欧博官网AI Workflow
Writer, IBM Consulting
Amanda Downie
Editorial Content Strategist, IBM
AI workflow
Artificial intelligence (AI) workflow is the process of using AI-powered technologies and products to streamline tasks and activities within an organization.
Recent advancements in AI-powered apps and 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 experience.
An IBM Institute for Business Value report found 92% of executives agreed that their organization’s workflows would be digitized and willuse AI-enabled automation by 2025.
According to Vanson Bourne (link resides outside of IBM.com)1 80% of organizations currently pursue the goal of end-to-end automation of as many business processes as possible.
AI-powered workflows become a critical step in enhancing key business operations, enhancing the work of their employees and improving the bottom line.
Components of AI workflow automation
There are several AI technologies that organizations can use to improve their workflows.
APIs
Business process automation
Generative AI
Intelligent automation
Machine learning
Natural language processing
Optical character recognition
APIs
APIs, or application programming interface, 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
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.
Generative AI
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 like 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. Gen AI can power so many AI workflows, from helping identify strategic goals and tactics to setting up meetings to offering feedback on marketing copy. McKinsey predicts gen AI might automate (link resides outside of IBM.com) up to 10% of all tasks in the US economy.2
Intelligent automation
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
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 uses multilayered neural networks to simulate the complex decision-making power of the human brain.
Natural Language Processing
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
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.
AI workflow tools
There are several prominent tools that use AI to create advanced and automated workflows.
Apollo.io
ChatGPT
Claude
Google Gemini
IBM watsonx™
IBM watsonx Orchestrate™
Microsoft Copilot
Zapier
Apollo.io
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.
ChatGPT
Created by Open AI, ChatGPT is a chatbot that is credited with starting the gen AI revolution (link resides outside of IBM.com).3 The basic version is free for any user, and Open AI also offers several advanced versions for a fee.
Claude
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.
Google Gemini
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.
IBM watsonX
This IBM® suite of technologies helps organizations build custom AI applications for their business, manage all data sources and accelerate responsible AI workflows. There are several use cases for watsonx, including creating content, deploying chatbots and coding more efficiently.
IBM watsonx Orchestrate
IBM watsonx Orchestrate helps organizations create personalized AI assistants and agents to automate and accelerate their work. It uses natural language processing to understand and run tasks. IBM watsonx Orchestrate uses a catalog of prebuilt apps and skills and a conversational chat experience to design scalable AI assistants and agents, automate repetitive tasks and simplify complex processes.
Microsoft Copilot
This is a gen AI chatbot that answers users questions. Copilot is available as a standalone app and is also integrated into Microsoft Teams, Outlook and Powerpoint.
Zapier
Zapier is a workflow tool that now 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.
AI workflow use cases
There is a range of standard uses cases for AI-powered workflows
Customer service
Customer relationship management
Data entry
Dynamic pricing
Financial reporting
Knowledge management
Operations management
Predictive analytics
Predictive maintenance
Recruiting and hiring
Sales and upselling
Web development
Customer service
Organizations can use AI workflows to better manage the customer process from onboarding new customers to sending them information about their purchase to handling inbound service requests. It can free up customer service representatives to work with customers on higher-level problems.
Camping World partnered with IBM to improve customer engagement by 40% and decrease wait times drop down to 33 seconds, thanks to AI workflows.
Customer relationship management
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.
Data entry
AI can collect and examine data sets 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.
Dynamic pricing
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.
Financial reporting
There are several use AI workflow 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 executives anticipated generative AI improving their ability to predicting anomalies, explain variances, generate scenarios (40%) and create reports.
Knowledge management
AI workflows can handle a host of knowledge management activities. They can transcribe phone calls and summarize meeting notes, so attendees can be 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, making quicker information on the fly.
Operations management
AI workflows can help organizations streamline many different operational processes from inventory and supply chain optimization to monitoring 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.
Predictive analytics
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.
Predictive maintenance
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 optimizes maintenance schedules by servicing the machines when it has the least impact on the business. IBM helped Toyota use AI to improve its predictive maintenance abilities. It led to a 50% reduction in downtime and 80% reduction in breakdowns.
Recruiting and hiring
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, 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 and upselling
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 (link resides outside of IBM.com).4 In addition, LLMs such as generative AI can help sales professionals make stronger arguments to potential customers why they should purchase a company’s solutions.
Web development
AI is at the heart of many web development workflows. It can help developers write and test code, learn about a code base, documenting code and other uses. A (link resides outside of ibm.com)5 study found developers expected that next year they would integrate more AI into documenting code (81%), testing code (80%) and writing code (76%). AI workflows are also a key component of the no code/low code movement, where nondevelopers can better understand and participate in the web development process.
Benefits of AI workflow automation tools
There are several key benefits to using AI-powered workflows.
Automate repetitive tasks
Drive cost savings
Eliminate human error
Enhance decision making
Improve the customer experience
Streamline and optimize processes
Automate repetitive tasks
AI workflows can eliminate the need for employees to focus on time-consuming tasks that are better automated. AI can handle these routine tasks and free up the human workers to spend more time with customers or partners and produce more business value.
AI can contribute to the “productivity paradox,” according to Rob Thomas, SVP Software and Chief Commercial Officer at IBM. Instead of taking everyone’s jobs, as some have feared, it might enhance the quality of the work being done by making everyone more productive.
Drive cost savings
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 makes decisions faster.
Eliminate human error
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.
Enhance decision making
AI can remove bottlenecks by acting without needing human intervention. It can do real-time data analysis to make decisions that impact several business units. For example, marketers can use AI workflows to automatically optimize ad campaigns. AI workflows can alter spend to route funds to the highest-performing segments or social media posts.
Improve the customer experience
Organizations that created AI-driven, automated workflows are likely to be more efficient than those that rely on more manual processes. Organizations can use AI to create and start advanced chatbots and virtual assistants to streamline customer support to better assist customers when they have issues. For some customers, an AI-driven workflow that provides user-friendly chatbots helps them get answers without needing to talk to a human, therefore improving customer satisfaction. For example, Estee Lauder has started (link resides outside of IBM.com)6 a voice-enabled makeup assistant via a chatbot feature.
Streamline and optimize processes
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 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.
Challenges of AI workflows
There are also several challenges organizations must overcome when setting up AI workflows.
Employee concerns
Initial setup
Possibility of mistakes
Upskilling and reskilling
Employee concerns
Employees might get nervous about companies introducing AI into their processes, especially if 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. Eventually, employees see AI as a positive force for them.
Initial setup
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 things and determine what they need to change to implement the new workflows. This takes patience and a strategic mindset. But the benefits of this initial time commitment far exceed the costs if the AI workflows are optimized to produce value.
Possibility of mistakes
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 further demonstrates the importance of employees and their knowledge based on experience to serve as a final determinant of what AI workflows produce.
Upskilling and reskilling
While many AI workflows can function without changing how employees work, some require employees to learn their processes. As such, organizations likely 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.
Footnotes
1 For Success with AI, Bring Everyone On Board, HBR, June 2024.
2 Generative AI: How will it affect future jobs and workflows?, McKinsey, 21 September 2023.
3 A year after ChatGPT’s release, the AI revolution is just beginning, CNN, 30 November 2023.
4 Revolutionizing sales in distribution: Harnessing the power of AI, McKinsey, 24 July 2024.
5 , Stack Overflow, 2024.
6 12 most popular AI use cases in the enterprise today, CIO.com, 19 September 2023.