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The Rise of AI Deployment Beyond Cloud Environments

In a world rapidly shifting towards data-driven decision-making and artificial intelligence (AI) integration, Actian has shed light on a crucial trend through its recent study conducted by BARC. The findings indicate a significant transformation in how organizations view AI workload deployment, particularly highlighting the growing importance of operating outside traditional cloud environments.

Key Findings from the Research

On October 22, 2026, Actian, the data and AI division of HCLSoftware, released the study titled Where Enterprise AI Runs: Deliberate workload placement across cloud, on-premises, and edge. One of the standout statistics from this research shows that a remarkable 76% of organizations believe that deploying AI applications outside of cloud setups has become increasingly vital in the past year.

Historically, cloud computing has dominated the landscape for AI workloads, with 77% of organizations utilizing cloud resources for at least some of their AI processes. However, as AI technology matures, companies face mounting pressures around data sensitivity, security, and compliance, prompting them to rethink their strategies. With critical functions increasingly moving beyond mere experimentation, organizations must ensure that they maintain control, particularly regarding sensitive data protection and strict adherence to regulatory requirements.

Drivers Behind the Shift

The new research illuminates various factors that are influencing organizations to deploy AI workloads in environments beyond the cloud:

1. Internal Motivations: Leading concerns include data sensitivity (55%) and mission-critical AI processes (49%). Organizations are prioritizing these elements to minimize risks associated with data breaches and to ensure compliance with stringent data regulations.

2. External Pressures: Many organizations are experiencing increased cybersecurity fears (45%), coupled with the demands of customers, partners, or contract obligations (45%), which necessitate heightened scrutiny of how data and AI applications are managed. Dependency on specific providers (42%) also plays a significant role in these concerns.

The benefits of adopting a multi-environment approach are clear. The study found that improved data privacy and confidentiality were cited by 62% of respondents, whereas 40% recognized a stronger stance on regulatory compliance as an advantage. Additionally, 37% noted cost optimization, highlighting the financial benefit of such strategic shifts.

The Role of Hybrid Architectures

Interestingly, the research indicates that organizations with hybrid architectures—those that integrate AI workloads across diverse environments—boast a notably higher AI maturity level. A substantial 65% of these companies reported having AI initiatives in production or generating measurable value from AI technologies, compared to 60% of organizations that rely solely on cloud infrastructures.

Understanding Data and AI Sovereignty

At the heart of these deployment decisions is the concept of data and AI sovereignty. This principle pertains to organizations' ability to control, enforce, and demonstrate the usage of their data and AI models across various platforms. Actian identifies three critical dimensions of sovereignty:

• - Control: Organizations must be able to define access protocols to their data and models, specifying who can access information and under what circumstances.
• - Enforcement: It is not sufficient to have policies; organizations must actively apply these rules in practice rather than just having them documented.
• - Proof: The ability to provide verifiable evidence that the established rules were indeed applied is essential for organizational governance.

Adapting AI deployment strategies necessitates careful consideration of these sovereignty factors across all computing environments. To this end, machine-readable data contracts become instrumental, facilitating consistent application of policies regardless of where data is processed.

Looking Ahead

Emma McGrattan, Chief Technology Officer of Actian, underscored the significance of the research findings by emphasizing that organizations are increasingly meticulous about their AI operational strategies. The challenge lies not just in moving workloads beyond the cloud; true sovereignty is about enhancing governance and policy adherence across different environments, particularly as AI systems evolve toward greater autonomy.

The data collected for this study came from a survey of 329 respondents involved in various leadership roles across industries such as manufacturing, IT, financial services, and healthcare in regions spanning Europe, North America, and Asia-Pacific. Given the escalating complexities of data management and security, the insights gained from this research could play a pivotal role in guiding organizations as they adapt to the ever-evolving landscape of AI deployment.

Conclusion

As enterprises navigate these transformative dynamics in technology and data governance, Actian's research not just describes a trend but also presents a roadmap for understanding the strategic necessities of AI deployment in our increasingly complex digital world.