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.

AI automation uses advanced technology to manage tasks and processes by programming computer systems to review data, recognize 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 judgments, 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, 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) 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 analyze.

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 analyze language to assess what the issue is, it can respond with a more relevant solution.

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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 analyze 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 organization, it requires robust infrastructure, both in terms of intelligence and delivery. This is where foundational models and cloud services come in.

For AI automation to scale, it requires the core infrastructure of foundational models and cloud services to work together. Think of foundational models as the brains of AI systems, while cloud systems act as the delivery platform that makes those brains accessible and useful in the real world.

Foundational models are large-scale machine learning models trained on massive datasets. They’re designed to perform a wide variety of tasks, including:

• Understanding and generating natural language
• Powering conversational agents and language translation
• Generating images and other media from text prompts

Data collection in AI automation refers to the process of gathering, organizing, and preparing data that AI systems use to learn, make predictions, or perform tasks automatically.

Data preparation is a crucial step in AI automation. It involves transforming raw data into a clean, structured, and machine-readable format so AI models can learn from it or make accurate predictions. In automated systems, much of this process is streamlined using tools, scripts, and workflows that reduce manual effort.

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. 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. Different types of machine learning include:

• 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. 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 can be trained to drive.

Deep learning is the use of automated systems to build, train, and deploy deep neural networks — AI models that learn complex patterns from large volumes of data. Automation accelerates and streamlines tasks like data preprocessing, model selection, hyperparameter tuning, and deployment. This makes it easier to scale deep learning applications.

NLP for AI automation is the use of AI to understand, interpret, and generate human language automatically.

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.

Continuous learning in AI models — also known as online learning, incremental learning, or lifelong learning — refers to the ability of a model to keep learning and refining its algorithms to improve over time as new data becomes available.

There are significant differences between automation involving AI agents and traditional automation. 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. In short, AI agents can handle far more complex tasks than traditional automation tools.

Instead of relying on specific keywords like a chatbot would, AI agents use ML and NLP to train models based on historical customer data and interactions. They can then interpret the meaning and context of the content. For example, 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 agents can even prioritize tickets based on urgency detected through sentiment analysis — something RPA systems can't handle as effectively.

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.

This emerging class of AI-driven tools has given rise to what's often called digital labor — virtual workers capable of handling tasks like data analysis, document processing, and customer interaction at scale. Unlike traditional software bots, these digital workforces can understand context, learn from patterns, and continuously improve, making them a powerful extension of the human workforce.

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 multilayer work that requires real-time decision-making and pattern recognition.

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:

Thanks to AI automation, sales pros no longer have to devote a significant part of their day to repetitive tasks like data entry and following up on leads. AI for sales quickly identifies the best leads, offers smart sales forecasts, and personalizes the customer experience. With AI working 24/7 on things like CRM automation, sales pros have more time and energy to devote to the most important part of the business — building relationships and closing deals.

Want more proof? One AI writing company increased conversion to its upgraded plans by 80% with AI-based lead scoring.

AI for the service industry has improved the experience for everyone — customers, service teams, and support reps alike. Customers benefit from personalized service experiences that are usually faster and more accurate. When service teams use tools like Agentforce, routine tasks like ticketing, case routing, and response generation are automated. The platform also provides valuable insights and recommendations, helping teams resolve cases more efficiently. And with automated customer service, support reps have more time to focus on complex issues that require human empathy and problem-solving skills.

How effective is AI? The service reps at one telecommunications company improved response times for resolving customer issues by 67%.

AI for marketing is revolutionizing marketing by streamlining tasks and enhancing personalization. By analyzing customer data, AI enables real-time segmentation and predictive analytics, allowing for more targeted campaigns. By leveraging AI tools for marketing automation, time spent on repetitive tasks like email scheduling, social media posting, and ad optimization is greatly reduced. AI-driven insights help in understanding customer behavior, leading to more effective strategies and improved ROI. With newly cleared calendars courtesy of AI, marketing teams now have time in their schedule to focus on creative strategies.

Interested in results? One of the largest privately held insurance brokerages in the nation leveraged AI automation to save the company an estimated 44,000 hours and $6.9 million.

AI for commerce is a boon to both customers and merchants. It makes the shopping experience more personalized and frictionless, offering tailored product recommendations based on a customer's past purchases and browsing history. AI-driven insights help merchants by enabling faster — and often better — decision-making by analyzing large volumes of data to identify patterns and trends. This allows merchants to make informed decisions and adapt quickly to market changes.

An example IRL: An online staffing platform decreased handle times by 20% with AI-generated replies.

AI for IT streamlines operations and enhances efficiency. Routine tasks like system monitoring and data management are automated, freeing IT professionals to focus on strategic initiatives. With intelligent process automation, IT teams can quickly identify and address issues, reducing downtime and improving system reliability. AI also assists in predictive analytics, allowing for proactive maintenance and informed decision-making.

AI works for IT teams: After implementing generative AI, a global information management services company saw a 70% decrease in chat abandonment rates.

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 a relevant business context. All of this means the automotive industry can move faster and better serve its ultimate customer: the driver.

AI takes the wheel: One of the biggest names in racing leveraged AI to connect with fans on a deeper and more personal level, sending personalized push notifications to millions of fans globally in real time.

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 more accurately detect diseases in their early stages and suggest preventive measures.

Healthy savings with AI: After launching Salesforce's Agentforce nurses at a healthcare company now spend 75% less time on manual charting, translating into $799,000 in annual savings.

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. Plus, it can 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.

AI in action: One of Europe’s largest industrial manufacturing companies launched an AI-based app and improved first-time fixes for hardware issues by 100%.

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 small businesses.

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 seeing the benefits — 90% report cost and time savings.

AI agents can take on a variety of tasks, including answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. They also can 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), a theoretical form of AI capable of human-like general intelligence, is an area of active research. While it is currently in the exploratory stage, it promises to 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. Meanwhile, enterprise general intelligence (EGI) is a similar concept, focused on AI systems for business.

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

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 increased efficiency and helping companies solve more challenges with the help of AI agents.