The Future of Workload Automation: Key Insights from EMA's Latest Study

One of the key findings of the study is the shift toward enterprise-wide orchestration. Organizations are moving beyond traditional job scheduling to a more holistic orchestration model, where automation spans across IT, business processes, and multi-cloud environments. This broader mindset enables organizations to enhance operational efficiency and resilience, ensuring seamless integration between disparate systems and workflows.

Businesses are rethinking their automation strategies to incorporate more sophisticated orchestration models. This means extending automation beyond IT operations to touch every part of an organization, ultimately improving end-to-end business processes.

Observability has emerged as a crucial component of AI-driven orchestration. The ability to monitor, analyze, and respond to system behaviors in real-time is essential for effective AI integration. By leveraging observability data, organizations can optimize their workload orchestration, enhance predictive analytics, and improve decision-making capabilities.

With increasing system complexity and the adoption of hybrid and multi-cloud environments, organizations are investing heavily in observability platforms. This. allows AI-driven automation solutions to proactively detect and resolve issues, ensuring optimal performance and compliance.

AI adoption in workload automation is expanding rapidly. Organizations are leveraging AI to reduce training barriers, enhance self-healing capabilities, and improve automation governance. According to EMA’s study, AI is playing a pivotal role in enabling workload automation systems to:

• Analyze and optimize workflows in real-time

• Improve resource allocation and system efficiency

• Enhance security and compliance through intelligent monitoring

• Reduce human intervention in routine operations

A key aspect of AI adoption is its ability to support self-service automation and citizen developers. While the concept of citizen developers is still in its early stages, AI is expected to bridge the gap between IT and non-technical users, empowering more individuals to contribute to automation initiatives.

Despite the focus on AI and orchestration, cloud and multi-cloud integration continue to be top priorities for organizations. Workload automation tools are evolving to better support hybrid and multi-cloud environments, ensuring seamless workload distribution across cloud providers like AWS, Azure, and Google Cloud.

Observability plays a significant role in cloud integration, providing the visibility needed to manage workloads efficiently across multiple environments. Organizations that embrace automation within their cloud strategies are better positioned to improve agility, scalability, and cost optimization.

Beyond IT automation, organizations are increasingly aligning workload automation initiatives with broader business objectives. End-to-end business process automation is becoming a strategic priority, enabling enterprises to streamline operations, reduce costs, and enhance customer experience.

A growing trend in this area is the rise of citizen developers—business users who contribute to automation initiatives using low-code and no-code platforms. While EMA’s study indicates that only a small percentage of organizations have fully embraced citizen development, AI-driven automation tools are expected to make it easier for non-technical users to participate in the automation process.

Despite the enthusiasm for AI-driven workload automation, organizations face several challenges in adoption:

• Skill and resource shortages: Finding qualified personnel to implement and manage AI-driven automation is a significant hurdle.

• Data quality and availability: AI requires high-quality observability data to function effectively.

• Security and compliance concerns: Ensuring that AI-driven orchestration adheres to regulatory requirements remains a top priority.

• Integration complexities: Organizations need to integrate AI-driven automation solutions with existing IT environments and business processes.

To overcome these challenges, organizations are focusing on upskilling their workforce, investing in AI-driven observability tools, and refining their automation strategies.

EMA’s study reveals that 80% of organizations are transitioning from traditional workload automation to enterprise-wide orchestration. This shift requires a strategic approach, with a focus on:

• Implementing predictive analytics to anticipate workload performance issues

• Enhancing observability to gain deeper insights into system behavior

• Integrating AI-driven recommendations to optimize workload execution

• Expanding automation beyond IT operations to include business process orchestration

For organizations just beginning their journey toward AI-driven orchestration, EMA recommends the following steps:

1. Strengthen Observability Capabilities: Invest in observability platforms to collect and analyze real-time data from IT environments.

2. Leverage AI for Predictive Analytics: Use AI-driven insights to optimize workload execution and reduce manual intervention.

3. Expand Automation Scope: Move beyond IT-focused automation to integrate business processes, ensuring end-to-end orchestration.

4. Enable Self-Service and Citizen Development: Leverage AI-driven tools to empower business users to contribute to automation initiatives.

5. Enhance Security and Compliance Measures: Ensure that AI-driven orchestration aligns with regulatory requirements and organizational policies.

Looking ahead, EMA predicts that AI will play an increasingly critical role in workload automation and orchestration. Organizations that embrace AI-driven automation will gain a competitive advantage by improving efficiency, reducing costs, and enhancing business agility.

The study highlights that 83% of respondents see AI as a key enabler for identifying automation issues and optimizing workflows. Additionally, 91% consider AI-driven orchestration essential for achieving digital transformation goals.

The findings from EMA’s study underscore the growing importance of AI-driven workload automation and orchestration in modern IT environments. Organizations that prioritize observability, embrace AI-driven automation, and expand orchestration beyond IT operations will be best positioned for success in the digital era.

As workload automation continues to evolve, Beta Systems Software AG and other industry leaders remain at the forefront of innovation, helping organizations navigate the complexities of modern automation. The shift from traditional WLA to enterprise-wide orchestration is not just a trend—it’s a strategic necessity for businesses aiming to thrive in an increasingly automated world.

For a deeper dive into these insights, the full EMA research report is available through Beta Systems Software AG and other study sponsors.

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