Artificial intelligence, or AI, is everywhere. From self-driving cars to facial recognition and language translation to more operational use cases. If we dive into how AI is helping large enterprises with IT operations, there’s a lot of untapped potential particularly for IT disaster recovery (DR). It’s an exciting time.
This article explores the results from Cutover’s IT disaster and cyber recovery trends report focusing on the challenges and potential impacts of incorporating AI into the complex world of IT disaster recovery.
But, first: what is AI and how is it relevant to IT disaster recovery?
AI is defined as the intelligence exhibited by computers to complete tasks typically performed by humans. This “intelligence” includes reasoning, decision making, problem solving, etc.
For IT disaster recovery, AI can be used to create runbook plans, summarize results, identify potential inefficiencies and suggest improvements. Agentic AI, for example is bringing further advancements to runbook task models enabling automated IT DR runbooks to evaluate if recovery outcomes are successful and explore alternative strategies or escalate the issue.
Automation vs. AI: what’s the difference
Automation replaces individual simple and repetitive, manual tasks with scripts. There’s no learning or problem solving with automation. Common examples of IT DR tasks that are automated include: backups, instance provisioning, replicating servers or decommissioning servers. IT DR automation is valuable and necessary - saving IT teams time, reducing risks and human error, and increasing productivity.
In fact, our survey found that enterprises are incorporating automation into disaster recovery at varying levels of maturity. The results found that many enterprises are still on their automation journey with 22% describing their maturity level as having a good level of scenario coverage, and only 15% as optimized IT DR with a fully integrated recovery stack.
AI, on the other hand, focuses on complex tasks which require multiple steps or decision making. AI brings intelligence, learning and adaptation.
When considering AI vs automation - it’s not an either or situation, both are necessary to advance your IT disaster recovery processes.
The limitations of traditional IT disaster recovery methods
Traditionally, IT disaster recovery processes include static disaster recovery plans (DRPs), disparate/disconnected technologies, multiple conference bridge calls to communicate, and manual reporting and auditing. These are resource and time-intensive, costly, and inefficient.
The manual nature of creating traditional IT DRPs can easily lead to them being outdated and inaccurate. For enterprises scaling recovery efforts to thousands of applications, it’s challenging to maintain DRPs for each application tier or disaster recovery strategy. Plus, ensuring they include any relevant lessons learned from the prior live recovery or test scenario.
Our report found that one-third, or 34%, of enterprises last evaluated or updated their DRPs more than 12 months ago. Having outdated IT disaster recovery plans brings significant risks including longer recovery times, operational and financial issues, etc.
The integration of AI in IT disaster recovery to solve known limitations
AI can bring huge efficiencies to IT disaster recovery. From creating plans or templates to analyzing plans and making suggestions, there are countless opportunities.
Ways artificial intelligence is changing IT disaster recovery
Cutover’s recent survey found that enterprises are thinking about the various ways AI and machine learning will impact DR processes in the next 2-3 years. Here’s what survey found:
While there is no single outlier, this shows three ways artificial intelligence will change disaster recovery including: creation, analysis, and summarization of DR plan runbooks.
What is holding organizations back from adopting AI for IT DR?
While enterprises anticipate a breadth of benefits from incorporating AI in IT DR processes, many are considering the implications of it on their business.
While businesses can more easily adopt AI agents for customer service, marketing, or even basic finance processes, IT operational processes, like IT DR, are much more complex. Our recent survey shows that organizations are looking at the following considerations for incorporating AI and machine learning into DR processes:
- Lack of AI skills internally and how best to build or buy these
- Compliance considerations
- Measuring success/ROI
- How, where and when to incorporate AI
- Cost implications
- Concerns about data quality
- Finding appropriate vendors or partners
- Educating the business/users
The future of AI in disaster recovery
Despite these considerations, businesses are still positive on AI’s potential impact in disaster recovery:
- 77% of enterprises surveyed agree that AI will transform the design of disaster recovery plans
- 76% agree that it will transform the execution of disaster recovery plans (DRPs)
The potential of AI is unquestionable, but the reality of how quickly it will be adopted in recovery processes is still to be determined. With 70% of businesses citing that cloud outages are increasing and 83% believing these outages take longer to fully recover from, now is the time to evaluate how AI can bring efficiency to your IT DR processes.
Cutover AI Runbooks - ready for AI IT disaster recovery
Cutover is here to help. Take your IT DR processes to the next level with Cutover AI. Enhance your IT disaster recovery, cloud migration, and application release processes with our AI-enabled runbooks.
- Create runbooks on the fly - include tasks, dependencies and descriptions from multiple data sources
- Auto-generate runbook summaries - give users a brief overview of the purpose and contents of the runbook
- Make intelligent improvements - reduce bottlenecks or inefficiencies with AI-suggested improvements
Learn more about Cutover AI runbooks or book a demo.