Building the Foundations for Intelligent Operations: Inside the Updated AIOps Foundation and Observability Foundation Certifications
Marc Hornbeek, CEO and Principal Consultant at Engineering DevOps Consulting and DEVOPS INSTITUTE certification author
Marc Hornbeek brings deep expertise across Continuous Testing, DevOps, DevSecOps, SRE, Security and AI-driven practices to digital transformation programs around the world. In this article, he examines how the DEVOPS INSTITUTE’s refreshed AIOps Foundation and Observability Foundation certifications respond to a new era of intelligent operations. Drawing on his hands-on experience with automation, reliability and AI-assisted decision-making, he explains how emerging trends, including Generative AI, DataOps and full-stack observability, are redefining what modern teams need to deliver resilience, performance and continuous value at scale.
As artificial intelligence and automation continue to reshape the IT operations landscape, organizations are re-evaluating what “operational excellence” really means. It’s no longer just about tools and dashboards; it now depends on interpreting complex data, integrating intelligence into workflows and supporting more proactive decision-making.
This shift is driving a noticeable change in the skills that employers now seek. Organizations increasingly value professionals who understand the underlying principles behind AI, automation and observability practices rather than simply knowing how to operate specific platforms. They seek individuals who can facilitate communication between technical and business teams, interpret the implications of AI-driven insights and support a cultural transition toward more data-informed and anticipatory operations.
The DEVOPS INSTITUTE’s AIOps Foundation and Observability Foundation certifications have been updated in direct response to these changing expectations. Both certifications help learners develop conceptual understanding and confidence, enabling them to explain how AIOps, Observability and DevSecOps fit together within an organization’s strategy for resilience, performance and security.
Even at this foundational level, participants become better equipped to contribute meaningfully to discussions and transformation initiatives, something employers increasingly value across modern digital teams.
Shifting trends and expectations
The updates reflect broader shifts in the IT operations landscape, particularly the growing influence of AI, automation and data-driven decision-making within digital enterprises. As organizations adopt cloud-native architectures, microservices and hybrid environments, the volume and complexity of operational data have exceeded the capabilities of traditional monitoring and analytics approaches.
Recent advancements, such as large language models (LLMs) and Generative AI, are expanding what’s now possible in AIOps by enabling systems to interpret data, summarize insights and support root-cause analysis. While the refreshed certifications remain foundational, they help learners understand these emerging capabilities and appreciate how AI, machine learning and data quality principles contribute to more intelligent, proactive and explainable operations. These industry shifts are also reshaping how core operational disciplines interact.
The growing convergence of Observability and AIOps
A key focus of the updated curriculum is the growing convergence of Observability and AIOps. These two disciplines are increasingly interconnected because they address the same challenge: understanding complex, fast-moving systems through the data they generate.
Observability provides the essential visibility needed to understand system behavior, while AIOps applies analytics and AI techniques to interpret that data at scale. Together, they enhance reliability, performance and security by supporting faster detection, diagnosis and prevention of issues.
The certifications help learners see how Observability generates insights and how AIOps enhances how teams interpret and act on them, enabling more resilient operations across distributed environments.
From data analysis to AI-assisted operations
The update of AIOps Foundation reflects the evolution of the discipline from traditional data analysis to AI-assisted operations. The primary reasons for the update include the rapid advancement of AI technologies, the increasing complexity of cloud-native systems and the growing role of automation and Generative AI in IT operations.
Big Data and Machine Learning remain the conceptual foundation because they continue to serve as the building blocks of AIOps. AIOps Foundation then introduces automation and Generative AI as natural extensions, helping learners understand how newer capabilities enhance familiar objectives such as faster insights, improved collaboration, and more intelligent decision-making.
This balance ensures participants gain clarity on how AIOps is progressing while remaining grounded in the enduring principles of data, learning and continuous improvement.
How AIOps, DevOps and SRE work together
A related theme explored in the updated material is how AIOps, DevOps and Site Reliability Engineering (SRE) complement one another. DevOps emphasizes collaboration and flow across development and operations, while SRE focuses on reliability through measurement and automation. AIOps adds intelligence and scalability through data analytics and machine learning. AIOps Foundation uses real-world examples and shared terminology, including service levels and DORA indicators, to help learners understand how these disciplines reinforce one another within a modern, AI-enabled operational culture.
Evolving observability for cloud-native complexity
Observability Foundation has also been refreshed to address the demands of cloud-native complexity. Modern systems, particularly those built on microservices, are significantly more dynamic and distributed than traditional monolithic architectures. As a result, organizations require visibility across every layer of the technology stack, from infrastructure and containers to networks, services and the user experience.
This update helps learners grasp how telemetry from metrics, events, logs and traces must be collected and correlated to understand how changes in one component affect the system as a whole. It also introduces how AI and automation enhance this visibility by helping teams interpret complex data relationships.
The aim is to ensure learners appreciate why observability must be built in from the start and how it supports resilience, security and reliability in cloud-native environments.
Turning concepts into real-world practice
Both certifications now incorporate scenario-based discussions and guided exercises that help learners relate key ideas to realistic organizational challenges. In AIOps Foundation, a scenario might describe a company struggling with alert fatigue and fragmented monitoring tools. Learners explore how an AIOps approach, using data correlation, automation and AI-driven insights, can enhance visibility and response times.
In Observability Foundation, a scenario may involve diagnosing performance issues in a microservices-based application, helping participants understand how various telemetry signals combine to reveal dependencies and inform resilience improvements.
These exercises focus less on tool operation and more on helping learners understand how AIOps and Observability principles lead to better collaboration, faster problem detection and more informed decision-making. These practical exercises also reinforce an equally important element — the human and cultural side of modern operations.
Bridging technical skills and organizational culture
Beyond certification, the most valuable takeaway is a shared understanding of how AI, automation and data-driven insights integrate with modern DevSecOps culture. Participants develop a common language regarding data, reliability, AI-supported decision-making and gain insights into how these concepts influence roles, workflows and shared goals such as performance, resilience and security. The certifications help bridge the gap between technical knowledge and organizational culture by connecting technical concepts to real business value.
Looking ahead, the next generation of AIOps and Observability education will be shaped by the continued evolution of Generative AI, intelligent automation and autonomous operations. Concepts such as AI agents, natural language interfaces, explainable AI, DataOps and security observability will become increasingly important to ensure trust and transparency.
For organizations beginning this journey, the advice is clear: prioritize culture and shared understanding before tools. Once foundational knowledge is in place, it becomes far easier to adopt advanced technologies confidently and sustainably.
The future will combine human judgment and AI-driven insight, and organizations that invest in foundational education now will be best prepared to thrive in this hybrid environment.
Learn more about the DEVOPS INSTITUTE’s AIOps Foundation and Observability Foundation certification here