Human-AI collaboration is about how humans and AI can work together to achieve goals. For example, instead of using AI to replace humans in a workflow, we can leverage AI to automate repetitive tasks, as a co-creator, or to make us more productive. In 2026, we’ve started to see collaborations between humans and AI across all sectors of society, from design and programming to writing, drafting documents, and healthcare to education.
Read more: What is Human-AI Interaction?
Human-AI Collaboration in Creative Work
Due to the capabilities of generative AI (GenAI), which is a type of AI that can generate new materials, we’re seeing a rapid adoption of GenAI tools in creative work, such as in design, software development, writing, and education. One type of GenAI is Large Language Models (LLMs), which are trained on vast amounts of textual data.
Read more: How can Generative AI be used in Product Design: The best tools, workflows & insight from research
- In UX/UI design, designers use LLMs (e.g., ChatGPT or Claude) to brainstorm ideas, develop personas, and to formulate documents. Designers can also co-create prototypes with generative AI tools such as Figma Make or Google’s Stitch, especially during early-stage prototyping. In this type of human-AI collaboration, the designer remains active by evaluating AI outputs and making final decisions about which parts of the generated materials to include or exclude.
- In education, teachers can use LLMs (e.g. ChatGPT and Claude) to develop lesson and workshop plans. Tools such as Claude can search the internet for related lesson plans and workshops to provide inspiration. The AI models also have embedded knowledge from their training data that they can draw upon. Still, it’s the teacher who has knowledge of their course, their students, and the cultural and societal context, and who decides how the final lesson plan will look.
- In software development, programmers can use AI code agents such as Cursor and Claude Code to help them write code. AI code agents can also be integrated into code management systems such as GitHub and write code tests and documentation. However, it’s still humans who oversee the codebase to ensure quality before launching the application to end-users.
- For writing, a writer can use LLMs (e.g. ChatGPT, Claude, Grammarly) to develop text-based documents, including generating bodies of text or fletching out ideas written from bullet points.
These examples of human-AI collaboration highlight the strengths of AI, utilizing the fact that AI models have been trained on vast amounts of example data and can search the web at an incredible speed compared to humans.
Why should we collaborate with AI?
Human-AI collaboration is a way of viewing AI as a collaborative partner, co-creator, and assistant, rather than a technology that will replace us on the job market. One central theme is to use AI for augmentation instead of automation. However, no one can know for certain what the effects will be. If AI can perform parts of, or the entirety of, our work in the future, what would keep companies from paying salaries to human employees?
Today, one of the strongest arguments against using AI to replace human workers is based on its weaknesses, which include:
- Large Language Models, such as ChatGPT, can produce faulty information and make things up.
- Generative AI, e.g., for design (such as Lovable and Figma Make), can produce designs that are inadequate in terms of usability and fail to meet user or client needs.
- AI code agents (such as Cursor and Claude Code) sometimes produce code that is incompatible with larger codebases or lacks the context to work in a larger organization.
Thus, if we use AI to fully replace our work tasks, the quality of the output must be raised.