What Are AI Workflows?
AI workflows are processes used to build an end-to-end AI solution, also known as an AI pipeline, that can leverage machine learning, deep learning or generative AI (GenAI) to help automate tasks or inform decision making. Organizations construct their AI pipelines using incremental AI workflows that include data generation and preparation, AI modeling or AI model training, and lastly deployment and AI model inference. To help make it easier to build a complete AI pipeline, some AI workflows can be predefined, modular, and designed for a specific use case. These predefined AI workflows can also be referred to as reference implementations or reference kits.
Role of AI Workflows
Most AI deployments will take the form of an AI pipeline that consists of three steps: data, model, and deploy.
- The data step includes any formal data gathering or generation processes, usually followed by preprocessing and storage. This step prepares data for use by an AI model, either for training or inference.
- The second step, AI modeling, involves the development of an AI model through the layering of algorithms to create a neural network that simulates the logical and decision-making patterns of the human mind. Once defined, AI models are trained on high volumes of data to improve accuracy and quality of results.
- The final step, deploy, occurs when the AI model is deployed in a real-world use case, such as detecting product defects on a factory assembly line, or in the case of GenAI, as a personalized chatbot that answers user queries with minimal or zero human intervention.
Within the AI pipeline, AI workflows support any data preparation, model training, or inference process. For example, AI workflows can be used to facilitate model development by providing pretrained models or code samples for organizations to work with, or they can be used to help optimize an already-deployed AI model to run faster and more efficiently.
Enterprises that use commercially available AI applications such as ChatGPT could potentially forgo the need to build their own AI workflows. However, enterprises may have less control over data they input into these AI applications, which can expose them to potential data security and privacy risks.
Benefits of AI Workflows
According to a McKinsey & Company survey, 72 percent of organizations have adopted AI and 65 percent have adopted generative AI in 2024, up from 55 percent and 33 percent respectively in 2023.1 As more organizations adopt AI, AI workflows can help them overcome challenges related to designing, deploying, or maintaining their AI pipelines:
- Faster deployment for AI applications: Research and development efforts for AI solutions are time- and effort-intensive and require highly skilled positions such as data scientists and AI developers. Many AI workflows offer the ability to leverage prebuilt components for key steps in the AI pipeline, giving organizations a head start in building and deploying performant AI solutions.
- Simplified integration and compatibility: As organizations look to integrate general purpose AI into their existing business operations, they face challenges from working with infrastructure that’s designed for use cases such as email servers and customer databases. AI workflows can be tested and validated on the same hardware as common IT servers, making it possible to leverage previous IT investments while introducing new AI applications.
- Openness, extendibility, and reconfiguration: Many AI workflows are built using open source frameworks that help reduce licensing costs and allow for deeper levels of customization to help meet specific business needs. This also empowers organizations to grow a portfolio of reusable AI software assets that can be deployed across diverse hardware targets as needs evolve.
AI Workflow Solutions
Organizations can take advantage of predefined AI workflows by adopting reference implementations, AI toolkits, and pretrained models as they develop their own AI pipelines. Some AI workflows are developed and marketed for frequently used or high-value use cases with demonstrated success in their industry, such as for conversational AI chatbots, automated visual quality control inspections, or predictive maintenance for equipment and asset health. AI orchestration suites also give organizations more tools to help manage AI workflows as they test, build, and deploy AI solutions across heterogeneous environments.