Automation in the AI Era: Enabling Systems to Manage Themselves

In past articles for this quarterly collaboration, I have dived into the topic of artificial intelligence: its potential, the main challenges and opportunities facing companies that operate in Mexico when it comes to this technology, as well as the different strategies for local leaders to begin adopting and implementing to generate use cases and transform their business. 

This time, I would like to jump one step ahead, toward another essential technology for our era: automation.

To do that, I want to describe a scenario that leaders in our sector might be familiar with: Imagine it’s 3 a.m. when the IT leader of your company calls you to confirm one of the worst nightmares in business: “The main system is down, and the reason is unknown.” At that moment, what matters most is getting the system up as fast as possible because business continuity depends on this. 

The foundation on which companies are built today is technology: old and new. But multiple software, applications, clouds, hardware, networks, integrations, and other components, have created a complexity that is hard to manage. To explain this, let’s take applications as an example. 

According to the CDP Institute, companies today use an average of 1,000-plus applications. If you were managing a restaurant, that would be like coordinating over 1,000 chefs simultaneously, each one of them specialized in a specific type of cuisine, with their own recipes. How do you manage a kitchen with 1,000-plus chefs, their techniques, interaction, dishes, quality, or even make sure that resources and ingredients are used effectively? That would be a logistical nightmare.

Tech outages can happen for many reasons, some are the result of technological and/or human error, but both can have a significant impact on businesses. The numbers tell the story: According to a recent Siemens study on predictive maintenance and reliability (2024), the largest 500 companies in the world by revenue still lose US$1.4 trillion annually to unplanned downtime. An hour of downtime costs an average of US$250,000, according to a 2023 IDC study, and every second of application latency results in a 16% reduction in customer satisfaction.

Turning the Black Box of Systems Into Transparency

What if systems could talk? What if they could point out where it hurts before collapsing? Well, this is exactly what AI-driven observability promises: turning mute systems into "patients" that can describe their symptoms before a crisis arrives. How? It helps to discover and address the technology “unknown unknowns” (the technology issues that teams don't even know exist), identify and resolve issues early in development, and thus, improve the user experience. 

But that’s not all. It also helps systems to scale automatically, automate remediation, and has the ability to self-heal the application infrastructure. With it, the technology teams can evolve from being technological firefighters to architects of systems that take care of themselves and drive business value. 

This explains why 92% of executives globally are leveraging AI-powered automation in the life cycle of their technology stack in 2025 (IBM Institute for Business Value). And it's not just statistics: real-world implementations demonstrate observability’s potential.

Since 1934, the Masters Tournament has been home to some of golf's greatest moments, and for nearly 30 years, IBM has been their partner in transforming data into world-class digital experiences for fans. To achieve this, the Masters created a platform built on a hybrid cloud infrastructure. To ensure that digital platform delivers a seamless user experience, critical applications are monitored by an AI-powered observability solution called IBM Instana, helping the Tournament proactively solve issues across their application stack.

Now, let's talk business.

According to a Forrester study, AI-powered observability can help companies reduce revenue-impacting incidents by up to 60%, achieve productivity improvements up to 40%, reduce mean time to repair by 70%, and decrease development troubleshooting time by up to 90%.

Could this be the next frontier for AI? Perhaps not for AI, but certainly for companies. Those implementing observability are at an inflection point known as AI+: they are reinventing themselves around AI's opportunities by combining it with IT automation.

Imagine a Different 3 a.m. Scenario

Instead of a panicked call about system failure, you receive a brief notification that an issue was detected, diagnosed, and resolved automatically, all while you slept peacefully.

This transformation isn't just about preventing downtime, it's about gaining a competitive advantage. While some organizations remain trapped in reactive firefighting, those embracing AI-driven observability are building resilient, self-managing infrastructures that adapt and evolve continuously with the business.

The question isn't whether this technology will reshape operations, but whether leaders will embrace or follow this transformation. The companies defining tomorrow's digital landscape aren't just implementing new solutions, they are fundamentally reimagining how technology serves their business objectives.

The integration of automation into an era of artificial intelligence is not only relevant because it helps companies accelerate their transformation, simplify their operation and integrate their data to their day-to-day activities, but also because it is an essential element of managing technological complexity, which is only poised to increase, unless leaders implement the systems and technological tools that can help them seamlessly fold technology into companies’ operations.