When Your Backlog Writes Itself
Summary
Community ideas and analytics can fill your backlog automatically, if you manage the noise.
Introduction: Imagine opening your product management tool on Monday morning to find a neatly populated backlog of feature ideas, bug fixes, and improvements - all prioritized and ready to go - without you having typed a single item. It sounds like magic, but in today’s world of AI and engaged user communities, a self-writing backlog is increasingly plausible. Between AI analysis and community contributions, product teams are discovering that backlogs can “write themselves.” In this post, we’ll explore how this happens, and why it’s both a boon and a challenge for product managers.
Sources of the Self-Writing Backlog: There are two main forces that can auto-generate backlog items: your users and your data (with a little AI help). On the user side, enthusiastic communities often continuously suggest features and improvements. Through forums, feedback portals, and social media, users are essentially writing your backlog for you by voicing what they want. Many modern products openly solicit ideas from their user base - consider how every popular app forum has endless “Wouldn’t it be great if…?” posts. With the right platforms in place (like an in-app feedback widget or a public idea board), idea collection becomes democratized. Anyone can add to your backlog, not just your internal team. On the data side, AI can mine usage analytics and user feedback to propose backlog items. For example, if your SaaS product’s telemetry shows many users clicking repeatedly on a non-existent menu item, an AI might infer there’s demand for something and create a “Make X feature accessible from menu” story. Tools powered by generative AI can even transform raw user feedback into structured tasks. In fact, a McKinsey study observed product managers using GPT-4 to draft product requirements and backlog entries, significantly speeding up the planning phase . The AI was able to synthesize user research and create product backlogs in the build phase, effectively automating what used to be hours of PM work .
Quantity does not equal quality - curation is still required.
Benefits of an Auto-Generated Backlog: A backlog that fills itself can be a dream in many ways. It means you have a constant pulse on what users want or need. You’re less likely to miss a great idea that an insular team might overlook. It also frees up a product manager’s time; instead of spending days on discovery or requirements writing, you can focus on higher-level strategy and decision-making. When users see their ideas showing up as planned features, it builds goodwill and engagement - they feel heard. Additionally, an AI-curated backlog might detect patterns we don’t, surfacing non-obvious improvements (for example, noticing that users who use Feature A heavily also ask for Feature B, suggesting a linkage). With execution becoming cheaper and faster thanks to AI (we can build and ship quicker), having a rich backlog of ideas is valuable; there’s always something vetted to work on next, avoiding idle cycles. It’s like having a suggestion box that instantly turns into a to-do list.
The Downside: Overwhelm and Noise: The flip side of a self-populating backlog is overwhelm. Not every idea is a good idea, and even good ideas may not align with your strategy. Left unchecked, you might wake up to a backlog with 500+ items, an unmanageable mix of trivial requests, edge-case bugs, and wild feature ideas. Quantity does not equal quality. Product teams must be careful to avoid becoming short-order cooks for the loudest voices. As open innovation experts have noted, a large volume of crowdsourced ideas can be difficult to systematically evaluate and filter . You’ll need mechanisms to sift the gold from the dross. This is where both AI and community governance can help. Some companies use machine learning to cluster similar suggestions and identify those with the highest frequency or sentiment score. Others rely on the crowd to do some filtering, via upvote systems or discussion threads that vet ideas. Still, the onus often falls on the product manager to apply strategic filters. Remember the caution from Aha! (a product management platform): “Brilliant ideas can come from anywhere. But you need to understand which ones will make a real impact. Without an underlying product strategy, the onslaught of ideas and requests can lead to a disjointed product.”  In short, if your backlog is writing itself, you must still curate it. This means regularly reviewing auto-generated items and pruning those that don’t fit your vision or goals.
Keep the list focused, not flooded.
Making the Most of It: To harness an self-writing backlog effectively, consider these tips:
- Set Clear Objectives: Communicate your product vision and current goals to your community and even to your AI systems. If your users know you’re focused on, say, improving onboarding this quarter, their suggestions are more likely to be relevant. Likewise, an AI prioritization model can weigh ideas that align with stated objectives more heavily.
- Implement Voting or Scoring: Use a voting mechanism for community suggestions. Ideas that resonate will rise to the top, effectively letting the crowd highlight what matters most. Companies like LEGO require 10,000 community votes for an idea to even be considered for production , ensuring only popular requests consume roadmap attention. This kind of threshold or reputation system keeps the backlog focused on impactful requests.
- Regularly Triage with Humans: Schedule routine backlog grooming where a human PM (and team) reviews what’s come in. Group duplicates, merge related ones, and eliminate the chaff. Look for the signal in the noise: an idea might not be taken as-is, but five different user suggestions could point to the same underlying need.
- Close the Loop: If an item was added by AI or a community member and you decide not to pursue it, consider letting users know (in broad terms) why. Maybe the idea was great but not feasible technically, or not in line with the current strategy. Closing the feedback loop maintains trust and helps educate the community on the product’s direction, hopefully improving future suggestions.
Conclusion: A self-writing backlog is a double-edged sword. On one side, it’s a powerful validation that your users care enough to contribute, and that your data is rich enough to constantly inform new improvements. It can keep your development pipeline brimming with ideas and ensure you’re always working on something that someone out there wants. On the other side, it demands disciplined product management. Without intentional steering, you risk serving a cacophony of requests rather than a coherent product strategy. The best approach is to embrace the wealth of input - let your community and AI give you that firehose of suggestions - while confidently applying your vision and values as a filter. Use the gift of an auto-generated backlog to spark creativity and uncover gems, but remember that leadership is required to decide which of those gems to polish and which to leave aside. In the end, whether written by an AI, a user, or a PM, a backlog is only as good as the judgment that prioritizes it.
Key Takeaways
- Ideas come from users and data
- PMs must still curate the backlog
- Clarity beats quantity
Curate, don't just collect.
