What is AI workflow automation? 

Artificial intelligence (AI) workflow is the process of using AI-powered technologies and products to automate tasks and streamline activities within an organization. In these structured sequences, AI systems perform, coordinate or enhance processes—either autonomously or in collaboration with human workers.

The concept applies across a wide spectrum. A simple AI workflow might involve a language model classifying incoming support ticket, while a multi-agent workflow could coordinate research, drafting and review across a content-generation process.

Recent advancements in AI-powered apps, tools and AI models have created new opportunities for businesses to improve how they handle workflows. As organizations embrace digital transformation, AI-driven workflows, powered by automation platforms and advanced templates, eliminate inefficiencies caused by manual tasks and improve the partner, employee and customer experiences.

And increasingly, autonomous intelligent systems using AI agents allow organizations to build complex multi-agent workflows from end to end. These systems are capable of handling several interconnected processes with minimal intervention. Recent research from the IBM Institute for Business Value found that 82% of cross-industry operations executives expect that process automation and workflow reinvention will be more effective because of AI agents by 2027.

Embracing AI-powered workflows as the backbone of a digital transformation may allow businesses to realize real value from AI. According to McKinsey, AI high-performers tend to report striving for transformative innovation using AI, including by redesigning workflows and scaling faster. In short, AI workflow automation, particularly though agentic automation, is expected to become a critical part of enhancing key business operations and increasing operational efficiency across sectors. 

AI agents are autonomous, rule-based software systems that, unlike traditional automation, perceive their environments and take action to accomplish a defined goal—often across multiple steps and tools. Unlike more static models responding to single inputs, agentic AI can plan a sequence of actions and call on external APIs to run specific objectives.

In the context of workflow automation, AI agents serve as active executors of complex, multi-step tasks. For instance, a single agent might research and draft a briefing document by conducting data searches, synthesizing findings and producing an output.

In AI workflow automation, multi-agent architectures multiple specialized agents to collaborate, each operating concurrently under an orchestrating supervisor agent.

APIs, or application programming interfaces, are sets of rules or protocols that enable software applications to communicate with each other to exchange data, features and functions. APIs are a key component of AI workflows, as they drive the ability to connect services. For example, connecting from a website to your bank account to buy something online is an example of an API connection in use.

Business process automation (BPA) is a strategy that uses software to automate complex and repetitive business processes. It is typically used to automate simple tasks like processing orders or managing customer accounts that are integral for running the business, but better handled by automation than employee resources. BPA can easily handle employee onboarding, payroll and other manual tasks.

A subset of BPA is robotic process automation (RPA). RPA uses intelligent automation technologies to perform repetitive office tasks. RPA powers data extraction, form completion, file movements and more.

Gen AI is a type of AI that creates original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. Generative AI technologies such as ChatGPT can help companies identify ways to improve their workflows and create the right outputs. It can respond to users’ prompts or requests to create content, such as text, images, video, audio or software code.

In workflow automation, Generative AI powers summarization, content generation and data analysis, providing natural-language outputs for employees to review. For example, gen AI might automate email follow-up responses or select code generation processes.

Intelligent automation is a hallmark of any AI-driven workflows. It involves the use of automation technologies to streamline and scale decision-making across organizations. For example, an insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs.

Machine learning (ML) is a branch of computer science that uses data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. One such subset of ML is deep learning, which uses multilayered neural networks to simulate the complex decision-making power of the human brain.

Natural language processing (NLP) is a type of AI that uses machine learning to enable computers to understand and communicate with human language. Financial services organizations, for example, can use NLP to parse information from lengthy financial statements and other datasets to make smarter decisions on where to invest.

Optical character recognition (OCR), also known as text recognition, uses automated data extraction to quickly convert images of text into a machine-readable format. It can help organization take legacy information, such as books, decks and other printed information and digitize it to feed their modern knowledge management systems. OCR’s role in document processing allows IT teams to quickly and effectively turn internal knowledge into easily digestible unstructured data.

As multi-system agentic AI has become the leading-edge technology used for complex workflow automation, orchestration layers have become more critical. These tools act as a kind of conductor for AI agents, APIs and data pipelines, managing workflow sequences as well as routing processes to decide which tools run when and under which circumstances.

AI workflows can eliminate the need for employees to focus on time-consuming tasks that are better automated. AI frees up human workers to devote more time to customers or partners—as well as orienting more jobs toward service- or relationship-based positions. Recently, for example, IBM tripled its number of open entry-level positions, planning to train early career workers in more intuitive, human-specific skills.

Organizations that use AI workflows can save their employees from wasting time on unnecessary manual tasks. Those employees can focus on high-value projects and tasks that drive extra revenue. It also reduces friction and inefficiencies in information-sharing, creating a smarter organization that decides faster.

Team members might make mistakes, especially when doing complex tasks. For those activities that are better automated, AI technologies can accomplish those tasks quicker and with a higher degree of accuracy. 

AI can remove bottlenecks by acting without needing human intervention. It can perform real-time data analysis impacting several business units. For example, marketers can use AI workflows to automatically optimize ad campaigns. 

AI workflows can also optimize funding by prioritizing top‑performing segments or social content. In many AI ecosystems, the use of dashboards helps stakeholders monitor key metrics in real-time, allowing for quick reactions to unforeseen events. 

Organizations that created AI-driven, automated workflows are likely to be more efficient than those organizations that rely on more manual processes. Organizations can use AI to create advanced chatbots and virtual assistants which streamline customer support to better assist customers when they have issues.

For some customers, an AI-driven workflow that provides intuitive tools helps provide answers without needing to talk to a human, improving resolution speed and customer satisfaction. For example, Avid Solutions, a research and development firm, reduced the time it takes to onboard new customers by 25% by using agentic AI.

AI-based automation software can easily manage many processes an organization depends on. Organizations want scalability and efficiency in their workflows so they can improve the user experience. AI workflows can easily route information and processes across the organization so executives and employees have real-time information wherever they need to access it. And where traditional growth requires proportional headcount, AI workflows allow organizations to increase volume with minimal additional investment.

There are several prominent tools and workflow automation platforms that use AI to create advanced and automated workflows. Some of the most popular include:

This product helps organizations identify leads and turn them into sales through AI-driven engagement workflows. It has several use cases, including inbound optimization, sales engagement and CRM improvements.

Created by Open AI, ChatGPT is a chatbot that is widely credited with starting the gen AI revolution. The basic version is free for any user and Open AI also offers several advanced versions for a fee.

Claude is another AI chatbot from Anthropic AI that can summarize information from longer documents, help with content creation and translate languages and help write code. Claude recently launched Claude Cowork, which allows users to delegate tasks to agentic AI.

Gemini is also a gen AI-powered assistant that can be used on its own. It is also built into Google tools like Gmail, Docs, Sheets and more, creating even more workflow opportunities. 

This IBM suite of technologies helps organizations build, tune and deploy custom AI applications. They also help businesses manage data sources and accelerate responsible generative AI workflows. There are several use cases for watsonx, including extracting insights from business data, deploying chatbots and voice agents or coding more efficiently.

IBM watsonx Orchestrate helps organizations create personalized AI agents to automate and accelerate their work—as well as providing an system to orchestrate complex workflows. It includes a catalog of prebuilt agents and tools, as well as an agent and tool-builder, to design scalable and integrated ecosystems. 

Microsoft Copilot is a gen AI chatbot that answers users’ questions. Copilot is available as a stand-alone app and is also integrated into Microsoft Teams, Outlook and PowerPoint.

Zapier is a workflow tool that uses AI to power many different types of workstreams. It also connects a wide variety of services, enabling rapid sharing of information and content across them. The software helps non-technical teams create AI agents as well as trigger-action workflows.

There are a range of standard uses cases for AI-powered workflows. Some of the most common include:

Organizations can use AI workflows to better manage the customer process, from onboarding new customers to sending them information about their purchase. They can also use these workflows to handle inbound service requests more efficiently. It can free up customer service representatives to work with customers on higher-level problems.

For example, one prominent bank recently introduced an AI-driven virtual assistant to analyze content during customer calls and suggest a “next best question” for contact center agents. The result was a 6% reduction in average handle times along with lower training requirements.

Customer relationship management (CRM) tools help organizations keep tabs on their most important customers. AI workflows increasingly power these tools, creating real opportunities for organization to derive more insights from their databases.

AI can merge multiple instances of the same customer, append information from external sources and pull in purchasing data, creating actionable insights. It can also analyze that data, helping organizations understand which customers might be at risk of churning and which ones would be open to upselling.

AI-powered automation allows organizations to collect and examine datasets in multiple formats, organize it and display it so that humans can analyze it. It can remove inaccuracies and process the data into formats that other AI algorithms can understand and analyze.

AI workflows can recognize patterns in complicated and voluminous amounts of data, finding insights that humans would struggle to identify. The workflows can also identify potential data errors and either raise them to human operators or fix them automatically. It can also extract data from external sources and neatly organize them within the organization’s internal systems, creating powerful data processing capacities that humans would not be able to perform alone.

Organizations can use AI workflows to automate their pricing strategy. For example, Uber and Lyft prices are variable depending on several factors, including supply and demand, special events and weather issues. An increasing number of businesses—such as airlines and grocery stores—take advantage of select dynamic pricing strategies.

There are several AI use cases for financial services. Organization can automate invoicing and accounts payable activities. They can also use AI to identify potential cases of fraud or financial mismanagement that might go undetected otherwise.

An IBM Institute for Business Value study found that executives anticipated generative AI improving their ability to predicting anomalies, explain variances, generate scenarios and create reports.

AI workflows can handle a host of knowledge management activities. They can transcribe phone calls and summarize meeting notes so attendees can focus on the meeting and know that the takeaways are available afterward. They can streamline how information is shared with the entire organization or individual parties. Employees can also use AI assistants and chatbots to find and analyze company information, acquiring information nearly in real time.

AI workflows can help organizations streamline many different operational processes from inventory and supply chain optimization to monitoring and quality control. For example, AI workflows can identify when a product is likely to run out due to demand and current supply levels. It can then contact the supplier to order more without needing human intervention.

AI workflows can also power predictive analytics functions. Machine learning algorithms can analyze historic data and external factors and predict what happens in the future. For example, a retailer can set up automated workflows to order more beverages for when the weather is expected to increase in temperature.

AI workflows can help predictive maintenance teams monitor equipment performance data to predict when machines are likely to have issues or fail. Therefore, organizations can optimize maintenance schedules by servicing the machines when it has the least impact on the business.

For example, IBM helped Toyota use AI to improve its predictive maintenance abilities. It led to a 50% reduction in downtime and an 80% reduction in breakdowns.

AI can help organizations improve how they find and hire employees. They can use AI solutions software to scan resumes to find the right candidates and software to automatically schedule introductory calls with candidates. They can also use AI workflows to onboard and set up training for the employees that are hired.

Corning worked with IBM to reduce HR costs while improving its employee experience with its 45,000 workers. It knew that millennials were a growing percentage of Corning’s workforce and wanted more technology-based self-service tools.

It then introduced HR self-service portals, pre-populated with each employee’s data, to make it easier for them to get the information or services they needed. The cloud-based platform now receives over 10,000 daily visits from employees and managers looking to get the information and training they need.

Sales teams can use AI workflows to identify and keep warm sales prospects. It can help sales representatives identify which prospects are most likely to buy depending on lead scoring. In addition, large language models (LLMs) such as generative AI can help sales professionals make stronger arguments to potential customers why they should purchase a company’s services.

There are also several challenges organizations must overcome when implementing AI for critical workflows. The most common include:

Employees might get nervous about companies introducing AI into their processes, especially when it replaces manual work that an employee does. Organizations can address these concerns head on and communicate how AI is meant to be additive to their work. They can also educate employees about how the removal of those manual tasks from their workloads frees them up to do more meaningful work. With consistent, transparent communication and a strong transformation plan, leaders can help employees see AI as a positive force.

As with the introduction of other systems, setting up AI workflows requires some initial work. It requires organizations to analyze their existing systems, current processes, identify areas where AI workflows would improve processes and determine what they need to change to implement the new workflows. This process takes patience and a strategic mindset. But the benefits of this initial time commitment far exceed the costs when the AI workflows are optimized to produce value.

While many uses of AI can help organizations avoid human error, they are still not infallible themselves. AI can make mistakes, which is why organizations need to check the data produced by AI. This requirement further demonstrates the importance of employees and their knowledge based on experience to serve as a final determinant of what AI workflows produce.

While many AI workflows can function without changing how employees work, some create a learning curve. As such, primary stakeholders need to invest in courses training employees to use AI or license those training tools from others. This upskilling has several benefits, as those employees learn valuable skills. They also produce better and more efficient work—as well as helping workers prepare for a future in which AI workflows are standard.