01: The rise (again) of 
IT Automation
Every business is a technology business now
isometric illustration of a person in front of a server with purple bubbles coming out of it
Let’s start with some terms

Understanding IT automation and AI first requires getting on the same page so that all stakeholders are talking about the same concepts. Doing that means understanding some key terms, exploring the changes created by generative AI, and digging into the role of the C-suite. It is also vital to dig into some use cases that showcase what automation and AI can do for IT operations.

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This term encompasses the big picture of making business processes move quicker and more efficiently with less human intervention.

IT automation

This is the process of taking manual actions and finding a way to use a system or tool to perform them instead of a human being.


This term, coined by Gartner in 2018, is the application of AI capabilities, such as natural language processing and machine learning (ML) models, to automate and streamline operational workflows.

Generative AI (Gen AI)

This term refers to deep-learning models that can generate high-quality text, images and other content based on the data on which they were trained.

Watch: Generative models explained (08:30)
With digital transformation, you're rarely simplifying things. Stephen Mortefolio Vice President Product Marketing, IBM Automation
Generative AI

Gen AI is sparking dramatically new approaches to how we work, including traditional IT processes. Three out of four CEOs say their competitive advantage rests on gen AI, and transformation is still very much a priority for most organizations, too. According to McKinsey research, 90% of companies have launched some flavor of digital transformation. That digitization makes every business—whether it’s ready or not—a technology business now.

“With digital transformation, you're rarely simplifying things. You're most often adding new and more complexity—more systems, more applications. In the past, as complexity grew, our teams would grow, too, e.g., SREs, developers, teams monitoring your cloud operations. But that doesn't scale in the long run. Now, organizations have to look for new ways to improve experiences and the productivity of those teams.” —Stephen Mortefolio

The role of the C-suite

If every company is now a technology company, then it follows that all C-suite executives need to become better-skilled technologists. For many companies, AI and automation are expanding from a C-suite focus to a board-level one, too.

With such high-level scrutiny, the pressure to adopt gen AI and other AI-infused technologies is stronger than ever. This burden also matches the constant pressure for IT operations to perform 24x7 while IT teams simultaneously deliver new features, keep customers happy and loyal, and ensure costs are as low as possible.

“If you're not providing the proper service to whoever your constituent or customer is, it doesn't matter if you're in public sector, the telecom industry or manufacturing. Ultimately, if that service isn't good and you're not delivering the capabilities they need, the rest of it doesn't really matter, because the customer is gone.” —Melissa Long Dolson, Vice President, AI Ops & Integration, IBM Technology Sales



Expectations are so high that even one bad experience can lead to someone leaving a brand, leaving a product, or leaving a service Keri Olson Vice President, Product Management IT Automation Software

What CIOs know is that these pressures are about trying to answer one question: “Am I getting the most—the most value, the most productivity, the most return—from my technology investments?” Fortunately, AI-powered IT is well-positioned to make “yes” the answer.

“We're certainly expecting more from these investments. Even in our personal lives, we expect more from the machine than we do from an actual person. Expectations are so high that even one bad experience can lead to someone leaving a brand, leaving a product, or leaving a service. That’s why it's more important than ever that applications continuously perform at a high level.” —Keri Olson

Fact: Among the organizations surveyed, 60% invest in automation to decrease IT and network complexity. And 50% invest to deliver new and improved IT platforms and applications.¹

Use cases

With AI and AIOps solutions, IT teams and the businesses they serve move from a “break-fix” model to one that enables preventive and predictive actions. Teams gain actionable insights and new ways to uncover productivity-enhancing, time-saving and cost-saving efficiencies at a scale that simply can’t be matched by humans. For example, consider how these use cases could potentially boost your IT operations and development:

Strengthen end-to-end system resilience

Use real-time root cause analysis capabilities—powered by AI and intelligent automation—to swiftly identify the underlying causes of incidents and then take immediate action to reduce both mean time to detect (MTTD) and mean time to resolve (MTTR).

Operationalize FinOps and optimize cloud costs

Apply a FinOps cloud financial management framework so that cross-functional teams can work together and take ownership of cloud usage. FinOps is a management practice that organizations use to optimize the financial performance of their cloud computing infrastructure. Using data-driven cloud spend decisions to safely balance cost and performance you can let software—not people—take appropriate actions and give applications the resources they need when they need them.

Improve CI/CD pipelines

Employ observability, powered by AI and automation, for full-stack visibility to better understand your environment and speed up innovation. You’ll also have automatic discovery, monitoring, and validation of the performance and integrity of applications in production. This includes your cloud infrastructure, virtual machines, container-based microservices, shared multi-tenant infrastructures, and storage systems—all reporting on metrics such as usage, availability and response times.

Redesign data integration

Use gen AI to reduce the time needed to connect applications and systems and unlock critical data.

Enhance code generation

Accelerate code generation and increase developer productivity using high-quality, accurate code with AI recommendations based on natural language requests or existing source code. Enable development teams to become proficient in different programming languages without the need for massive upskilling.

Chapter 02 →
Moving IT teams from cost center to collaborator with AI automation
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1 The ESG data conundrum, IBM Institute for Business Value, April 2023