ClickUp is adding templates and collaboration tools to make it easier to access generative artificial intelligence platforms such as ChatGPT from within its project management application.
Haris Butt, head of product design for ClickUp, said ClickUp AI now includes pre-built department-specific prompts that any product managers, developers or designers can easily access rather than having to develop prompt engineering skills to engage a generative AI platform more effectively.
The templates, for example, make it simpler to produce summaries of project timelines in addition to the ability to automatically generate a whiteboard that lists tasks that need to be finished to complete a project.
In the longer term, ClickUp is also moving toward developing its own large language model (LLM) using its own data to provide a generative AI experience specifically optimized for project management, added Butt.
ClickUp is embedding AI into project management applications as part of an effort to eliminate drudgery, added Butt. One of the primary issues that makes project management challenging is that navigating workflows can be challenging, especially for stakeholders that might not be as well versed in, for example, how a Gantt chart flows.
It’s still early days as far as generative AI is concerned, but it’s already apparent that a wide range of templates are being developed to make it simpler to invoke these platforms. The need for project managers to become well-versed in all the nuances of prompt engineering techniques to optimize generative AI queries is likely to be limited.
In fact, the general expectation is that every application will eventually include templates for invoking generative AI capabilities. Less clear is the impact that should have on productivity. In the case of DevOps teams, however, many tedious project management tasks should become increasingly automated.
Generative AI will, of course, soon be employed across the entire DevOps workflow. Many of the tasks that have historically conspired to make DevOps processes tedious will be automated. In fact, many application developers are already using AI to test and review code. DevOps teams that have historically advocated ruthless approaches to automation are likely to be at the forefront of AI adoption.
There are, of course, concerns about the accuracy of generative AI platforms such as ChatGPT that have been trained using data that hasn’t always been verified. As a result, it’s possible that some of the results being surfaced by these platforms should be reviewed by humans before being acted on. However, LLMs trained on narrower ranges of validated data will soon become commonplace. As those AI advances are made, the accuracy of generative AI platforms that are optimized for specific use cases will increase.
In the meantime, DevOps teams should be more concerned about being left behind than about how generative AI impacts their employment prospects. There is no doubt generative AI will transform DevOps workflows along with a wide range of processes. The issue now is determining where DevOps teams can add value—and where machines can’t—in an era where IT environments are becoming more complex with each passing day.