AI Data Infrastructure Statistics
Where the data layer, not the model, is the constraint on AI progress.
What is the biggest barrier to AI progress for cloud teams in 2026?
57% of cloud IT leaders name a data-layer problem as the single biggest barrier to AI or analytics progress: complex data pipelines (26%), access to usable internal data (17%), or the cost of preparing and storing usable data (14%). Only 11% point to AI models or tooling, more than five times fewer than those blocked by the data layer. The bottleneck sits below the model, in the AI data pipeline.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How many cloud teams run AI on production data because backups are unreachable?
75% of respondents run AI workloads against production data. Backups are snapshots by design, not live datasets, so AI workflows can't query them. 54% already use backup or retained data for AI anyway, despite infrastructure built to protect data rather than share it. Running AI on production isn't a preference; it's the cost of the alternative being unreachable.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How long does it take cloud teams to make data usable for AI?
84% of cloud teams take a day or longer to make data usable for AI work, and 23% take more than a week. Only 3% say infrastructure and pipelines aren't slowing their AI initiatives. AI workflows iterate faster than week-long data prep cycles, so teams ship on partial datasets or move on.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Do cloud leaders see strategic value in their retained data?
77% of respondents see retained data as having strategic value beyond recovery, but only 16% say their organization is actively moving toward that use. 94% say easier access to AI and analytics data would be valuable to their business. Intent is near-universal; an AI-ready data layer is not.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Cloud Recovery Statistics
Where confidence in data integrity outruns the evidence, and AI is the new failure mode.
How confident are executives in recovery versus how often it actually fails?
98% of executives are confident in their organization's recovery, yet 56% experienced three or more recovery failures in the past year. 75% of executives say their teams rely on assumptions rather than verified testing when estimating recovery time. The data resilience disconnect is widest at the top of the org, where strategy is set furthest from the evidence.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How long does a full data restore take for most cloud teams?
60% of respondents need six or more hours to complete a full restore. Only 5% can do it in under an hour. When a hyperscaler outage is the cause, a six-hour restore stacked on an eight- to fifteen-hour outage stops being a recovery plan.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Are ransomware operators targeting backup and recovery environments?
77% of cloud leaders are concerned their recovery environments could be targeted in a cyberattack, yet 90% remain confident they could recover from one anyway. Of those confident respondents, 80% had at least one recovery failure in the past year. This is the AI supply chain security problem at the recovery layer: the last line of defense sits inside the blast radius.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Is recovery confidence reliable on its own?
79% of respondents who say they're confident in their ability to restore critical data during a major outage had at least one recovery failure in the past year. Confidence isn't tracking capability, because most teams estimate recovery from assumptions rather than tested restores.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
What do teams do when data recovery or access fails?
64% pull from production systems when recovery fails, 48% rebuild data manually, 45% escalate to another team, 44% delay decisions, and 29% use incomplete data. Respondents could select more than one workaround. Pulling from production moves the threat into the place teams are trying to protect.
Multi-select question. Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How often do cloud teams hit a recovery failure or delay?
38% of respondents experienced three or more recovery failures or delays in the past 12 months, and 80% had at least one. Recovery is now a continuous operational requirement, not an annual tabletop, and the systems built for the old model can't keep up.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Cloud Cost and Data Protection Statistics
Where cost cuts move risk instead of removing it.
Are cost cuts making cloud data less protected?
63% of respondents say storage costs force them to retain or protect less data than they should. 87% of those same organizations also retain data they don't need. Teams aren't cutting the data they don't need; they're cutting the protection on the data they do.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How heavily do data protection costs weigh on cloud budgeting?
63% of respondents say data protection costs weigh heavily in their cloud budgeting decisions. Only 4% say rising costs aren't causing them to rethink retention, protection, or platform decisions. The rethinking is nearly universal; the classification needed to do it intelligently is not.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How does cost pressure correlate with recovery failure?
54% of respondents under cost pressure experienced three or more recovery failures last year, more than four times the 12% rate among those who weren't. The same group is twice as likely to discover protection gaps only after an incident, audit, or failed restore. The dollars saved on backup line items show up as recovery costs elsewhere.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Why are AI workloads squeezing the backup budget?
Cloud infrastructure spending hit $129 billion in Q1 2026, growing 35% year over year, with AI workloads as the primary driver (Synergy Research Group). AI compute scales with usage and can't be capped without breaking the workload. When finance asks where the cuts come from, backup is what's available.
Sources: Synergy Research Group, Q1 2026 (cloud spend); 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Multi-Cloud and Governance Statistics
Where machine-speed provisioning outpaces human-speed classification.
How many cloud teams operate across three or more cloud platforms?
54% of respondents operate across three or more cloud platforms. Multi-cloud is now the default operating model, and the complexity that came with it is permanent. Each cloud brings different identity systems, configuration patterns, and consoles.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Does multi-cloud increase the rate of recovery failure?
84% of teams on three or more clouds had at least one recovery failure in the past 12 months, compared with 72% of teams on one or two clouds. Teams on three or more clouds also discover protection gaps after an incident more often (71% versus 50%). The teams running the most clouds aren't the most mature; they're the most exposed.
Subgroup n: 315 respondents on 3+ cloud platforms; 268 on 1–2. Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How much more do multi-cloud teams hit repeat recovery failures?
46% of teams on three or more clouds experienced three or more recovery failures last year, compared with 30% of teams on one or two clouds. More clouds doesn't just raise the odds of one failure; it raises the odds of chronic failure.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Do cloud teams know when their governance is failing?
97% of organizations on three or more cloud platforms are confident their data can be restored predictably across clouds, yet 84% of those same organizations had at least one recovery failure in the past year. Only 7% say multi-cloud has weakened their visibility. The instrumentation is telling them everything is fine even as failures pile up.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
How often do teams discover unprotected workloads?
63% of respondents frequently or sometimes uncover unprotected workloads due to policy misconfiguration. 72% say migration or modernization frequently or sometimes exposes unexpected protection or governance gaps, and 61% discover protection gaps only after an incident, audit, or failed restore. AI coding agents and automated workflows create, modify, and decommission resources faster than human tagging can classify them.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Are multi-cloud teams more exposed to unprotected workloads?
69% of teams on three or more clouds frequently or sometimes uncover unprotected workloads due to misconfiguration, compared with 57% of teams on one or two clouds. Every additional cloud adds another identity system and console for a workload to slip through.
Source: 2026 Cloud Data Infrastructure Report, Eon, March 2026.
Key Terms, Defined
AI data infrastructure
The storage, protection, and access layer that determines whether data can survive AI-era failure modes and feed AI workloads. Separate from the models and tooling on top of it.
AI-ready data layer
Data should be held in open, queryable formats (such as Apache Parquet and Iceberg) so AI and analytics workloads can use it without rehydration, ETL, or copying it out of protected storage.
Data bottleneck
The point where moving, preparing, or accessing data, rather than the model itself, becomes the limiting factor on AI progress. The survey locates this bottleneck in the data layer for 57% of teams.
Data integrity
The assurance that recovered data is complete, uncorrupted, and free of attacker presence. Ransomware that targets the recovery layer attacks data integrity directly, which is why a restore can reintroduce the threat it was meant to undo.
AI supply chain security
Securing every stage that feeds an AI system, including the recovery and backup layer that holds the data those systems depend on. When the recovery layer sits inside the blast radius, the supply chain has no clean source to fall back to.
AI data pipeline
The sequence of steps that moves data from where it lives to where an AI workload can consume it. Each boundary the data crosses adds a copy step, format conversion, and permissions reset, which is what the 84% who take a day or more to prepare data are paying for.
Where Eon Fits
Every number on this page describes the same root condition: data infrastructure built for a slower, smaller, more predictable cloud than the one teams operate in now. Recovery breaks because the recovery layer sits inside the blast radius. AI stalls because the largest dataset a company owns isn't queryable. Cost cuts backfire because classification is too hard to cut intelligently. Governance fails quietly because resources appear faster than people can tag them. Eon is the cloud data infrastructure that closes those gaps together rather than one at a time: backups land in an immutable, logically air-gapped vault that production credentials and AI agents can't reach, recover at the row, file, or object level in minutes, classify automatically across every account and cloud, and become a queryable, AI-ready data layer in open formats from day one. The teams ahead of this shift aren't running a better backup strategy. They're running a different category of infrastructure.
Methodology
Eon commissioned independent research firm TrendCandy to survey 583 cloud IT leaders and managers in March 2026 about how their organizations recover, govern, access, and use cloud data at scale. All respondents are at the manager level or above, with 18% at the executive level. 68% spend $1 million or more annually on cloud storage, and 77% are at companies with 1,000 or more employees. The margin of error is ±3% at the 95% confidence level. Multi-select questions are labeled where they appear; percentages on those questions sum to more than 100. Subgroup and crosstab figures (for example, the three-or-more-cloud cohorts) carry smaller sample sizes and are noted in line. First-person quotes referenced in the full report are attributed by role rather than name to protect speaker confidentiality.
To cite a figure, link to this page or the full report, attributed to the 2026 Cloud Data Infrastructure Report, Eon, March 2026.
