What Is AI Automation?

AI automation is the process of using artificial intelligence (AI) technologies to expand the capabilities of robotic process automation (RPA) technologies. Traditional RPA technologies repeat digital tasks like clicking, moving files, or copying data to automate routine business functions. AI introduces intelligence to the process so the RPA can perform more complex tasks with minimal training. For example, AI automation can categorize documents, label images, respond to customer queries, and more.

Automation is the process of automating well-defined actions and procedures. It is best suited for repetitive tasks like scheduling an email, data entry, and tasks that are the same every single time. Where automation falls short is with any task that doesn’t have a certain outcome. Automation cannot finish manual tasks without stable business processes and clear results.

In contrast, artificial intelligence mimics human intelligence using neural networks. It utilizes several technologies and strategies, such as natural language processing, predictive analytics, and computer vision. You can integrate AI and automation technologies to perform highly challenging tasks that are impossible to automate traditionally.

You can apply AI automation systems across broader use cases and problem areas. For example, AI can

• Use complex algorithms to understand the logic behind a task's outcome.
• Learn from tasks and improve over time.
• Replicate the task at scale and in numerous contexts.

Businesses can use AI automation to generate several benefits.

Improve efficiency

AI automation allows businesses to streamline repetitive tasks, even complex ones. Human workers can focus on other high-value projects, improving overall business efficiency.

Enhance decision-making

Researchers train AI models on vast amounts of data. This contextual understanding allows AI to identify patterns in data and trends that would otherwise go unnoticed. Intelligent automation in data analysis also reduces human error and allows for more precise decision-making.

Increase efficiency

AI-powered automation tools can reduce the cost of business operations by reducing labor costs without compromising accuracy, speed, and scale. If a company needs to meet certain goals or experiences a surge in demand, it can use AI automation to increase production speeds. At the same time, businesses can reduce the need for human input in key tasks and contribute to a more productive workforce.

Drive innovation

AI and automation are so effective that businesses can use them in various use cases and industries. For example, in supply chain management, AI automation can pull data from internal sensors to adjust the temperature of vehicles transporting goods. AI automation is also useful in a variety of customer experience use cases. AI tools can respond to human inputs as chatbots, act as virtual assistants, and point customers to knowledge hub resources.

Foundational models (FMs) are at the center of intelligent automation. An FM is a pre-trained AI model that learns from vast data and can complete several tasks in various industries. You can train it further on specific functions and with internal data. For example, you can train the FM on internal document archives to answer customer queries in a relevant manner. Foundation models are used alongside other AI technologies below.

Multimodal AI

Multimodal data could include everything from speech and voice data to videos and images. Many foundation models are multimodal, allowing AI automation systems to understand various inputs and data formats, not just text information. Multimodal AI technology allows businesses to automate a greater range of processes.

Combined with technologies like computer vision, multimodal AI allows AI models to process even unstructured data and help automate routine tasks. Another technology that multimodal AI may use is optical character recognition, translating handwritten text into machine-readable information.

Retrieval augmented generation

Retrieval-augmented generation (RAG) extends the already powerful capabilities of FMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving FM output so it remains relevant, accurate, and useful in various contexts. RAG enables businesses to connect their foundational models to enterprise data, creating a flow of information that the model can then use for task automation.

Prompt engineering

Prompt engineering is the process of guiding the FM to generate desired outputs. FMs require detailed instructions to create high-quality and relevant output. In prompt engineering, you choose the most appropriate formats, phrases, words, and symbols to guide the AI system, enhancing its ability to automate precisely.

Smart assistants

A smart assistant is a powerful generative AI tool that can leverage a company’s internal data to help automate several processes. Smart assistants connect to enterprise data repositories to analyze and process data. They can generate content, securely complete tasks, provide summaries, and answer any questions you may have about your own business data.

For example, an employee asks a smart assistant about company policy, product information, or a business result. The smart assistant automates the process of data extraction and presentation.

AI automation is vital for effective digital transformation. You can start by investing in ready-made intelligent automation tools or building your custom business process automation. Below are some steps that organizations can follow.

Plan your automation

The first step toward intelligent process automation is to create goals and strategies for your digital transformation. Setting clear automation objectives helps align this new pursuit with your existing business goals. This stage is also where you can explore granular details of your automation plan, such as your budget, implementation pathway, and overall strategy.

Select a model of operation

There are several different models for implementing AI automation in a business. The first is to share automation duties between development teams. This approach supports continuous integration and rapid feedback loops. The second approach is to designate automation duties solely to one team for efficient business process management. Establishing clear goals and policies helps enhance the success of your AI automation efforts.

Design end-to-end automation

Your team’s automation efforts should span from end to end across the development lifecycle. Develop and integrate an automation toolchain across the software lifecycle. Where possible, integrate automated observability solutions that optimize and monitor your automation workflow’s overall security, performance, and reliability.

Measure solution efficacy

Focus on demonstrating the cost savings, time savings, and overall productivity that your automation systems introduce. You can calculate the return on investment by monitoring these factors. Your ROI calculations can include cost savings, efficiency gains, time to market, and productivity improvements.

Assessing maturity

Introducing maturity models into your organization helps assess the current state of automation and adoption. Your maturity level determines the KPIs you should measure and the next steps to prioritize for the future. Assessing maturity in AI tools also helps identify new use cases and opportunities for more business value.

With enterprise-grade security and privacy, access to industry-leading FMs, and generative AI-powered applications, generative AI on AWS lets you build and scale AI automation customized for your data, use cases, and customers. For example

• Amazon Q is a generative AI–powered assistant that generates code, tests, and debugs. It has multistep planning and reasoning capabilities to transform and implement new code generated from developer requests. It can also answer questions across business data and support analytics use cases.
• Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) through a single API so you can build generative AI applications with security, privacy, and responsible AI.
• Amazon SageMaker lets you build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows for any use case.

For customers who want ready-made options, machine Learning on AWS details a range of artificial intelligence (AI) and machine learning (ML) services you can integrate to help streamline workflows. For example, you can use:

• Amazon Personalize to quickly build and deploy curated recommendations and intelligent user segmentation at scale.
• Amazon Rekognition to automate image recognition and video analysis tasks with computer vision.
• Amazon Textract to automatically extract printed text, handwriting, and data from any document.

Get started with AI automation on AWS by creating an account today.