What is AI Automation?

Humans are already spread thin, which explains the widespread adoption of artificial intelligence (AI). AI and intelligent automation have revolutionized the workplace, helping people — and the businesses they work for — streamline complex workflows with speed and precision.

With AI automation, businesses can simplify their processes and get more done by automatically executing a series of actions.

What we'll cover:

• What is AI automation?
• How does AI automation work?
• Differences between AI automation and other automation
• Top benefits of AI automation
• Examples of AI automation across industries
• Challenges and considerations with AI automation
• The future of AI automation

AI automation works to handle repetitive and time-consuming tasks with the goal of streamlining a given workflow. Using intelligent automation in this way helps businesses to reduce costs and boost efficiency. It also allows human talent to focus their time on strategic work instead of more tedious responsibilities.

Humans have a complex ability to make decisions. And now machines and computers can mimic that human ability to make decisions by combining AI and automation. Unlike traditional automation, which follows a static set of rules to repetitively perform a job, AI automation allows for growth. AI agents have the ability to autonomously analyze results and data and adapt the automated processes to try to achieve more relevant results.

AI automation uses both machine learning and natural language processing (NLP), which is able to to understand and respond to human language, can analyze large swaths of datasets and make intelligent decisions. Machine learning (ML) provides AI with the ability to analyze data and then recognize and predict patterns so it can make decisions based on historical data.

The introduction of large language models (LLMs) brought significant improvements to these techniques. Adding generative AI to the mix represents infinite opportunities for using AI systems to create, rather than just predict or analyze.

A real-world example of AI and automation in action is when a customer asks a question to a virtual agent on a company's website. With traditional automation, a chatbot would give a preprogrammed answer, but an AI automation model offers a more complete resolution. Since an AI automation model is an AI agent that has been trained to analyze language to assess what the issue is, it can respond with a more relevant solution.

Want to interact with an AI agent? You can give it a try in the chat box at the bottom of this page, which is powered by Salesforce’s Agentforce.

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Today's AI automation works by combining artificial intelligence techniques with automation processes to perform tasks and make practical decisions in a human-like way. It uses algorithms as the foundation for its processes, driving the decision-making and actions for AI automation. These algorithms, which consist of sets of rules and calculations, help AI systems analyze data, learn patterns, and make decisions autonomously.

AI automation began with robotic process automation (RPA). These bots, which are still in use today, perform repetitive, rule-based tasks that don't require deep analysis — things such as populating forms based on existing data and sending automated email responses. As AI has evolved, automation's capabilities have grown to include end-to-end processes, connecting systems, and coordinating tasks.

The process of AI automation begins by collecting data relevant to the task. This data can come from structured sources, such as databases, or unstructured data sources, such as text documents, images, and audio files. The AI removes irrelevant or erroneous data and then converts raw data into a new format, such as tabular data for ML algorithms or tokenized text for NLP.

Once the data is prepared, it's used to train an AI model. There are three types of machine learning:

• Supervised learning: Labeled data is used to train the model — in other words, each input in the training dataset is paired with a known output. An example of this would be email spam filtering, where emails are marked either “spam” or “not spam.”
• Unsupervised learning: Data without any labeled outcomes is the basis for training. Instead, the AI model identifies patterns, structures, and relationships within the data on its own. Customer segmentation in marketing is an example of unsupervised learning, since customer data is analyzed without any predefined labels.
• Reinforcement learning: An AI model learns by interacting with an environment and receiving feedback in the form of rewards or penalties based on its actions. For instance, an autonomous car being trained on how to drive.

Once trained, the AI model is deployed into a workflow automation:

• Inference engine: The model makes predictions in real time based on incoming data. For example, using conversational AI, a model in customer support can instantly identify the intent of a customer's question.
• Decision-making: The predictions then guide the next steps in the workflow. One instance of this is if an AI system detects a potentially fraudulent transaction, it may automatically block the transaction and escalate the issue to a human to investigate.

Humans still play an important role in AI automation. In the human feedback process, people review AI predictions and manually make corrections where needed. These corrections are then fed back to the AI, which further improves its accuracy. With self-learning, the AI continuously gains insights from new data, boosting its knowledge over time.

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There are significant differences between automation powered by AI vs. automation that is more traditional. AI-driven automation can handle more complex tasks. Traditional automation is useful for rule-based, repetitive tasks in stable environments, while AI automation is better suited for dynamic, data-rich tasks that require decision-making.

Instead of relying on specific keywords like a chatbot would, AI automation uses ML and NLP to train models based on historical customer data. It interprets the meaning and context of the text, understanding different phrases and expressions with machine learning and natural language processing. An AI agent can scan a customer’s text that reads, "I'm not sure how to make a payment on the app," and use its model-based training to offer a suitable human-like response.

AI automation can even prioritize tickets based on urgency detected through sentiment analysis — something RPA systems can't handle as effectively.

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When comparing ​​AI-based automation provides significant advantages over traditional automation. It streamlines repetitive tasks, reduces human error, and speeds up processes. The time saved with AI and automation allows employees to focus on strategic, high-impact work that drives growth. By working faster and smarter, intelligent automation helps businesses be more efficient, save money, and stay competitive.

Here are a few examples:

• Scalability: With machine learning and cloud computing, AI-powered automation can scale with increasing data and demands.
• Speed: AI-driven autonomous agents enable faster response times in customer interactions.
• Accuracy: AI systems excel at precision, especially in tasks such as data entry, quality control, and image recognition.
• Complex tasks: AI can tackle multi-layer work that requires real-time decision-making and pattern recognition.

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Saving time, increasing efficiency, and cutting costs are a few ways AI automation has revolutionized almost every industry. Employees spend an estimated 41% of their time on repetitive, low-impact work, and 65% of desk workers believe generative AI will free up their time so they can be more strategic, according to the Salesforce Trends in AI for CRM report.

Here are a few of the industries where AI automation has already made an impact:

Automotive AI uses data from both vehicles and drivers to offer new and engaging services to customers. And auto makers and dealers can take advantage of AI solutions that are grounded in relevant business context. All of this means that the automotive industry can move faster and better serve their ultimate customer: the driver.

Whether it’s for payers, providers, or public health agencies, healthcare AI has huge potential. Healthcare AI can quickly reduce administrative overhead like billing and scheduling, giving healthcare providers more time to spend with patients. With patient data that’s grounded in relevant context and health information all in one place, AI can help healthcare providers to more accurately detect diseases in their early stages and suggest preventive measures.

Manufacturing AI can help control expenses by searching for cost changes in dense contracts, improving efficiency, and reducing labor costs. AI automation can also help to scale commerce, unifying customer interactions across digital and physical channels, and generate sales recommendations based on historical data. Not to mention analyze data from machinery to avoid expensive repairs, use image recognition to detect defects in products and equipment, and ensure safety by having AI-powered robots perform the most dangerous tasks.

Automotive AI uses data from both vehicles and drivers to offer new and engaging services to customers. And auto makers and dealers can take advantage of AI solutions that are grounded in relevant business context. All of this means that the automotive industry can move faster and better serve their ultimate customer: the driver.

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While AI automation can be transformative for businesses, it's not a panacea. As intelligent automation advances, ethical concerns increase. A shift in the skills needed to work with AI , a lack of transparency with AI-driven outcomes, and privacy breaches are all thorny issues that need to be weighed carefully.

Businesses can be proactive by learning more about the advantages and disadvantages of AI as well as its limitations — and adopting responsible, fair, and inclusive practices along the way.

Here's a closer look at some challenges:

• Data quality: Inconsistent, incomplete, or outdated data can compromise the performance and reliability of AI systems.
• Integration with existing systems: Many companies have legacy systems that aren't compatible with AI-driven platforms.
• Algorithm bias: AI systems can inadvertently learn biases present in training data, leading to unfair or inaccurate outcomes.
• Costs: Developing and deploying AI automation solutions can be expensive, especially for smaller businesses.

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Autonomous AI agents have revolutionized customer relationship management (CRM) software, making life easier for those who work in service, sales, marketing, and commerce. Business leaders who use AI are clearly seeing the benefits — 90% report cost and time savings, according to the Salesforce State of Service Report.

There are a variety of tasks that AI agents can take on, including answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. They can also be deployed quickly, without the hassle and expense of AI model training. These autonomous AI agents can work 24/7 — and businesses can scale this virtual workforce on demand with just a few clicks.

The future of AI automation promises even more advances. AI systems are increasingly able to handle tasks that require perception, reasoning, and even complex problem-solving — capabilities that were once uniquely human.

Artificial general intelligence (AGI) is a technology that is currently being developed. It'll be able to understand, reason, plan, and apply knowledge. It may also be able to transfer knowledge it's learned from one domain to the next — possibly performing at an expert human level. AGI may even be able to develop agency.

While job roles will inevitably shift, opportunities for humans in creative and more strategic and higher-skilled roles will grow. Instead of competing with these powerful AI models, humans will guide them to prevent unforeseen outcomes.

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A future where humans can use machines to work smarter rather than harder is nearly a reality. AI automation will reshape industries on a global scale as it continues to be adapted for more business situations, providing more efficiency, and helping companies solve more challenges with the help of AI agents.