by AI automation defined
AI automation uses advanced technology to manage tasks and processes by programming computer systems to review data, recognise patterns, and make logical choices. It can take over repetitive or time-consuming work that would otherwise require human effort — whether it’s simple data entry and customer invoicing or complex inventory management and dynamic pricing. Shifting these duties to AI agents gives people more time to focus on more highly valued work.
While computers are not yet capable of abstract reasoning or making moral judgements, agentic AI is technology that can be trained to mimic human decision-making and take autonomous actions. Unlike traditional automation, which follows a fixed set of rules to repetitively perform tasks, AI automation allows systems to change and improve over time. With reinforcement learning or model retraining with human-in-the-loop (HITL) feedback, agentic AI can learn from experience and adjust its actions to deliver more relevant results.
AI automation uses both machine learning and natural language processing (NLP), which is able to understand and respond to human language, analyse large swaths of datasets, and make intelligent decisions. Machine learning (ML) provides AI with the ability to analyse data and then recognise and predict patterns so it can make decisions based on historical data.
The introduction of large language models (LLMs) has brought significant improvements to these techniques. Adding generative AI to the mix represents infinite opportunities for using AI systems to create content and interact with humans rather than just predict or analyse.
A real-world example of AI and automation in action is when a customer poses a question to a virtual agent on a company’s website. With a traditional chatbot the customer would receive a preprogrammed answer, but an AI automation model offers a more complete resolution. Since this model is an AI agent that has been trained to analyse language to assess what the issue is, it can respond with a more relevant solution.
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How does AI automation work?
AI automation works by combining artificial intelligence techniques with automation processes to perform tasks and make practical decisions, similar to how a human might. It uses algorithms as the foundation for its processes, driving the decision-making and actions. These algorithms consist of sets of rules and calculations and help AI systems analyse data, learn patterns, and make decisions autonomously.
While digital workers do a lot of the heavy lifting in automating complex tasks with AI, humans still play a vital role. They provide feedback, review predictions, and manually make corrections where needed. With self-learning, AI continuously gains insights from new data, boosting its knowledge and accuracy over time.
The technology is evolving quickly, and several key tools and concepts are essential to understanding how AI automation works.
Here’s a primer:
These automation technologies form the building blocks of AI-powered systems, enabling everything from routine task execution to complex decision-making. But for AI automation to scale effectively across an organisation, it requires robust infrastructure, both in terms of intelligence and delivery. This is where foundational models and cloud services come in.
