We Read Leading Industry Analyst Research on DR and AI, So You Don’t Have To

If you’ve been watching the AI conversation in IT over the past 18 months, you’ve probably noticed it has shifted from cautious curiosity to something closer to urgency. Analyst research spanning 2025 and 2026 makes that shift very clear, and your RPS team dug through the analysis to pull out what actually matters.

What Enterprise Organizations Are Actually Doing With AI

AI Hype is at 100%. AI Action is Not

Enterprise optimism about AI is high, but the gap between sentiment and execution is real. The most striking data point: more than half of CIOs listed improving operational resilience as one of their top three IT priorities, but only 1/3 of Heads of I&O shared that priority, with most focused on cost reduction. Everyone above the operational level is picturing an AI-powered future. The people actually running DR are still fighting for basic funding and attention. That’s not a vibe problem; it’s a budget problem.

Automation First, AI Later: Here’s the Proof

This is the most practically important finding from the research, and it shows up consistently across reports: almost ¾ of surveyed organizations have targeted RTOs of less than four hours for mission-critical applications. Yet static runbooks, spreadsheet-based tracking, and manual coordination cannot reliably hit that benchmark.

Most enterprise organizations are still in the automation phase, not the AI phase. They want repeatability, governance, and evidence of recoverability first. AI-driven optimization becomes compelling once the underlying automation foundation is in place. If they haven’t solved automated orchestration, adding AI on top doesn’t fix it.

“If you haven’t solved automated orchestration, adding AI on top doesn’t fix it.”

Organizations Want Evidence, Not Just Trust

Research on IT services vendor selection describes a clear shift away from tenure-based trust and toward auditable evidence. There’s an increasing demand to see delivery data, SLA adherence records, and verifiable proof that recovery objectives will actually be met. For DR specifically, this means validated proof that RTOs and RPOs can be achieved. Tenure used to be enough, but now organizations want to see the receipts.

No Audit Trail, No Deal: What Regulated Industries Actually Require

For healthcare, financial services, insurance, and government industries, the research identifies specific AI requirements beyond general enterprise expectations: data sovereignty (no data egress to third-party cloud models during recovery), full audit trails for every AI decision, and demonstrable alignment with frameworks like HIPAA, SOX, GDPR, and FedRAMP. These are table stakes, not differentiators. Skip one, and it’s not just a red flag; it’s a locked door.

Where Expectations Are Heading by 2030

Forward-looking projections paint a striking picture:

• Almost 3/4 of organizations will implement autonomous reliability practices by 2030, with dramatic MTTR (Mean Time to Resolution or Mean Time to Repair) reductions
• Almost 1/2 of I&O tasks will be augmented by GenAI by 2027, up from only a handful in 2024
• Most new infrastructure designs will be AI-validated before deployment begins

Organizations evaluating DR providers right now should be asking not just what a vendor does today, but where their roadmap is headed. Because betting on a DR partner is a lot like buying a car. You don’t just want to know it drives fine off the lot; you want to know it’s not going to be obsolete in two years.

A Mile Wide, an Inch Deep: The State of AI in DR

Everyone’s In on AI. Almost Nobody’s ALL In

AI adoption across the DR and cyber resilience vendor market is accelerating, but depth of integration varies enormously. The leading data protection platforms are building AI capabilities into their core products, while dedicated orchestration vendors and DRaaS providers are at much more variable stages.

The specific AI capabilities identified as becoming standard in Disaster Recovery Automation and Orchestration (DRAO) include:

• AI-driven recovery optimization: Recommending or automating optimal recovery sequencing based on historical patterns
• Automated runbook generation: AI creating and maintaining DR runbooks based on current infrastructure state, replacing manual documentation
• Analytics for execution reliability: Identifying workflow bottlenecks and configuration drift by analyzing recovery execution data over time

A critical nuance to note is that AI-driven recovery optimization “remains emerging and should not be assumed as a standard capability.” That’s worth keeping in mind when evaluating vendor claims. Translation: if a vendor’s pitch deck makes it sound like the robots have already taken over recovery planning, ask to see it live.

This is What Leading DR Vendors Are Doing

The most mature vendor implementations in this space combine several AI-powered capabilities into end-to-end pipelines.

The leading edge today looks like this:

1. AI-powered backup scanning that validates images are clean before restoration is attempted
2. Synthetic recovery that assembles verified, malware-free restore images from across multiple backup points
3. Cleanroom recovery that only releases restored systems once an AI confidence threshold is met
4. Agentic orchestration that can connect a security alert to a recovery runbook automatically, across multiple platforms and tools.

Vendors building in this direction are framing resilience as a continuous, automated operational discipline rather than a reactive, event-driven function. They’re not waiting for the fire alarm to go off; they’re running the drill before someone smells the smoke.

Watch Out for “Agent Washing”

An important term has recently been introduced that is genuinely useful: agent washing. It describes vendors misapplying “agentic AI” terminology to drive incremental benefits that don’t justify the label, creating market confusion and credibility risk.

Clear warning signs include vendors claiming autonomous AI capabilities with no explanation of how those agents are governed, “AI-powered” claims with no published outcomes data, and single-AI agents with broad permissions across systems, which are explicitly flagged as a security risk. The best vendors in this space are building multi-agent architectures with separated duties, clear human-in-the-loop governance for high-risk actions like executing a failover, and full audit trails of every AI recommendation. If you can’t get a straight answer on who’s governing the AI, assume no one is.

“The best vendors in this space are building multi-agent architectures with separated duties, clear human-in-the-loop governance for high-risk actions like executing a failover, and full audit trails of every AI recommendation.”

What This Actually Means for Your DR & Resilience Program

Industry research maps the vendor market against a four-phase AI maturity model. Most DRaaS providers currently sit in phase one or two, with leading platforms beginning to enter phase three:

• Phase 1 – Align: Identify operational pain points suitable for agent-based automation and establish governance
• Phase 2 – Establish Observability: Centralize telemetry, standardize monitoring, and build a unified data foundation
• Phase 3 – AI Intelligence Augmentation: Layer in AI-driven root cause analysis, noise reduction, and dependency mapping
• Phase 4 – Agentic Resilience: Deploy autonomous agents that can map dependencies, resolve high-churn incidents, and execute remediations before they impact operations

For IT and resilience leaders, this model is a useful benchmark for evaluating both your own program and the vendors you’re considering. The key question is not whether a vendor uses AI, but where they sit on this curve and what their roadmap toward phase four actually looks like.

The bottom line is consistent across the industry research: AI is genuinely changing DR and cyber resilience, but the change is incremental and uneven. Most organizations are still solving the automation problem before the AI problem. And the programs that will benefit most from AI are those that have already invested in the orchestration and testing foundations that make AI-driven optimization meaningful. As in most things in life, there’s no getting around the hard stuff. There is no AI shortcut without the fundamentals first. There never was.

Curious where your own program actually sits on this curve? Start with a free resiliency assessment, or talk to our team if you’d rather begin with a conversation.