In today’s enterprise environment, the push for IT automation has become the norm. However, IT leaders face obstacles in finding the best ways to implement automation on a large scale. Additionally, they must grapple with the impact of emerging technologies like generative AI on automation.Bottom of Form
IT automation is a broad, all-encompassing effort that involves networking, security, operations, business processes, cloud computing, software development, and so much more.
In this archived keynote session, Eve Logunova-Parker, the founder and CEO at Evenness, explains the importance of adopting automation practices across various aspects of business operations. She highlights the need to minimize repetitive tasks, eliminate human errors, accelerate response times, and improve productivity through comprehensive automation implementation.
This segment was part of our live virtual event titled, ‘Strategies for Maximizing IT Automation.’ The event was presented by ITPro Today and InformationWeek on March 28, 2024.
A transcript of the video follows below. Minor edits have been made for clarity.
Transcript:
Eve Logunova-Parker: From its humble beginnings of basic scripting and batch processing to the sophisticated orchestration and AI-driven automation we see today, the journey has been one of continuous innovation and evolution.
Advancements in technology, coupled with changing business needs, have propelled IT automation forward, making it more accessible, scalable, and intelligent than ever before. What started as a means to simplify tasks has now become a strategic imperative for organizations seeking to stay ahead in the competitive landscape.
Let's explore the key milestones in the evolution of IT automation. The first one mentioned on this slide is the emergence of scripting languages that happened between 1950 and 1960. In the early days of computing, programmers developed scripts to automate repetitive tasks such as batch processing and job scheduling.
Languages like COBOL, Fortran, and Shell scripting played pivotal roles in automating routine operations on mainframe computers. In the 1960s, we also saw the introduction of job control language (JCL). IBM introduced JCL to automate batch processing on their mainframe systems.
JCL allowed users to define and execute sequences of jobs while automating tasks such as data processing and report generation. Then, in the 1970s and 1980s, we saw mainframe automation tools appearing in the market. With a proliferation of mainframe systems in large enterprises, specialized automation tools emerged to streamline operations. Tools like IBM's job-entry subsystem, known as Control-M and CA Workload Automation, helped automate job scheduling, resource management, and workload optimization.
Then, it was the fantastic client-server era in the 1980s and 1990s. The advent of client-server architecture brought new challenges and opportunities for automation technologies like network management systems, database management systems, and enterprise resource planning software, or ERP as you may know it.
This introduced automation capabilities for managing distributed computing environments. For servers, we saw the rise of configuration management tools. The complexity of IT environments grew with the adoption of distributed systems, virtualization, and cloud computing.
Configuration management tools like Puppet, Chef, and Ansible emerged to automate the provisioning, configuration, and management of servers and infrastructure at scale. There was the DevOps movement that emphasized collaboration, automation, and integration between development and operations teams.
Continuous integration and deployment pipelines became central to automating the software delivery process, which enabled faster and more reliable releases. Then, we saw the introduction of cloud orchestration platforms in the 2010s.
With the rise of cloud computing, orchestration platforms like Kubernetes emerged to automate the deployment, scaling, and management of containerized applications. These platforms provided a standardized approach to managing distributed microservices-based architectures in cloud environments.
During the same period, we also saw the integration of AI and machine learning. AI and machine learning technologies are increasingly being integrated into IT and automation solutions since the 2010s.
These technologies enable predictive analytics, intelligent decision-making, and autonomous remediation, which further enhance the efficiency and effectiveness of our automation process. In recent years, the development of large language models by organizations like OpenAI, Microsoft, Google, and various startups has revolutionized the field of IT automation.
These models leverage advanced natural language processing techniques to automate tasks such as text generation, summarization, and translation, which offers new possibilities for streamlining communication and decision-making processes in IT environments.
Watch the archived ‘Strategies for Maximizing IT Automation’ live virtual event on-demand today.
