How AI for Automation Will Revolutionize Today's IT Workflows

Automation has been transforming industries for centuries. Johannes Gutenberg’s printing press mechanized knowledge-sharing in the 15th century. In the 1800s, the Jacquard loom used a punch card with binary code to automate intricate weaving. Later, conveyor-driven assembly lines redefined production for everything from cars to canned goods.

What may seem rudimentary today were once groundbreaking technological advancements—forever altering industrial and day-to-day human activities. Automatic doors, scheduling healthcare appointments online, traffic signals, and robotic vacuums are now so commonplace that we hardly notice their contributions.

Across industries, AI models, artificial intelligence algorithms, AI-powered tools, and AI agents are set to deliver the next evolution of historical breakthroughs in automation. Specifically, for IT, the introduction of AI for automation is reshaping workflows, minimizing manual intervention, increasing productivity, and enabling real-time, data-driven automation at scale.

This post will define AI automation, distinguish it from conventional automation, explore its practical applications in IT workflows, and highlight its significant benefits. Ultimately, you’ll see why adopting AI tools for smarter automation is essential for modern IT operations and how these solutions will accelerate transformation.

• What is AI automation?
• What’s the difference between automation and AI?
• How can AI be used for IT automation?
• Benefits of AI for automation
• Why use AI tools with automation

What is AI automation?

AI automation is a sophisticated blend of AI technologies—including machine learning (ML), large language models (LLMs), deep learning, natural language processing (NLP), and computer vision—combined with traditional preset automation rules. This intelligent approach enables automation systems to leverage the benefits of AI, such as learning from data, autonomously adapting processes, and making informed decisions without human intervention while automating.

Historically, automation relied on rigid, rule-based programming where software robots, also known as bots or scripts, executed repetitive tasks that were defined by human engineers.

However, the integration of AI with automation enables tasks to benefit from evolving learning algorithms. For example, by fusing AI with robotic process automation (RPA) in manufacturing and into general business process management (BPM) efforts, organizations can create intelligent automation that adapts to changing conditions to perform optimal actions.

AI-powered automation can independently refine its approach based on data insights, which means that over time, tasks—from data entry to complex decision making—are executed with increasing precision. This evolution is a monumental shift from static, human-scripted processes to dynamic systems capable of continuous improvement and self-optimization while enhancing the accuracy and efficiency of various automated tasks, ultimately improving the overall effectiveness of these processes.

Now that we understand the basics of what AI automation is, let’s explore how it diverges from traditional automation and the unique advantages it offers.

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What’s the difference between automation and AI?

Although the terms “automation” and “AI” are often used interchangeably, they represent distinct concepts:

• Automation: Traditional automation, such as RPA, relies on explicitly programmed instructions to perform repetitive tasks.
  
  These systems work well for predictable, rule-based operations but falter when facing unexpected scenarios or deviations from the norm. Examples of traditional automation include data entry, factory assembly lines, and email marketing campaigns.
• Artificial intelligence: On the other hand, AI provides the capacity to analyze vast amounts of data, learn from patterns, and make informed decisions without needing step-by-step human intervention.
  
  AI systems, including chatbots, image recognition, and speech recognition, continuously refine their operations using machine learning and natural language processing. This adaptability allows AI to handle variations in data and situations more effectively than traditional automation.

[Read also: Yes, ChatGPT will turbocharge hacking—and help fight it, too]

Key differences to know

• Focus: Traditional automation focuses on executing predefined tasks, while AI systems focus on learning and decision making.
• Function: Automation follows explicit rules and instructions, while AI systems learn from data and experiences to make decisions.
• Adaptability: Traditional automation lacks adaptability and struggles with variations, while AI systems exhibit greater adaptability, such as continuously improving their performance based on new information.

Understanding these differences between automation and AI is crucial, as it highlights the unique strengths each brings: Traditional automation is reliable for predictable, predefined tasks, whereas AI systems offer the ability to learn, adapt, and make informed decisions based on data.

However, while research shows that nearly all companies are investing in AI, the 2025 report Superagency in the workplace: Empowering people to unlock AI’s full potential from McKinsey determined that only 1% of surveyed leaders believe that their companies are “mature” on the AI deployment spectrum. This would indicate that AI is seamlessly embedded into their organizations’ workflows and that they are achieving meaningful performance enhancements.

To address this challenge of successfully integrating AI to drive substantial business outcomes, more and more organizations are embracing the synergy between automation and AI, which is paving the way for more advanced, autonomous systems capable of making informed decisions and adapting to new information seamlessly. By combining the best of both functionalities, organizations are unlocking new possibilities to drive innovation across various industries.

A new era of AI-driven automation brings higher levels of intelligence and adaptability to the process, enabling systems to make informed decisions and handle complex situations autonomously, and the measurable value of this integration continues to grow.

Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. We previously estimated that technology has the potential to automate half of the time employees spend working.¹

Beyond the well-known advancements in data analysis and routine task management, such as improving processes using generative AI bots like ChatGPT developed by OpenAI, Gemini from Google, Microsoft Copilot, and other AI-powered chatbots, similar systems are becoming essential for organizations looking to modernize and future-proof their IT operations.

[Listen to the free podcast: Obsessed with ChatGPT? Here’s the hype and the hope with guest Junior AI Fellow Krystal Jackson from the Center for Security and Emerging Technology at Georgetown University]

AI with automation is now tackling specific challenges faced by IT departments. Imagine a world where AI not only predicts system failures before they happen but also automatically and dynamically allocates resources to optimize performance. How about automating complex troubleshooting by diagnosing issues, suggesting solutions, and even implementing fixes without human intervention?

The transformative power of AI for automation is revolutionizing how IT teams and organizations generally view the capabilities of automation, making these tasks more efficient, proactive, and resilient.

With these differences in mind, let’s explore common use cases for combining AI with automation to amplify crucial IT functions.

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How can AI be used for IT automation?

AI integration within IT workflows enables organizations to automate many complex processes, from asset discovery to incident response, revolutionizing IT operations through AI’s ability to inform automating data-driven decisions by providing predictive capabilities that continuously improve over time.

Consider the following applications where AI is making inroads in enhancing IT automation:

• Asset discovery and inventory involves cataloging all hardware, software, and other assets connected to a corporate network. This process is crucial for organizations and supports their ability to make informed decisions about their IT assets. Today, automation plays a key role in maintaining an accurate and up-to-date inventory by streamlining these efforts.
  
  Using AI-driven insights can increase confidence in what’s being automated, allowing organizations to more easily optimize IT inventory management processes, reduce risks associated with outdated or incomplete inventory data, maintain comprehensive records of their assets to meet compliance standards, and reclaim costs and resources by identifying and eliminating unused or underutilized software using high-fidelity device data.

Learn how to achieve complete IT visibility with Tanium

• Compliance and risk management efforts include a wide range of activities, including ensuring adherence to regulatory standards and mitigating potential risks. Automation ensures that compliance management tasks are performed consistently and reliably to help organizations stay ahead of regulatory changes while also proactively preventing risks through efforts like automating patch management to reduce potential vulnerabilities and updating device configurations to maintain a robust security posture.
  
  By integrating AI into automation for compliance and risk management activities, organizations can gain valuable insights that drive more informed decision making. For example, advanced AI algorithms can inform the data analysis of both structured and unstructured data in real time to identify patterns and anomalies that might be missed by manual processes, enabling proactive risk mitigation across millions of endpoints.

[Read also: How enterprises are using AI to improve cybersecurity]

• Digital employee experience (DEX) encompasses the overall interaction and satisfaction employees have with their digital work environments, including the tools, apps, and systems they use daily. Automating the monitoring and managing of digital work environments can help organizations understand user sentiment and key areas like user usage patterns, which are critical to maintaining optimal IT performance, allowing them to improve and adjust to changing IT conditions and technologies more easily.
  
  By leveraging AI for automation into DEX efforts, organizations can allow end users to address issues through more informed, personalized self-service workflows. This level of support not only enhances employee satisfaction and productivity but also ensures operational continuity and reduces the volume of help desk tickets.
  
  Using AI systems that inform automation can also help organizations better track endpoint performance, detect bottlenecks, and predict potential issues before they impact productivity and employee experience, ensuring that digital workspaces remain efficient and reliable by constantly optimizing these experiences.

Unlock the secrets to providing a seamless digital workplace with our Ultimate Guide for Digital Employee Experience

• Incident detection and response involves identifying, managing, and mitigating cyber threats and attacks by taking steps to detect breaches, contain impacts, eradicate threats, and recover operations as quickly as possible. Automating these processes is crucial for maintaining a robust security posture, ensuring routine tasks like monitoring, alerting, and initial threat containment are performed consistently and efficiently to reduce mean-time-to-repair (MTTR) while also streamlining compliance and reporting processes needed to maintain comprehensive records of incident response activities.
  
  Incorporating AI into automated tasks powered by continuous endpoint monitoring enables real-time threat detection and incident response, improving speed, accuracy, and efficiency. Additionally, using tailored threat intelligence and predictive analytics to analyze vast amounts of data and identify patterns can effectively predict potential threats before they impact operations, allowing for more informed decision making and proactive mitigation.
  
  By utilizing AI alongside automation to execute predefined actions like killing malicious processes or quarantining compromised endpoints, organizations can also minimize the risk of attacks infiltrating supply chains and causing widespread damage by creating a more resilient and secure IT environment.

[Listen to the free podcast: How to lead a threat intelligence team with guest Microsoft Director of Threat Intelligence Strategy Sherrod DeGrippo]

These specific use cases collectively illustrate the incredible potential of combining AI-driven outputs with automation, which results in a powerful solution that empowers organizations to achieve streamlined, self-maintaining IT ecosystems that require minimal human oversight.

Now, let’s move beyond these specific examples to explore some of the broader, more tangible benefits of using AI with automation that organizations can experience across their efforts to understand better why these benefits are so impactful.

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Benefits of AI for automation

Organizations can unlock a number of benefits that transform everyday operations by integrating AI with automation. From boosting efficiency and reducing costs to enhancing the insights you gain from your data to drive other initiatives, the advantages of AI-driven automation are limitless.

Let’s delve into some of the top benefits you can expect from pairing AI insights with automation to revolutionize your organization’s efforts and why that’s the case.

Increased productivity and efficiency

Organizations that integrate AI to inform the automation of business processes are starting to see measurable gains in productivity, including making workloads more manageable and boosting creativity.

By taking over repetitive, time-consuming routine tasks, AI with automation frees up time for teams to use their human intelligence on more complex and strategic initiatives that drive business growth.

Improved resource utilization and reduced costs

By automating routine IT operations and using AI to enhance these efforts, organizations can reduce the need for manual labor, cut operational costs, and allocate resources more effectively.

For example, organizations can identify where resources are being wasted, allowing them to optimize areas like software inventory and usage to eliminate inefficiencies, which can also lead to significant cost savings.

Fewer mistakes and better quality control

IT, operations, and security teams rely on consistent and precise execution for tasks like software updates, security patching, and compliance checks.

[Read also: What to know about the risks and mitigation of unpatched software]

By leveraging AI to enhance automation, organizations can use intelligent insights to perform these functions dependably while also minimizing the risks associated with manual oversight, leading to higher standards of quality and reliability.

Enhanced data insights and streamlined workflows

AI-powered automation tools excel at processing and interpreting vast amounts of data, uncovering trends, and generating actionable insights.

AI algorithms informed by timely and accurate information can quickkly output valuable data to inform automated actions, enabling faster decision making, smoother processes, and a strategic advantage in the competitive landscape.

While it’s clear how pairing these technologies together can boost efficiency, security, and performance, let’s explore some of the fundamental reasons why organizations should use AI tools by examining how this evolution in automation transforms business operations at the most basic level and what this means for your organization.

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Why use AI tools with automation

Organizations are no longer bound by the limitations of static, rule-based automation. Instead, they can leverage predictive analytics, real-time data processing, and intelligent decision-making to create a more resilient and agile infrastructure by integrating AI tools into automation processes for unparalleled efficiency, productivity, and innovation gains in their IT workflows.

The convergence of AI and automation tools not only streamlines operations but also reduces the need for human intervention, ensuring that complex processes are handled with greater precision and reliability. Intelligent automation, where AI enhances automation, helps organizations adapt to evolving challenges by learning and finding solutions autonomously.

This holistic approach enables organizations to tackle complex tasks and make nuanced decisions by redefining how organizations automate routine operations and the human element of these activities.

Perhaps most importantly, AI-driven automation fosters innovation and growth. When mundane tasks are handled automatically, IT professionals can focus on developing new solutions, refining existing processes, and driving technological advancements for operational improvements that enhance overall business performance.

By leveraging AI-powered tools to fuel automation, organizations can achieve greater efficiency, security, and long-term success by reallocating human talent from repetitive, time-consuming tasks to high-level strategic initiatives, unlocking their full potential to propel the business forward.
 
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Tanium Autonomous Endpoint Management (AEM) exemplifies the future of AI-driven automation in IT workflows, delivering enhanced operational efficiency, robust security, and strategic agility for modern organizations.

Tanium AEM empowers organizations to automate and streamline complex processes, reduce human error, and respond to threats with unprecedented speed. Whether it’s automating asset discovery, ensuring compliance, or fortifying cybersecurity defenses, Tanium’s comprehensive platform is designed to meet the evolving needs of organizations striving to provide the best customer experiences and achieve success.

Tanium also provides a powerful API that integrates real-time data from millions of endpoints into other systems, such as Microsoft and ServiceNow. This API ensures operational health, reduces the risk of negative IT outcomes, and enhances the security of the IT environment by feeding the latest device information directly into these platforms, enabling intelligent automation.

Experience a future where every process is optimized, threats are proactively mitigated, and every decision is powered by real-time data and intelligent insights. Redefine what intelligently automating your IT operations means with Tanium AEM. Request a personalized demo today.