What is AI Automation?
AI automation is the process of using artificial intelligence to automate business workflows. It uses tools, code, and configuration to replace manual steps and achieve a particular outcome.
Software-based automation has been prevalent for decades—from robotic process automation (RPA) tools that automate back-office tasks like form-filling to SaaS integrations that get information flowing between different enterprise systems. However, traditional business automation has been limited in its capabilities due to the constant need for specialized pre-programming. Humans have had to screen-record app interactions or code the software systems to set up the automation. Constantly changing situations meant automation "broke" frequently and had to be redone or constantly updated to keep operations running. This has historically minimized the business impact of automation — especially in large enterprises.
AI automation seeks to solve these challenges by combining artificial intelligence with existing enterprise automation tools and knowledge repositories. Generative and predictive AI algorithms combine to sort, filter, classify, and create data in ways that reduce human intervention in the most complex workflows. AI can also work alongside and with humans to manage admin tasks in the background and reduce the cognitive load of all employees.
What are examples of AI automation?
There is a place for AI-powered smart operations in every industry and within every business. Below are just a few examples of AI automation starting to grow within industries.
Human resources
AI automation can automate time-consuming human resource (HR) tasks like candidate screening, form submissions and processing, training, knowledge sharing, and ongoing leave and payment management.
For example, Deriv, an online broker, hosted training content in a variety of locations, such as GitHub, cloud storage, internal wiki pages, and Slack discussions. This made it difficult to locate information, causing delays in getting new hires up to speed. By utilizing AI to index all customer support materials, Deriv’s HR team could quickly find and share the relevant training material for employees from various departments. Deriv achieved a 45% reduction in onboarding time and a 50% reduction in recruiting task time.
Media management
All organizations have to store, process, and publish images and videos for marketing, education, onboarding, and sometimes in core business processes. AI automation can help accelerate media editing and processing, saving time in tedious tasks. AI can generate, integrate, filter, and polish media content as needed. For example, 123RF, a stock photos agency, uses AI to automatically screen images for copyright issues and suitability. AI flags inappropriate content within seconds of upload, helping 123RF eliminate complaints about inappropriate images. AI automation allowed them to reallocate resources from manual reviews to business development.
Customer service
AI chatbots support customer self-service and automate problem resolution, reducing contact center workload. Beyond that, AI chatbots can also assist customer service staff, further automating the process. For example, BPC, a global leader in payment solutions, developed a chatbot that could be used by both clients and customer support teams. The human agent can enter a client’s request into the chatbot and pass the generated response to the customer after review. The chatbot uses retrieval-augmented generation to fetch data from BPC’s internal knowledge sources and automatically enrich human prompts to provide more relevant and accurate responses.
Sales and marketing
AI automation can be used as part of all marketing and sales workflows—from creating campaigns and ad content to supporting the sales team with personalized recommendations and offers for individual customers. For example, managed service provider Trek10 leverages AI to provide their sales team with the knowledge required to accelerate the buying process. Their AI system provides data-based recommendations to gain customer trust and reports that help close the deal by demonstrating the product value to customers more quickly.
How to assess readiness for AI automation and adoption?
Implementing gen AI technologies for automation requires business readiness. Most organizations use maturity models to assess their current state of automation. Maturity models provide a guideline for setting automation goals, prioritizing investments, and formulating an automation roadmap.
Implement governance and security frameworks
Before building a strategy, it’s necessary to implement guidelines within the organization for how AI automation governance and security will work in practice. For example, you can include:
- Outlined roles and responsibilities within the organization
- AI automation champions, including key stakeholders
- A security policy identifying data usage limitations, identity management policies, and other guardrails
- A guide for employee upskilling and change management
This will form the basis of your AI automation program.
Identify an automation and infrastructure strategy
An end-to-end automation and infrastructure strategy helps set the organization up for program success and reduces the likelihood of failure to deliver ROI. Within the strategy, consider:
- Strong business use cases
- Modern data pipelines
- Data residency and training data rule configuration
- AI tools and technologies that will power the process
- Continuous improvement practices
It is also essential to ensure that the outcomes of the automation efforts are measured. Identify and track relevant metrics and establish a baseline before automation is rolled out, then track the data over time. You can use the data to make informed decisions and improve the effectiveness of future automation efforts.
Build a skilled team
Building a strong AI culture is just as important as building your technology right.
The team building your new infrastructure and AI-powered automation should include system administrators, cloud engineers, software developers, and AI experts. Besides technologies, teams include business users who request automation, legal representatives, and security experts.
There are two ways to organize automation teams.
- A centralized automation team caters to automation needs across the organization.
- Smaller, distributed automation teams build out automation for a specific modernization initiative within a particular department.
A centralized team brings the benefit of consistency in tool usage, data management, and other AI-related tasks across the organization. However, distributed teams yield results faster and do not cause bottlenecks in your automation efforts.
What are key strategies for implementing AI automation?
Investing heavily in technology experts, software licensing, deployment, and other expensive solutions that may not yield the required results is not the best automation strategy. Deploying a large set of new tools at once can overwhelm your team, leading to a poor grasp of skills and adoption rates.
Depending on the use case, artificial intelligence automation is best implemented step-by-step. AWS tools and fully managed services provide the building blocks for rapid plug-and-play. There are no upfront investments; you only pay as you go and scale as needed.
Here are some strategies and supporting AWS tools for increasing automation maturity cost-efficiently—even with limited in-house developer expertise.
Unify the search experience.
Across organizations, data is stored in apps, repositories, files, and disparate servers. A significant challenge for all employees is knowing where to find the correct data at the right moment. AI can power unified search across data sources, allowing employees to query the full extent of the resources available to them at once. For instance, a marketing professional could query the unified search for all internal and external resources on a key product within the last year, including public-facing campaigns.
Amazon Q Business is an enterprise AI assistant that integrates with all your internal data sources and multiple third-party apps to provide summarized answers to complex questions. It cites from the source and allows custom plugins, all within a safely managed environment. It introduces automation and increases productivity by reducing employee time searching for information.
Empower your employees
Every team and individual in your organization is best placed to identify how AI can empower them to work more efficiently. For example, an employee responsible for communications requires AI to pull and summarize industry news content while an employee responsible for payroll management requires AI to generate monthly reports of time logged by contractors.
With natural language processing and AI-powered automation capabilities, you can empower employees to build and self-manage the AI automation workflows they need using natural language chat. For example, Amazon Q Apps, a lightweight app creation capability in Amazon Q Business, allows users to automate prompting, content creation, and tasks in their workflow. Users can generate apps by describing requirements in natural language. They can also share apps for others to use, duplicate, and customize.
Introduce AI in software development and ops
Software development is a natural fit for AI automation. AI-powered automation can be used for tasks like:
- Updating legacy software systems
- Refactoring code
- Developing complex modules
- Generating test cases and user documentation
- Third-party data enrichment
- Bug hunting and troubleshooting
Human-AI team can work together to design ML models, build best-fit deployment pipelines, optimize cloud infrastructure to minimize cloud spend, and so much more.
Amazon Q Developer is an AI assistant for software development that is very easy to setup and use. It runs within the developer’s environment and provides knowledgable coding and infrastructure suggestions, first code drafts, auto-code reviews, upgrades and more. Amazon Q Developer integrates with IDEs, the CLI, AWS Console, and GitLab to assist developers wherever they work.
Introduce AI in analytics
Reporting and dashboarding become even more insightful with AI-powered automation. Analysts can use AI automation to quickly generate mixed reports, combine data, compare to the market, and help guide fast decision-making.
Amazon Q in Quicksight allow users to generate visually compelling documents, build custom dashboards, and explore their data with suggested questions, data previews, and support for vague queries. It revolutionizes data exploration by providing business users with multi-visual insights that go beyond traditional dashboard limitations.
Automate customer service
Automated customer service augments your human workforce. Customer service representatives can access customer and product information instantly and discover problem fixes without having to make another call. Customers can access personalized online self-service help, conduct multi-step purchase decision-making, and blend AI-human interactions.
Amazon Q in Connect is a generative AI-powered assistant for customer service that delivers end-customers and agents the information and actions needed to solve issues in real-time. It provides faster resolution and improved customer experience.
Automate supply chain management
Supply chain management is all about forecasting. With AI-powered automation, analysts can run almost any what-if scenario to predict forecasts and conduct risk-resolution activities, optimize upstream supplier jobs, and discover hidden patterns in the data.
AWS Supply Chain is a fully managed service that unifies supply chain data and provides machine learning–powered actionable insights, built-in contextual collaboration, and demand planning.
Amazon Q in AWS Supply Chain is a generative AI assistant that helps your team operate the supply chain more efficiently by analyzing AWS Supply Chain data, providing important operational and financial insights, and answering urgent supply chain questions. It simplifies the process of finding answers, and minimizes the time needed to learn, deploy, configure, or troubleshoot supply chain management.
How can AWS support your AI automation needs?
The AI-powered automation journey often begins with enterprise-wide search, driven by natural language chat, and can grow to completely customized, complex, multi-step tasks across roles and domains. With this new form of business automation, the possibilities are endless. By laying the right foundations, organizations can expect higher productivity levels, increased employee and customer satisfaction, improved decision-making, faster product, service, and materials build-out, and more. This guide is only a starting point for your AI automation journey. You can further streamline business processes using generative AI tools and services on AWS.