Avoiding the Feature Factory Trap with AI-Driven Insights
More Features ≠ More Value
Product managers often operate under constant pressure to deliver. Shipping new features quickly becomes the default measure of success—especially in high-growth environments. But when success is judged solely by what’s released, not whether it solves the right problems, teams fall into the feature factory trap.
It’s a common pattern. Teams ship at velocity, but outcomes remain unclear. According to a 2023 Product Management Insights report, 80% of features in enterprise software are rarely or never used. That’s a huge amount of wasted effort.
As Aakash Gupta notes in The Product Manager Podcast, this problem often emerges subtly:
“I actually think that all roads lead to the feature factory... Many times there is a bottoms-up process and a top-down process happening in parallel that’s invisible.”
It’s these invisible forces—conflicting priorities, lack of visibility, process inertia—that keep teams stuck. But with the right data, visibility improves. And that’s where AI-driven insights come in. When used thoughtfully, they can help shift product teams from an output-first mindset to an outcomes-first approach.
What Is a Feature Factory?
The term “feature factory” was coined in 2016 by product-thinker John Cutler and refers to organizations that prioritize delivery over discovery—shipping features without without understanding or measuring their impact. This phenomenon has also been referred to as the Build Trap (coined by Melissa Perri), feature creep, the launch-and-leave mentality, feature-led development, and plain old "shipping for the sake of shipping."
Gupta defines it simply:
“Feature factory: A software company focused on constantly building and releasing new features rather than building a product that users actually want.”
These teams often look productive on the surface. They ship frequently, have packed roadmaps, and meet their deadlines. But underneath, there’s little understanding of impact. Features are rarely retired. User feedback loops are weak or non-existent. And stakeholders often ask for the next feature before the current one has even been validated.
You might hear the signs in passing:
“It’s roadmap rinse-and-repeat.”
“We’re in reactive build mode.”
“Our backlog is a dumping ground.”
“We’re shipping artifacts, not solutions.”
They’re not just throwaway comments—they’re symptoms of a team stuck in a feature-first cycle, mistaking velocity for value.
If that sounds familiar, you're not alone—and you're not doomed. AI can make the invisible visible and give PMs data to lead with confidence. Let’s break down how.
Understanding this trap is the first step. The next is finding a more strategic way forward—one that prioritizes learning, user insight, and measurable value. That’s where AI can make a meaningful difference.
Reflection: Are You Operating Like a Feature Factory?
Before pointing fingers or revamping roadmaps, it’s helpful to pause and observe. Many high-functioning teams unknowingly slip into feature factory behaviors—not out of neglect, but because it’s the path of least resistance when deadlines loom and visibility is low.
Use the prompts below as a team reflection tool. You can treat them as conversation starters in your next retro or strategy sync. The goal isn’t to “score” well—it’s to notice where your habits might be out of sync with your goals.
| Prompt | Reflective Question |
| Measuring success | When we ship a feature, how do we know it worked? Do we celebrate delivery, or results? |
| Roadmap drivers | What determines what gets built next—customer behavior, business outcomes, or internal urgency? |
| Feature lifecycle | When was the last time we removed or meaningfully iterated on a feature after launch? |
| User feedback loop | How often do we hear directly from users after a release? Is that feedback shaping what comes next? |
| Cross-team visibility | Can everyone on the team articulate why the current priorities matter to the business or user? |
| Learning velocity | Are we learning as fast as we’re shipping? How often do we revisit what we’ve already built? |
Rather than a rigid self-diagnosis, this reflection is meant to invite honest discussion. If even a few of these questions spark discomfort, that’s not failure—it’s a useful insight. Many teams operate in this way simply because they don’t have access to the right data, patterns, or feedback loops.
That’s where AI-driven insights can help—not by replacing product judgment, but by strengthening it. They give teams the clarity and confidence to prioritize what matters, revisit what doesn’t, and move forward with purpose.
How AI-Driven Insights Can Help
1. AI-Powered Prioritization: Narrow Down On What Actually Matters
Prioritization has always been one of the toughest parts of product management. Traditional methods often rely on intuition, internal requests, or prioritization frameworks that don’t capture real user behavior. AI changes that by giving teams a clearer picture of what’s working, what’s not, and where the real opportunities lie.
Through clustering, usage analysis, and natural language processing, AI can surface patterns across millions of data points—helping teams understand what features are driving retention or frustration. For example, Spotify segments users based on listening behavior and churn risk, allowing product managers to focus on features like personalized playlists that directly impact engagement.
Adobe uses its AI engine, Sensei, to analyze which tools inside Creative Cloud are most frequently used—and by whom. These insights inform where the team invests in improvements, rather than adding new tools that dilute the core value.
When teams use AI to guide their priorities, the result isn’t just smarter decision-making—it’s better alignment with actual user needs.
2. Predicting Feature Success Before Launch
Forecasting what users will love (or ignore) is notoriously difficult. Even the best product teams have launched features that seemed promising, only to discover low adoption or friction post-release. AI helps de-risk this process by bringing simulation and prediction into the development cycle.
Predictive analytics models trained on historical user behavior can forecast adoption rates, engagement levels, or potential churn triggers. Companies like Netflix use these models to test how interface changes will impact user flow—before the changes go live.
Similarly, Amazon leans heavily on AI to manage large-scale A/B tests. Their experimentation engine can test multiple variants in real-time and automatically sunset underperforming ones. This allows them to fine-tune features for specific cohorts and avoid rolling out changes that don’t move the needle.
Instead of relying solely on gut instinct or stakeholder influence, teams using AI can approach product launches more like pilots—learning quickly and adapting based on real signals.
3. Automating Decision Intelligence for Continuous Feedback Loops
One of the subtle traps of the feature factory is the assumption that shipping a feature is the finish line. In reality, it’s just the start. AI helps make post-launch analysis and iteration more automatic, surfacing insights that would otherwise be missed.
For example, Shopify uses AI to monitor how merchants interact with newly released features. If usage drops or task completion slows, the system recommends improvements—or even removal. It’s not about perfection at launch; it’s about responsiveness after launch.
LinkedIn applies a similar approach to content-sharing features. Their AI tracks which formats perform well and feeds that data back into ranking algorithms, which dynamically adapt to promote more engaging interactions.
As Aakash Gupta notes, many product decisions are influenced by invisible dynamics between teams and stakeholders. AI can reveal those patterns by tracking how decisions translate to outcomes. That visibility enables teams to course-correct early, instead of compounding costly missteps.
In this sense, AI isn’t just a decision support tool—it’s a feedback engine for continuous product learning.
How to Get Stakeholder Buy-In for AI-Driven Product Strategy
1. Frame AI as a Business Enabler
One of the biggest challenges in adopting a more data-driven approach is securing leadership support. Executives may be intrigued by AI, but they’re often skeptical unless it ties directly to strategic goals. When making the case:
- Tie AI to revenue → Predict which features will drive upsells or reduce churn
- Tie AI to efficiency → Reduce wasted engineering effort on features users won’t adopt
- Tie AI to risk mitigation → Run pre-launch simulations to prevent flops, reducing costly failures.
Instead of pitching AI as a novel capability, position it as a way to de-risk investment, reduce development waste, and make better bets.
2. Speak the Language of Leadership
Instead of saying:
“We want to use AI to optimize prioritization.”
Say:
“We can reduce engineering waste by 20% by predicting feature performance before development begins.”
When teams translate AI insights into business terms, conversations shift. What might have felt like a technical pitch becomes a strategy discussion—one where everyone at the table wants to play a part.
Real-World Applications: How Leading Companies Use AI to Avoid the Trap
Across industries, product teams are integrating AI not to replace human judgment—but to sharpen it. These companies offer useful examples of how to escape the feature factory mindset:
- Airbnb uses AI to refine search results and dynamic pricing, ensuring that any new features in the booking experience align with guest preferences and behavior. Rather than layering on filters or interface changes, they focus on improving discovery and trust.
- Tesla takes an iterative approach to vehicle software, using over-the-air updates informed by real-world driving data. Features like Autopilot improve continuously based on machine learning models trained on user behavior.
- Duolingo personalizes the learning experience using AI models that adapt content difficulty based on individual progress. This ensures that feature updates contribute to improved learning outcomes—not just more options in the app.
- Salesforce Einstein uses AI to identify which CRM automations drive the greatest sales efficiency. Product teams use these insights to prioritize new automation features and to sunset underused ones.
- Adobe applies insights from its Sensei platform across Photoshop and Premiere Pro. Instead of adding features based on internal intuition, the company refines existing tools to match how users actually work.
Each of these examples reflects a shift in mindset: from feature delivery to user value. The AI isn’t doing the work for them—it’s helping them do the right work.
Moving Forward: Better Feature Investments
There’s no one-size-fits-all solution to the feature factory problem. But product teams have more tools than ever to push toward clarity. AI is one of them.
It can help identify what matters, predict what’s likely to work and learn from what already has. But perhaps more importantly, it can make the invisible visible—helping teams see beyond the roadmap and into the real impact of their work.
Start small. Audit your roadmap. Ask tough questions about what’s been delivered and what value it created. Then explore how AI might support—not replace—your product judgment.
The goal isn’t to ship more. It’s to ship smarter.
Next Steps
- Audit your roadmap for “feature factory” patterns
- Explore AI tools for product analytics and forecasting
- Watch our webinar: Do All Roads Lead to the Feature Factory?
