What’s really blocking enterprise AI—and how CIOs can fix it

Enterprise AI isn’t blocked because the models aren’t ready. It’s blocked because the data those models need isn’t ready for activation. Across industries, a consistent pattern has emerged: AI isn’t failing on interest, investment, or innovation—it’s failing on scalability.

IDC reports that 88 percent of successful AI pilots never reach production. S&P Global shows the same trend at the enterprise level. In 2025, the share of companies abandoning most of their AI initiatives jumped from 17 percent to 42 percent, with nearly half of all AI proof-of-concepts (POC) scrapped before launch.

The root cause isn’t model performance. It’s fragmented, unreliable data foundations.

Presidio’s AI Readiness Report found that 86 percent of organizations struggle with significant data challenges: from inconsistent permissions to brittle data pipelines. Even among companies already using generative AI, 84 percent report experiencing issues with their data sources.

Taken together, the message is clear—the blocker isn’t the models themselves, it’s enterprise’s ability to govern and orchestrate user data effectively.

CIOs feel this friction every day. Pilots succeed, early demos impress, and executives get excited about the possibilities. But when these same leaders go to scale their AI initiatives across regions, brands, or product lines, progress slows. Not because teams lack strategy or talent, but because they lack the clean, trustworthy data they need to safely activate AI at enterprise scale.

As the leaders accountable for data integrity, governance, and cross-functional access, CIOs are uniquely positioned to break through this barrier. Keep reading to learn how modern user data control platforms help CIOs unify permissions, clean up fragmented data ecosystems, and finally unblock AI at scale across the enterprise.

The silent blockers slowing down enterprise AI

When AI initiatives stall, the cause isn't always obvious. But time and again, bumps in the road can be traced back to one or more data issues. These are the AI blockers CIOs encounter most often.

Permissions are unclear or out of sync

Most large enterprises are holding years of disparate consent and preference data—spread across dozens, sometimes hundreds, of systems. This isn’t just a privacy or compliance challenge, it’s a direct barrier to scaling AI effectively.

Impact:

• Teams can’t confirm which datasets are permissioned for model training or personalization
• Reviews drag on as stakeholders try to validate data usage
• To avoid risk, teams over-restrict data access—starving models of the inputs they need

Industry data shows this isn’t the exception. Many companies still struggle with basic data-governance hygiene even as they accelerate AI investment. When permissions aren’t enforced ecosystem-wide, AI sits on a fragile foundation.

Models train on ungoverned or unreliable data

When permission enforcement and data lineage don’t extend into AI pipelines, models risk training on data that is incomplete, noncompliant, or outright unusable.

Impact:

• Costly rollbacks and retraining cycles
• Heightened regulatory exposure when ungoverned data reaches downstream systems
• Hesitation ahead of launches because teams lack confidence in data governance

These issues are pervasive. Presidio reports that 86 percent of organizations face significant data challenges, and among enterprises already using GenAI, 84 percent struggle with the reliability of their data sources.

Engineering time disappears into manual data plumbing

Many enterprises keep AI initiatives afloat with custom scripts, brittle connectors, and manual workflows. This strategy may work for a pilot, but it doesn’t scale.

Impact:

• Engineering teams spend more time fixing pipelines than building AI products
• Every new model or region requires rebuilding the same plumbing
• Innovation stalls under the weight of maintenance

If it isn’t already, unifying data access and governance should be a top priority for CIOs—until plumbing is automated, AI velocity will remain throttled.

Privacy, compliance, and audit reviews become bottlenecks

Compliance teams aren’t slowing AI initiatives because they want to, they’re putting up roadblocks because they lack real-time visibility into governed, permissioned data.

Impact:

• Reviews that should take days stretch into weeks or quarters
• Personalization, RMN, and AI launches stall
• Confidence erodes across the organization

In global enterprises, where data crosses regions and business units, uncertainty compounds. Without clear visibility and enforced permissions, cross-functional teams have no choice but to proceed conservatively.

Global scale amplifies the risk

Multi-brand, multi-region enterprises must be able to demonstrate good data governance and privacy compliance before they can safely scale AI.

Without it:

• They lack the evidence required for global compliance
• Risk increases with every new system or data source
• AI that works in one market can’t be replicated enterprise-wide

Why CIOs are uniquely positioned to unblock AI

CIOs sit at the intersection of data, systems, governance, and business outcomes—the exact levers that determine whether AI becomes a strategic engine or another stalled initiative. No other executive has both the visibility into the enterprise architecture and the mandate to standardize it.

AI doesn’t slow down because the models aren’t good enough. It slows down because the organization can’t fully trust the data feeding those models. That trust depends on four enterprise-wide capabilities, all of which sit squarely in the CIO’s remit:

• Consistent permissions: A single, authoritative source of truth for what data can be used, for which purpose, and in which system. Without this, AI workstreams stop at legal review or never leave the sandbox.
• Clear visibility: Proven, end-to-end visibility into where data comes from, how it’s transformed, and whether it’s governed. This is the backbone of AI safety, auditability, and cross-functional confidence.
• Automated enforcement: Policy and permissions that update in real time—not through manual scripts, brittle connectors, or one-off engineering workarounds. This is the only way to keep AI pipelines compliant at scale.
• A foundation that scales across brands and regions: Modern enterprises operate in multi-brand, multi-region footprints with fragmented data estates. CIOs are the only leaders with the authority to standardize governance and data access across this complexity.

These aren’t abstract governance principles, they’re operational requirements for AI to reach production, remain compliant, and scale with confidence.

This is why the AI bottleneck is fundamentally both a CIO problem and a CIO opportunity. Today’s AI transformation is, at its core, a transformation in how enterprises manage user data.

CIOs who modernize this foundation unlock a compound advantage for the business:

• Products ship faster because data reviews no longer stall execution
• AI models and personalization improve as teams gain access to clean, fully permissioned data
• Risk decreases as data pipelines become governed by design rather than by exception
• Growth initiatives, from Retail Media Networks to AI copilots, scale across brands and regions instead of stalling out

CIOs who lead this shift become the force multipliers of enterprise AI. Those who don’t will continue to watch promising initiatives get stuck in POC purgatory.

The path forward: An AI-ready user data foundation

The enterprises moving the fastest with AI share one trait: they treat user permissions as an integral piece of their data architecture—not as a legal afterthought, spreadsheet, or set of taped together scripts. They build a foundation where data is usable because it’s governed, and governance is automated at the systems layer.

For CIOs, the path forward is a unified user data control plane—a central layer that normalizes permissions, enforces them across every system, and gives teams the clarity they need to scale AI with confidence. Here’s what that looks like in practice:

User permissions are orchestrated and enforced consistently across all systems

Every dataset, every system, and every AI pipeline reflects the same real-time user choices, meaning there's:

• No drift between regions
• No inconsistencies between the website, CRM, CDP, and model training data
• No “we think this dataset is okay to use” debates that stall AI sprints

When permissions are consistent by design, teams stop slowing down for revalidation and start accelerating towards greater strategic impact.

Only fully permissioned data reaches AI systems

Models perform better when they train on permissioned, high-quality data i.e. the data users have agreed to share. A unified user data control plane ensures that data is automatically filtered, tagged, and orchestrated to honor user’s choices and company data policies before it ever enters an AI pipeline. This leads to fewer rollbacks, less retraining, reduced risks, and models that improve quickly and responsibly.

Manual scripts and one-off integrations are eliminated

Modern AI pipelines break when they depend on custom scripts, brittle connectors, or manual updates to permission logic. A unified data control plane removes this fragility with automated orchestration and deep integrations across the stack. Engineers stop maintaining plumbing, and start building the AI products, recommendation engines, and personalized experiences the business needs to stay competitive.

Audits move forward smoothly

To scale AI globally, enterprises need more than policy, they need proof. A clean user data foundation provides end-to-end visibility that shows where data came from, whether it’s permissioned, and how it’s used across the business. This becomes essential for regional expansion, risk reviews, AI governance requirements, and tight cross-functional alignment.

AI scale quickly with a “deploy once, use everywhere” model

When permission logic is centralized, instead of re-implemented in every region or pipeline, achieving scale stops being so painful. Any new model, brand, or market inherits the same governance framework instantly—no rework, no reintegration, no new rounds of legal review. What once took quarters now takes weeks.

The payoff: AI that actually ships—and scales

Most enterprises don’t struggle with AI models. They struggle with the systems, processes, and permissioning layers that determine whether AI can operate safely, effectively, and at scale. When these layers are fragmented, initiatives stall. When they’re unified, AI becomes predictable, repeatable, and enterprise-ready.

CIOs who modernize the user-data foundation unlock a platform that lets teams:

• Launch AI products faster without bottlenecks
• Improve model accuracy using complete, permissioned data
• Expand Retail Media Networks and other data-driven revenue programs
• Deliver personalization at true enterprise scale
• Reduce engineering overhead tied to manual workflows
• Strengthen governance as a strategic advantage, not a constraint

AI stops being a backlog of pilots and becomes a durable enterprise capability—one that powers every new initiative with speed, safety, and confidence.

The models are ready. Your teams are ready. Now the user-data foundation must be ready as well, and CIOs are the leaders positioned to make that shift real.