Three in four CEOs believe that their AI ambitions match their IT’s readiness. Only one in four IT leaders agrees. Closing the gap requires confronting the hidden realities of data, infrastructure, and governance before racing ahead.
Artificial intelligence (AI) has become the centerpiece of executive strategy, driving bold commitments to transformation, productivity, and competitive advantage. But beneath that confident vision lies a quieter tension: Whereas 75% of CEOs believe that their organization is ready for AI, a significant share of IT leaders report a different reality. Their day-to-day experience reveals fragmented data, legacy architectures, skills gaps, and governance models not built for AI at scale.
Developed in conjunction with NetApp, the IDC AI Maturity Model highlights this disconnect. Surprisingly, less mature “AI emergent” organizations believe they are achieving greater AI success — faster deployments and easier processes — than more mature peers. But the report makes clear that this confidence is misplaced. Emergents haven’t yet attempted the complex enterprise-scale AI initiatives (such as agentic AI) that truly test the limits of infrastructure, flexibility, governance, and security.
In other words, early wins can create the illusion of readiness. Small pilots are easy; scaling AI across an enterprise exposes every weakness in the underlying data architecture.
To bridge this AI ambition gap, forward-thinking organizations are turning to intelligent data infrastructure to power enterprise-scale AI.
Why leaders overestimate readiness
Often, overconfidence stems from incomplete visibility. Early AI pilots usually draw from curated data sets, controlled workflows, or isolated cloud environments. But once organizations widen the aperture, they encounter realities that weren’t visible before, including:
- Siloed data that can’t be unified or governed consistently
- Legacy architectures not built for graphics processing unit (GPU)–intensive workloads
- Inconsistent security and compliance controls
- Poor data quality, requiring extensive preparation
- Lack of visibility into sensitive or regulated data
- Hidden operational costs that derail return on investment (ROI)
These challenges don’t appear in pilot projects, but they dominate production-scale deployments.
Closing the readiness gap without slowing innovation
CIOs don’t need to dampen executive enthusiasm for AI. They do, however, need to redirect it toward the foundation that makes sustainable innovation possible.
Experts point to one clear factor: AI-ready data infrastructure. That means building a data environment where teams can see all their data; understand what is sensitive or valuable; and automate the curation of high-quality, governed, and secure data sets. Actions that can be taken to strengthen data include:
- Unify data visibility across all environments to eliminate blind spots
- Automate data classification and governance to accelerate model development
- Consolidate tooling and simplify operations
- Establish consistent security and compliance policies
- Prioritize data quality as a measurable key performance indicator (KPI)
Having AI-ready data infrastructure, as defined by IDC, reduces the operational burden and accelerates innovation by ensuring that teams spend more time building value and less time wrangling data.
Communicating realities to boards and executive leaders
CIOs need to become adept at translating technical constraints into business consequences. Boards respond most clearly to risk, ROI, and operational impact. The discussion should be framed in terms of:
- Security risk: Poor data governance exposes sensitive data to model leakage.
- ROI risk: Scaling AI on fragmented or low-quality data increases cost and decreases accuracy.
- Operational risk: Without a unified data platform, AI initiatives stall in the transition from pilot to production.
Real-world customer outcomes help reinforce the message. As Jagdish Joshi of vehicle manufacturer Mahindra says, “With NetApp, we’re ready to continue our AI journey with infrastructure that gives us the confidence to innovate without limits.”
The bottom line
AI ambition is accelerating. But capability depends on the quality, governance, and readiness of the data fueling it. Bridging the AI ambition gap starts with acknowledging what pilot projects don’t reveal and building the intelligent data infrastructure that enables enterprise-scale AI to thrive.
Is your organization prepared to realize the full potential of AI? Learn more about NetApp’s vision for AI or take the IDC AI maturity self-assessment to discover your next steps toward AI success.