Atlassian this week revealed it has acquired Cycle, a provider of a platform that leverages artificial intelligence (AI) agents to enable organizations to more easily analyze feedback provided by customers.
The core technology developed by Cycle will be integrated into Jira Product Discovery, a platform the Atlassian provides to enable product teams to manage cross-functional workflows.
The inclusion of the AI capabilities specifically trained to analyze customer feedback into that platform will make it easier for product teams to plan better, says Tanguy Crusson, head of product for Jira Product Discovery at Atlassian. That’s crucial because an Atlassian survey of members and managers of product teams finds a full 84% were very concerned (27%) or somewhat (57%) concerned the products they are developing will not succeed, he adds.
The Cycle AI technologies will provide levels of insights that should reduce that anxiety, notes Crusson. “They’ll be better connected to customer conversations,” he says.
It’s not clear to what degree existing analytics tools provide insights into customer preferences but as the amount of that data being collected continues to exponentially increase the need for AI tools to analyze it all is becoming more apparent. The challenge, of course, is finding ways to make sure the insights being surfaced by AI tools are validated, which in some instances may require using multiple AI models to validate results.
Hopefully, organizations armed with those insights will make better product management decisions. One of the issues that organizations have wrestled with time immemorial is the number of failed product introductions and updates tends to far outnumber the successful ones. Fortunately, it usually only takes a few successes to make up for all the failed initiatives. However, if the ratio of failed to successful efforts could be improved using AI insights, the revenue implications for organizations could be profound.
One way or another, product management will become more scientific in the age of AI. Of course, a lot more goes into making a successful product launch than simply having access to better analytics. Sometimes there is no substitute for human intuition but at the very least there is comfort in analytics that might confirm an instinctual guess borne of hard-won experience.
Nevertheless, gaining funding for a project typically requires some customer research to convince senior leaders there is a good chance for a meaningful return on investment (ROI). The more data that drives that analysis the better able most product teams will be able to make a case for their project when there is almost always no shortage of initiative competing for the same amount of limited funding resources. In fact, the more data product managers have the more likely it becomes they will have a seat in the room where the final decision is made, notes Crusson.
Mountains of data and analytics won’t guarantee ultimate success but they should, at the very least, provide product teams the knowledge that, given all the available evidence, the best decision possible is being made.
