Defining AI and automation
Artificial intelligence (AI) acquires knowledge and applies informed reasoning to make dynamic decisions, while automation performs repetitive IT tasks and processes. Although AI and automation both promise to reduce manual labor, they aren’t inherently the same and serve 2 distinct functions.
AI acquires knowledge and processes vast amounts of data–more than most humans reasonably can on their own. From this data, AI can pull key insights and make dynamic decisions. Its capabilities can cover something as basic as making a weather prediction to determine if the next few days will be optimal for surfing or as complex as creating a multitiered remediation protocol to guard against a potential security breach.
Automation performs repetitive IT tasks and processes according to manually set guidelines. It consistently and reliably does what it's told. Even when obstacles come up, automation can be programmed to handle these instances on its own without needing further human assistance. Automation can be trusted to do things like run a daily data backup or send updates to thousands of machines on a schedule and address small problems if they come up.
When it comes to enterprise IT, these roles are distinct and important: Automation is the machine that executes tasks based on predefined rules, while AI is the brain power that learns and adapts to make non-rule-based decisions about complex problems. Understanding the difference between AI and automation can help you position your business to work smarter and achieve more strategic outcomes using a combination of insights and planned guidelines from both.
What is AI?
AI describes systems capable of acquiring knowledge and applying insights to solve problems. Like human intelligence, AI constantly interprets the environment and makes real-time decisions that improve the output. AI doesn’t follow a script—it writes the script in real time. While traditional automation uses repeating logic to perform the same action given the same input, AI is built on probable inference. It chooses the best possible action based on patterns and context, even when the input isn’t identical.
Primary characteristics of AI include:
- Nondeterministic: AI generates results based on statistical probability and learned patterns. The output can change based on new data or an evolved model, even if the initial prompt is similar.
- Learning-based systems: Machine learning (ML) and deep learning identify complex patterns within datacenters and improve their performance over time without direct human reprogramming.
- Predictive: AI excels at tasks that require predictive analytics, natural language processing (NLP), and classification.
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