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How are you / your team using AI in your product development workflow today?
Very relevant question for the times we live in. I'm going to focus on how PMs at Atlassian are using AI to augment their workflow, since that has been a huge focus for us.
Refining your writing - Atlassian has a very strong written culture, especially within the PM team. This how we communicate ideas, strategies and influence stakeholders. It's no surprise that we leverage Confluence very heavily and the AI features help PMs go from rough draft to a polished document pretty quickly.
Consuming written communication faster via AI summaries - the flip side of having a strong writing culture is that you now have to consume a lot of written documents very quickly. AI driven summaries integrated in Confluence, Rovo Search & Chat are time savers.
Creating prototypes from PRDs - most of the time a PM spends with their teams is to drive clarity on what needs to be built and why. AI Tools like Bolt, v0, Lovable and Replit enable PMs enable PMs to quickly convert their PRDs into a working prototype without having any design or coding skills. It's way more efficient to align your team using a prototype rather than a written document (PRD)
Rapid iteration - we all know that testing prototypes with customers is a great way to validate ideas before writing any code. But it still take significant effort and know-how to create prototypes and iterate on ideas. Changes are made between user testing sessions rather than during the session. Prototyping with AI significantly shortens the time and effort that it's possible to make changes to the prototype during the user testing session with just 1 or 2 prompts.
I currently work in the intersection of enterprise security & AI and it is incredible to see the use cases that have emerged for AI in this space.
User research: As I mentioned in one of the earlier questions, AI tools can be a fantastic source to understand user trends, market, and competitive trends. For example, you can take a look at online user reviews for your product to understand key functionalities and usability gaps.
Product functionality: Within security SaaS we often use the framework of detect, investigate, and resolve. AI is changing each of these experiences from a product development perspective. For example within ‘detect’ AI is enabling us to develop product experiences which help organizations more proactively understand attacks their orgs are more vulnerable to. Leveraging machine learning and external data sources we can provide scores to attacks to help understand how significant a vulnerability truly is. Within remediation AI helps to develop automated playbooks based on other similar playbooks that can help users more quickly resolve issues and get external data about how other orgs are resolving the issue.
AI experiences: In addition to augmenting the security workflow to make it more productive and effective, gen AI is also enabling us to create net new experiences for prospects and customers. For example if an organization doesn’t have the security skillset to complete one of the tasks across detect/investigate/resolve - what is the role AI could play here in filling the gap? How can AI be leveraged to empower shift left security in an organization so that developers are encouraged to incorporate security from the get go in their designs?
There is so much potential for how AI can fundamentally change the product development process and excited to see all the innovation organizations bring to their products over the next two years.