Agentic AI Takes the Lead in Cloud Operations
▼ Summary
– Most organizations are in testing or early deployment of agentic AI, with nearly one quarter scaling it across business functions, primarily for employee productivity and cloud management.
– Key concerns limiting safe scaling include limited visibility into data and AI workload locations, cloud security, provider concentration, and regulatory uncertainty.
– Confidence in data architecture and automation has increased, with 90% of organizations using automation to optimize IT environments, though a shortage of skilled workers remains a top issue.
– Application modernization focuses on enabling AI and automation, but integration with legacy systems and technical debt remain leading challenges.
– Nearly half of organizations experienced a cybersecurity breach in the prior year, prompting the use of security controls like identity governance and access management to guide agentic AI deployment.
Companies are turning to agentic AI to handle the growing complexity of application environments, automating routine tasks and supporting critical decisions. According to the Unisys AI & Cloud Insights Report, business and IT leaders increasingly view this technology as a core component of cloud application management.
Most organizations are still in the testing or early deployment phase, but nearly a quarter have begun scaling agentic AI across business functions. Early use cases focus on boosting employee productivity and streamlining cloud management. Spending plans reflect sustained interest, with half of respondents expecting to increase investment over the next year.
Successful deployment hinges on preparation. Companies have made progress in identifying priority use cases, securing executive support, training employees, collaborating with technology vendors, and establishing governance rules. These foundational efforts enable broader agentic AI adoption across departments and workflows.
“The organizations winning right now have figured out how to turn what they already have into outcomes their boards can see and measure. The technology itself matters less than the discipline to act on it,” said Mike Thomson, CEO and President at Unisys.
Day-to-day control remains the primary concern. Organizations cite limited visibility into data and AI workload locations, cloud security, provider concentration, and regulatory uncertainty. Business leaders express higher concern across all measured risk categories.
Concerns around trust and employment have diminished. Fewer respondents now associate agentic AI with job displacement, bias, hallucinations, or limited human empathy. As experience with the technology grows, attention has shifted toward deployment rules, workload oversight, and access controls.
Infrastructure supports wider AI use
Confidence in data architecture and automation has risen. Most business and IT leaders report having the architecture and tools needed to support data-driven decisions. Nine in ten organizations say they are using automation to streamline and optimize IT environments.
A smaller share of organizations than in the previous survey report operational performance exceeding expectations. A shortage of skilled workers, along with data management and integration issues, remain the leading IT challenges.
Investment plans remain active across cloud infrastructure, cloud applications, automation, generative AI, and zero trust. Organizations are expanding their use of public and private cloud services. Edge, sovereign, and industry cloud environments are gaining traction in sectors with specific data, location, and compliance requirements.
Expanding cloud environments add management demands. Each platform can introduce separate controls, data locations, service dependencies, and access rules. Companies need consistent oversight across these environments to support cloud operations and AI workloads.
Application work stays focused on technology
Application modernization efforts center on technical upgrades and AI support. Enabling AI and automation ranks near the top of stated objectives, followed by resilience, cost reduction, customer experience, and speed to market.
Integration with existing systems remains the leading application strategy challenge. Legacy systems, technical debt, and ongoing optimization work also slow projects. Many companies are moving older applications to cloud environments and adapting them for cloud-native use.
Keeping older systems in operation leaves services spread across legacy, cloud, and hybrid environments. This creates dependencies and complicates application management. Retiring more legacy applications could help reduce that complexity.
Security controls guide AI deployment
Agentic AI gives software systems access to enterprise data, applications, and business processes. This increases the need for identity controls, access management, workload visibility, and defined limits on system authority.
Nearly half of surveyed organizations experienced a cybersecurity breach in the prior year. They are using managed detection and response, identity governance, privileged access management, and continuous threat exposure management to support security operations.
Cloud security capabilities influence how much autonomy organizations assign to AI systems. Data sovereignty requirements affect workload locations, with regional deployment serving as a common response.
Security and governance controls are now integrated into AI planning from the start. These controls set rules for data access, system authority, workload placement, and accountability. Respondents expect these controls to limit agentic AI deployment in the near term, but the same controls provide the operating boundaries needed for wider use across business functions.
(Source: Help Net Security)