress. But in reality, this sequence traps organizations in a perpetual waiting room. Perfect data never arrives. By the time teams agree on what “fixed” even means, the business has shifted, the use case has evolved, and the model planned for development is already outdated. Layering governance afterward only slows things further, turning it into a gatekeeper rather than an enabler. And treating AI as the final destination all but guarantees you never reach it. 

The companies that are breaking through share a fundamentally different mindset: they treat readiness as a living, integrated practice rather than a linear project. Instead of chasing perfection, they focus on building trust incrementally and relying on transparency, measurable quality, and continuous feedback loops to make data good enough to act on, and better over time.  

In these organizations, governance isn’t an afterthought. It’s woven directly into workflows and automation so that responsible practices happen by default, not by decree. Data shifts from being a series of one-off deliverables to a set of reusable products: clearly defined, consistently governed, and built to scale across teams rather than trapped within them. “Trusted, AI-ready” data is no longer a mythical state of flawlessness, but a practical standard defined by clarity, context, and trustworthiness. They are taking those traditional Data Management best practices of the past and using AI to automate and embed them into their day to day data activities. 

And because data modeling, quality, cataloging, and policy enforcement flow through unified systems rather than disconnected tools, AI can finally advance at the pace the business expects. When readiness is integrated and continuously reinforced, innovation stops stalling at the pilot stage and starts compounding across the enterprise. 

Readiness as a Differentiator 

Many organizations are quietly deferring as much as a quarter of their upcoming AI budgets into 2027. At first glance, it looks like hesitation and another sign that the AI frenzy is cooling. But it’s really a strategic pause, mixed with a moment of recalibration after a couple of years of unrestrained experimentation. 

This gap year will create a visible split in the market. Companies willing to confront their readiness gaps (data readiness, identity sprawl, brittle infrastructure) and will use this period to rebuild for scale. Others will try to push forward without fixing the foundation, hoping that another model upgrade or tooling refresh will compensate for structural weakness. It won’t. 

Despite the chatter about an AI bubble, the market is correcting more than collapsing. The industry is shifting away from novelty demos and inflated pilots toward systems that consistently produce measurable value. The organizations that emerge strongest will be the ones that treat AI not as a sprint or a showcase, but as a long-term capability that requires discipline, integration, and structural alignment. 

As the landscape resets, the differentiator becomes unmistakable: readiness, not experimentation, will determine who turns AI hype into meaningful, defensible impact.