Artificial Intelligence: What’s the Difference between Automation and AI?

You might hear people using the terms “automation” and “artificial intelligence (AI)” interchangeably, but the two technologies are very different. While both will improve your IT organization’s services, operations, experiences, and outcomes, any IT service management (ITSM) professional looking to benefit from the technological advances being made in ITSM tools must understand the differences between automation and AI.

To help, this blog explains the fundamental differences between automation and AI, including how each technology helps ITSM and when they work well together.

Automation explained

Automation is the use of technology to perform tasks without human intervention. Importantly, automation is based on predefined rules, scripts, or workflows. It simply follows instructions. It doesn’t think, learn, or adapt beyond its programming.

A form of automation that’s commonly confused with AI is Robotic Process Automation (RPA), which uses “software robots” to mimic simple, repetitive human actions. ITSM examples of RPA use include:

• Copying and pasting data between systems
• Processing service requests
• Updating fields in your ITSM platform or tool.

So remember that this is automation, not AI, at work.

Examples of how automation helps ITSM capabilities

Automation excels at undertaking predictable, repetitive tasks. It usually replaces manual effort, time, and costs – for example:

• Automatically assigning incidents based on fixed criteria like severity, affected service, or location
• Auto-provisioning access to software when a new employee joins or an employee’s role changes
• Triggering standard change workflows, like software patching
• Automatically sending password reset instructions when an end-user requests assistance.

All these automated activities help speed up tasks and reduce the reliance on (and cost of) human staff. However, automation has limitations – because no learning is involved, it is best suited to consistent, structured, high-volume activities where variation is minimal.

AI explained

AI refers to the simulation of human intelligence processes by machines. A key differentiator from automation is that AI involves the ability to:

• Learn
• Reason
• Problem-solve
• Make decisions.

So, AI doesn’t just follow pre-programmed steps. Instead, it analyzes information, adapts to new data, and improves over time.

While the umbrella term “AI” might be used, many flavors and variants are involved in AI ITSM use cases. While knowing the ITSM use case (and how it helps) is likely more beneficial than knowing about the different AI variants, it’s worth understanding some of the employed terminology. To start, there are:

• Machine learning – algorithms that improve their performance as they are exposed to more data
• Natural Language Processing (NLP) – understanding and interpreting human language.

Then, there are three AI terms in vogue in 2025:

• Large Language Models (LLMs) – a type of AI model trained on vast amounts of text that can generate human-like text responses (e.g. Generative AI (GenAI) models such as ChatGPT)
• GenAI – AI that creates new content rather than just analyzing or processing existing data
• Agentic AI – AI systems that behave autonomously, set goals, make decisions, and adapt dynamically – acting like digital agents.

Examples of how AI helps ITSM capabilities

As already mentioned, AI goes beyond the rote task execution of automation to add intelligence to your ITSM processes. For example:

• Intelligent ticket routing – rather than using static rules, AI assesses the content and urgency of a ticket and dynamically routes it to the best-fit resolver group (as well as resolving tickets automatically when possible).
• Virtual agents and chatbots for end-users – these AI-driven bots understand the end-user’s intent to deliver contextual, relevant responses.
• Knowledge management – AI can recommend relevant knowledge base articles to end-users and agents based on natural language search queries.
• Predictive incident management – AI analyzes historical data to forecast major outages before they happen, allowing proactive intervention.

Automation vs. AI – the main differences at a glance

 Another way automation and AI capabilities are often characterized is through the terms “deterministic” and “non-deterministic,” respectively. Deterministic is where a process always produces the same output for a given input, while a non-deterministic algorithm can produce different outputs for the same input.

Examples of automation and AI working together well

The title of the previous section, “Automation vs. AI,” is a little misleading because the two technologies and their ITSM opportunities aren’t mutually exclusive. Instead, they can work together to achieve the required IT operations and business outcomes. For example:

• Virtual agent escalation – a virtual agent will use NLP to understand a complex end-user request. However, if the issue can’t be solved automatically, it triggers a workflow to create and route a ticket to the correct support group.
• Predictive maintenance – an AI model detects a pattern indicating a potential server failure. Once identified, an automated script patches the server or spins up a replacement virtual machine.
• Intelligent service request fulfillment – AI predicts what software a new employee will need based on historical usage patterns and user role. Automation automatically provisions the necessary applications and permissions on the employee’s first day.

Combining automation and AI allows your IT organization to benefit from both technologies. Automation boosts operational efficiency and consistency, usually by eliminating manual work for routine tasks. While AI brings intelligence and adaptability. It’s important to use each technology where it excels. It’s a little like the adage of not using a screwdriver to hammer in nails.

To learn more how AI will help your IT operations and outcomes, check out these resources:

Blog Post: The Human Touch in AI Adoption

Blog Post: The Skillsets IT Service Desk Agents and Managers Need in the Age of AI

Blog Post: AI Driven ITSM: Pioneering Workload Management for the Future

Blog Post: Microsoft Copilot: The AI and Automation Opportunity for ITSM

Whitepaper: Cloud Lighthouse Crafting Your Future-Ready Enterprise AI Strategy