📍TLDR
In this conversation, I interview Grégoire Charles, the former Head of Product at eFounders, about how he’s leveraging AI in product development.
We discuss Grégoire’s favorite AI tools, workflows he’s automated, and tips and tricks for effective prompt engineering.
Grégoire explains how AI can help automate feedback loops with users and improve product adoption and activation using automated workflows he built on Make.com & ChatGPT integrations.
Grégoire also discusses other AI-integrated tools he has tried, such as Cycle, Chat PRD, and wireframing tools, and the challenges of integrating AI into visual design workflows.
Grégoire Charle
Grégoire was the Head of Product at Hexa (formerly eFounders) before becoming an angel investor with Mozza Angels, a dedicated group of product entrepreneurs and operators investing in the startups of tomorrow.
He now advises early-stage startups on product development, UX/UI, and PM operations.
AI in Product Management
Picture this: you're a product manager juggling multiple projects, trying to keep up with user feedback, and constantly iterating on your product. It's a demanding role that requires a wide range of skills and a lot of time. But what if you could harness the power of AI to streamline your processes and make your life easier? That's where tools like ChatGPT come in.
As Grégoire explains, "There's a ton you can do with such a tool, especially with the ability to structure content, to write stuff down in a way that you would take ages to do." By leveraging AI, product managers can automate repetitive tasks, generate high-quality content, and gain valuable insights from user feedback.
Real-world examples of AI-powered product management successes are popping up everywhere. From automating feedback loops to generating release notes, AI is proving to be a game-changer in the field.
Automating Feedback Loops and User Communication
One of the most time-consuming aspects of product management is managing user feedback. It's a crucial part of the job, but it can be overwhelming to sort through countless emails, social media comments, and support tickets. This is where AI can make a huge difference.
Grégoire shares his experience implementing an AI-driven feedback loop system:
"We have tons of users every day sending you feedback on social networks, on, by email using your, your intercom or internal form or whatever, and all those feedbacks [...] AI can help also on categorizing and enhancing those feedback, challenging those things and, and making sure that the, the item that you have in front of your eyes in the end is better than the initial content."
By automating the collection and categorization of user feedback, product managers can save countless hours and focus on what matters: making data-driven decisions to improve their products.
Leveraging AI for Product Documentation and Release Notes
Another area where AI shines is in the creation of product documentation and release notes. Writing these materials can be a tedious and time-consuming process, but with tools like ChatGPT, it doesn't have to be.
As Grégoire explains,
"I spend time writing specs. So as a product, I'm trying to do my best to write proper specs with context, why we're doing this. [...] And, and I, I've always felt like writing release notes, for instance, in the end, after you've done the, the wall work is more or less the same job."
By using AI to generate release notes and user-facing content, product managers can save time and ensure consistency in their communication. However, it's essential to master prompt engineering to get the best results. Grégoire shares his experience:
"I'm bad at prompting and I can, and sometimes I'm even asking myself whether I would be faster at actually writing the content I need to write rather than trying to improve the prompting."
Integrating AI into Product Management Workflows
Incorporating AI into your product management workflow can seem daunting at first, but with the right approach, it can be a seamless process. The key is to identify the areas where AI can have the most significant impact and start small.
Grégoire suggests starting with ChatGPT's user interface to see if you can make something better, then gradually automating processes as you gain confidence in the tool's capabilities. It's also essential to balance AI-powered automation with human oversight and judgment. As Grégoire puts it,
"It's not plug and play. You just don't grab the content and send it to your users without even reviewing it."
The Future of AI in Product Design and Wire-framing
While AI has made significant strides in text generation, its application in visual design and wireframing is still in its early stages. Grégoire, a self-proclaimed "visual guy," has been exploring AI-powered design tools but admits that they haven't quite met his expectations yet.
"I would have loved to find a relevant, suitable tool to help me build wireframes. [...] But yeah, these are tools I'm trying a lot, but maybe I'm not using them the right way. But I always feel a bit disappointed about the output."
However, as AI technology continues to advance, we can expect to see more powerful and intuitive design tools emerge.
The main improvement we hope to see is the ability to easily edit and iterate first drafts.
For example, it’s hard to create an on-brand image in Dall-E and create multiple images in the same style. Consistency and iterating have been notoriously difficult with image and video generation models. But this is changing.
Dall-E by OpenAI recently announced features to edit images and we’ve seen video generation models help create consistent characters for video games. As we start to see this spill into more B2B use-cases visual asset generation like wireframing will become easier and easier.
Conclusion
AI is impacting every industry and product development is no exception. Grégoire demonstrates how starting off in ChatGPT, learning how to improve prompts, and then starting to automate processes once you’ve gotten consistent results is the way to go.
An incremental approach to adopting AI.
Grégoire was able to implement a full feedback loop system by automating the analysis of customer feedback as well as the communication of new product features. This is just one of many use cases you could implement with tools like Make.com, ChatGPT, Zapier, and more.
"As long as you have to follow up with more than 15 to 20 people per week. It's almost impossible to do it manually."
By embracing AI as a powerful tool and continuously honing our prompt engineering skills, product managers can stay ahead of the curve and create better products for their users.
So, are you ready to take your product management game to the next level with AI?
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