How AI Improves Cloud Migration Efficiency

Businesses using artificial intelligence (AI) during cloud migration could streamline processes, saving time and costs. Advanced subsets like natural language processing or machine learning models hold even more potential. How can organizations leverage this technology to improve their efficiency?

1. Accelerates Deployment

Properly cleaning and transforming data in preparation for migration is vital for extracting its total value. AI automates preprocessing, preventing errors and ensuring databases align with the cloud-native architecture. It accelerates time to completion in the process, helping businesses deploy sooner — and with fewer costs — than they initially planned.

2. Realizes Savings

Dozens of hidden migration costs exist, from vendor lock-in to access management. These expenses can significantly impact companies throughout their cloud application’s life cycle. Even though on-demand scalability enables flexibility post-deployment, the drain on their resources may delay their projects — or even induce failure.

While the cloud expenses are much lower than the cost of on-premise data storage, these unplanned expenses can complicate things. However, with AI, businesses can feel at ease knowing they’re realizing savings wherever possible. This technology’s automation and predictive maintenance capabilities reduce expected and hidden migration costs.

3. Automates Compliance

Navigating compliance during cloud migration is complex, requiring meticulous attention to detail. When done manually, it is often a time-consuming and taxing responsibility. With AI, it becomes straightforward — an algorithm can automate recordkeeping, monitor the chain of custody and refactor codebases to align with cloud-focused regulatory standards, helping professionals work much more efficiently.

4. Fills Knowledge Gaps

Whether companies want to shift from on-premises to cloud hosting, move to a new platform or retire some cloud applications while retaining others informs their migration strategy needs. However, realistically, many business leaders won’t know the technical specifics of rehosting, re-purchasing, re-platforming or re-factoring.

These individuals can use an AI-powered chatbot to fill their knowledge gaps, enhancing planning. They can ask it for advice, answers or insights. Since it would have access to a massive database and could rapidly analyze thousands of data points simultaneously, its output would be accurate and comprehensive enough to act as expertise.

5. Improves Resource Management

Whether organizations use a public, private or hybrid platform, an ML model can dynamically allocate computing resources based on forecasted usage patterns and business needs, optimizing performance and simplifying management. More importantly, it ensures businesses only pay for what they need, resulting in significant cost savings.

6. Automates Migration

Automating migration with AI involves using machine learning (ML) to re-architecture on-premises legacy applications, create backups, transfer datasets and configure access controls. Advanced models could do even more complex work. For example, a natural language processing algorithm could phrase-based specifications into working source code.

Since this technology is so versatile, organizations can use it for whatever they need during re-platforming, restructuring or re-purchasing. Those who have already leveraged it have experienced substantial benefits. One accelerated migration timelines by up to 40% by using it to handle data migration and replication while its human experts handle any exceptions.

7. Enhances Decision-Making

Since an ML algorithm can analyze thousands of data points simultaneously, uncover hidden trends in massive datasets and evolve as it absorbs new information, using it for decision-making is a sound strategy. Business leaders can use a purpose-built model to assess the scope of migration, potential dependencies and resource utilization, enabling them to identify possible technical obstacles before they become issues.

8. Minimizes Downtime

This technology can automatically back up servers, datasets and applications to ensure a firm’s existing environment remains retrievable in the event of a catastrophic failure. For instance, if refactoring legacy codebases for the cloud goes awry, having a copy of the original ensures teams lose no time or data.

In addition to preventing unplanned delays, AI also minimizes the amount of time professionals lose during planned service interruptions. Its predictive maintenance and automation capabilities accelerate time to completion. Considering information technology downtime costs approximately $1.5 million annually on average, firms could save a substantial amount.

9. Strengthens Security

A mid-migration cyberattack or data breach could be devastating, forcing companies to postpone their plans indefinitely for incident response and recovery. Considering 75% of enterprises agree cloud security issues are their top concern, it isn’t something team leaders should readily overlook.

With AI, companies can leverage behavioral analytics and real-time monitoring to ensure both of their environments remain safe. This technology can also safeguard data in transit. If it identifies any anomalous activity, it can flag a security professional or trigger a pre-determined incident response. This way, it frees up teams for more complex tasks and accelerates migration.

Enhancing Cloud Migration Efficiency With AI

Migrating to the cloud can be expensive and time-consuming unless businesses plan accordingly and prepare for all contingencies. For small- and medium-sized firms, leveraging AI is an excellent way to gain the necessary expertise and skills to streamline this process.

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ABOUT THE AUTHOR

Zac writes for ReHack as the Features Editor and covers cybersecurity, IT, and business tech. His work has been featured on publications like AllBusiness, CyberTalk, and BLR. For more of his writing, follow him on Twitter or LinkedIn.