In the age of AI, hybrid cloud is no longer a strategy of convenience – it’s a strategic necessity.
The data bears out this maxim. According to Google Cloud’s State of AI Infrastructure report, 98% of organizations are actively experimenting with, developing, or using generative AI in production, with 74% preferring a hybrid cloud approach for deployments. This overwhelming adoption underscores why companies must rethink hybrid cloud strategies in light of AI demands, regulatory pressures, sustainability goals, and performance expectations.
AI workloads demand both the elasticity of cloud and the performance of on-premises environments, so the future is clearly hybrid. This hybrid cloud future will see intelligent enterprises leveraging aspects of the cloud and on premises – based on the needs of the workload, data or application – and with some form of management simplicity across the two.
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Striking the right balance between on premises and cloud has never been more urgent. The data showed that while 74% of organizations say hybrid is their preferred model, 70% still struggle with data governance and integration, and 83% cite cost efficiency as a top priority. Regulatory scrutiny, data growth, IT complexity, and rising power requirements are converging, prompting enterprises to rebalance what they run on and off premises.
Need help striking the right balance with your hybrid cloud strategy in a world of AI? Try this.
1. Know your data and put workloads where they work best
Not sure where to start with your balancing effort? Start by aligning your predictable workloads with data on premises. Look at locating fluctuating workloads in the cloud, which adjusts quickly to changing demands. Ensure that you have the capability to seamlessly manage data across both. This can be achieved through unified data management platforms that provide consistent governance policies, orchestration tools spanning on-premises and cloud, and vendor solutions that integrate monitoring, compliance, and access controls across both environments.
Now is the time to balance your cloud utilization and on-premises activities appropriately. Doing so empowers you with the flexibility to place workloads where they run most efficiently, and the agility to scale resources quickly in response to AI demands, regulatory shifts, or business priorities. As you row forward with your AI initiatives and transformation, delaying these shifts will only make them harder.
2. Position yourself for optimal performance and advantage
Lots of data is generated internally and may be housed on premises. But simply having data won’t deliver value. You need to be able to access, analyze, and act on data while it’s fresh. As you rebalance on-premises and off-premises efforts, consider managing and putting processing closer to your data so you can run analytics more efficiently. The urgency is clear: according to the Hitachi Vantara State of Data Infrastructure Global Report, IT leaders say the amount of data they manage has nearly tripled in just two years, and they expect it to double again by 2026.
Through the power of AI and data analytics, companies can turn that data into better decisions, increased operational efficiency, risk mitigation, and competitive advantage.
3. Analyze regulations to inform your hybrid cloud strategy
AI is creating more enterprise data and driving businesses to collect and use more data. At the same time, companies are becoming much more sensitive about where their data is stored.
The growing array of data privacy and security regulations across the world is a key reason why. The Digital Operational Resilience Act (DORA) may be the most impactful such new regulation. DORA, which took effect in January 2025, includes requirements about how and where you can store your data. While this new regulation came out of the European Union, many U.S. firms – such as those that provide critical services to the EU financial services industry – must comply.
The stronger the data sovereignty requirements become, the more enterprises are moving toward on-premises environments as opposed to relying on shared public cloud environments.
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4. Choose data platforms that span across modalities
Workloads are transitioning to AI, altering the storage architectures and data access methods enterprises rely on. Block storage remains critical for transactional workloads, but AI models increasingly depend on file and object data to handle massive, unstructured datasets such as images, videos, and logs.
As these data types grow in importance, enterprises need unified visibility across block, file, and object environments to avoid data silos and ensure consistent governance and performance, a foundation that allows them to manage all data types seamlessly as AI adoption accelerates.
Build consistency in data services across the different deployment modalities. Adopt common data platforms that enable you to adapt and scale as workloads transition more toward AI.
5. Empower yourself by making the sustainability-savings connection
Power is now the governing factor if you want to do AI, which uses energy-hungry GPUs. Data centers leveraging these technologies are hitting the limit of how much power they can use.
JLL says that power availability is now “a major bottleneck” for data center deployment. Another report indicates that power constraints are extending data center construction times in the U.S. by 24 to 72 months. Deloitte adds that AI data center power demand could surge 30x by 2035. Plus, power-hungry AI and data centers have attracted the attention of regulators.
But if you adopt energy-efficient data infrastructure, you can reduce the amount of power needed for storage. That provides more head room to invest in pursuits such as analytics. To get there, seek partners with deep energy sector expertise, established sustainability leadership and sustainable-by-design hybrid cloud infrastructure, data management and AI solutions.
6. Establish data context to feed AI what it needs
Data management will become increasingly important as businesses progress with AI, so go beyond just managing the ones and zeros to understanding and managing the actual data.
Index your data. Get metadata about that data to learn where the data came from. Such context on the data will be more important than ever as your business feeds AI engines and relies on AI.
Organizations that strike the right balance with their hybrid cloud strategies will unlock performance gains and avoid the cost and complexity traps of a cloud-only approach. Beyond immediate efficiency, this balance enables enterprises to better meet regulatory requirements, improve resilience, and future proof their infrastructure as AI workloads continue to grow. Bottom line: hybrid cloud is not just a tactical choice, it is the foundation for long-term competitiveness in the AI era.
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