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Why is AI infrastructure so complex?
An AI system is a chain of dependencies: data sources and pipelines, models and vendors, cloud or other computing environments, applications, security controls, and the people responsible for operating them. A weak link can constrain the whole service. For example, a capable model cannot deliver reliable business results if its data is poor, inaccessible, out of date, or restricted from moving to the environment where the model runs.
Complexity grows when teams select tools locally. Separate business units may adopt different models, platforms, data arrangements, and controls. Those choices can solve immediate needs, but they increase the work required to integrate systems, track spending, enforce policy, and understand what depends on what. DDN’s 2026 report summary attributes complexity in part to fragmentation, data movement, and manual orchestration; DDN is an infrastructure vendor, so its survey findings should be read in that context.
The problem is therefore not simply whether an organization has enough GPUs or cloud capacity. It is whether the full operating environment is understandable and manageable as AI use expands.
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What do executive surveys say about the control gap?
The findings below come from distinct surveys with different populations and sponsors. They are respondent reports, not audited measures of every enterprise or directly comparable estimates.
| Publisher and study | Survey scope | Reported findings |
|---|---|---|
| IBM, June 17, 2026 | IBM and Oxford Economics surveyed 1,000 senior executives across 16 countries and 17 industries from February through April 2026. | 91% said they did not fully understand dependencies across AI vendors, models, and infrastructure; 71% said switching their primary AI vendor or model would be difficult; 68% said meeting data residency and sovereignty requirements across geographies was challenging. IBM study summary |
| IBM, June 8, 2026 | 2,000 senior technology executives across 33 geographies and 19 industries, surveyed from January through April 2026. | 77% said AI adoption was outpacing current governance capabilities; 70% said teams were deploying technology faster than IT could track; 11% believed they were fully ready for expected AI agent deployment scale. IBM study summary |
| Google Cloud, 2025 State of AI Infrastructure | More than 500 global technology leaders. | 98% of surveyed organizations were exploring generative AI and 39% had it in production. The report identifies data quality and security as leading challenges and cost efficiency as both a consideration and potential benefit. Google Cloud report |
| DDN, 2026 report summary | 600 business and IT decision-makers. | 65% considered their AI environments too complex to manage; 54% had delayed or canceled AI initiatives in the prior two years; 97% said cloud infrastructure was essential to scaling AI. These are DDN-reported survey results from an infrastructure vendor. DDN report announcement |
The IBM results describe two different surveys: one focuses on executive visibility into dependencies and switching, while the other highlights the pace of deployment relative to governance and IT tracking. Neither should be treated as proof that every organization has the same control gap. Google Cloud’s and DDN’s figures likewise reflect their own survey populations and publisher perspectives.
Why do AI pilots fail to scale?
A pilot can work because it is small, carefully supported, and insulated from the conditions that determine whether a service can operate across an enterprise. KPMG’s 2026 analysis warns that pilots may rely on curated data, limited integrations, concentrated expertise, and manual work that is hidden or unsustainable at production scale.
Moving beyond a pilot means answering practical questions that a demonstration may not test:
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- Data: Are the required data sources reliable, current, appropriately permissioned, and available where the workload runs?
- Integration: Can the AI service connect to business systems and workflows without fragile, one-off links?
- Controls: Can the organization apply security, governance, and audit requirements consistently?
- Ownership: Is there a named team responsible for support, incidents, updates, and ongoing operation?
- Economics: Can leaders see the costs of the model, infrastructure, data movement, integration, and human oversight alongside the value delivered?
If those answers are missing, a successful demonstration is evidence that a use case may be promising—not that it is ready for broad deployment.
How can leaders govern AI across vendors and business units?
Governance needs to keep pace with deployment. IBM’s June 8, 2026 survey found that 77% of respondents said adoption was outpacing governance and 70% said teams were deploying faster than IT could track. These results make visibility an operating requirement: leaders cannot govern systems they cannot inventory or understand.
That does not mean every AI decision must be centralized. Business teams need room to identify useful applications, while technology, security, risk, legal, and data leaders need enough information and authority to set guardrails and manage shared dependencies. A workable model makes decision rights explicit: who can approve a use case, choose a vendor or model, authorize data access, assess risk, monitor performance, and respond when requirements change.
Deloitte’s 2026 enterprise report says organizations feel less prepared in infrastructure, data, risk, and talent even as more report strategic preparedness. It also says only one in five companies has a mature governance model for autonomous AI agents. Those are Deloitte-reported findings, not independently audited universal measures. They underscore the distinction between having an AI strategy and having the operational capabilities to govern it.
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How can organizations control AI costs?
AI value and AI costs can appear in different parts of an organization. A business team may sponsor an application while infrastructure, data, security, and IT teams absorb the costs of computing, storage, integration, monitoring, and support. If those expenses are not connected to the use case, leaders can mistake local budgets for total cost—or judge an initiative’s value without counting the resources it requires.
KPMG’s 2026 analysis describes a cycle in which local initiatives prompt bespoke technology and governance decisions, raising integration and oversight demands while making enterprise-wide cost and value less clear. A more useful view connects each workload’s business purpose to the full set of enabling costs and operational responsibilities. Google Cloud’s 2025 report identifies cost efficiency as both a consideration and a potential benefit, but does not establish that a particular platform or deployment model will reduce costs for every organization.
How should leaders compare AI infrastructure choices?
No deployment model or provider is established by the cited sources as best for every organization. Compare options against the requirements of the workload and the organization’s ability to operate it, rather than treating a cloud, on-premises, or hybrid label as a decision in itself.
| Decision area | Questions to answer |
|---|---|
| Workload fit and performance | What response time, throughput, availability, and capacity does the use case require? |
| Total cost and visibility | Can costs be attributed across model use, compute, storage, data movement, integration, and human support? |
| Data location and security | Where must data reside, which rules apply across geographies, and how will access and protection be enforced? |
| Governance and accountability | Who approves changes, monitors the system, maintains records, and handles incidents? |
| Resilience | How will the service respond to outages, model deprecation, vendor changes, or new regulatory and business requirements? |
| Portability | What work would be required to move data or workloads to another vendor, model, or location? |
| Integration and operations | Which business systems must connect, and which team has the skills and capacity to maintain those connections? |
These questions turn infrastructure selection into a business-risk and operating-model decision. In its June 17, 2026 study, IBM reported that 71% of surveyed executives expected difficulty switching their primary AI vendor or model, while 91% did not fully understand dependencies across vendors, models, and infrastructure. The practical implication is to understand both the dependencies an option creates and the effort required to change it.
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- Map dependencies before expanding use. Record which data, models, vendors, platforms, applications, and teams each important AI service relies on. Identify unclear ownership and cross-geography data requirements.
- Set minimum production-readiness checks. Require evidence for data quality and access, integration, security and governance controls, support ownership, cost visibility, and incident handling before treating a pilot as production-ready.
- Give governance and IT visibility at deployment speed. Establish a current inventory and clear approval and monitoring responsibilities so business experimentation does not become invisible infrastructure.
- Connect costs to outcomes. Bring enabling costs from different teams into the same decision view as the business value, including ongoing integration and operational work.
- Test the ability to change course. For critical workloads, understand what it would take to move data or workloads, replace a vendor or model, or meet new location and regulatory requirements.
These steps address the mechanisms behind complexity without assuming that complexity alone determines AI success. The cited surveys and analyses describe risks and reported challenges; they do not establish a universal causal estimate for its effect on AI returns.
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