Saying yes to numerous individual client features creates a 'complexity tax'. This hidden cost manifests as a bloated codebase, increased bugs, and high maintenance overhead, consuming engineering capacity and crippling the ability to innovate on the core product.
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Features follow an S-curve of value. Early effort yields little, then a steep rise, then diminishing returns. Use this model to determine if a feature needs more investment to become valuable or if you've already extracted its maximum worth and should stop investing.
When a team presents a timeline that feels instinctively too long, trust that gut feeling. It likely signals an over-engineered solution. Complex systems never become simple; they only breed more complexity, causing timelines to expand endlessly. It's better to reset the team or the approach early on.
For incumbent software companies, an existing customer base is a double-edged sword. While it provides a distribution channel for new AI products, it also acts as "cement shoes." The technical debt and feature obligations to thousands of pre-AI customers can consume all engineering resources, preventing them from competing effectively with nimble, AI-native startups.
When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.
Avoid the trap of building features for a single customer, which grinds products to a halt. When a high-stakes customer makes a specific request, the goal is to reframe and build it in a way that benefits the entire customer base, turning a one-off demand into a strategic win-win.
Many B2B companies begin by customizing software for one client, then stacking new custom projects for subsequent clients. They believe they are building a product, but are actually creating a complex, unscalable monolith that is difficult to maintain and evolve.
Creating feature "modes" (e.g., "uphill mode") instead of exposing core mechanics (e.g., gears) creates a "nightmare bicycle." It prevents users from developing a general framework, limiting their ability to handle novel situations or repair the system.
A single roadmap shouldn't just be customer-facing features. It should be treated as a balanced portfolio of engineering health, new customer value, and maintenance. The ideal mix of these investments changes depending on the product's life cycle, from 99% features at launch to a more balanced approach for mature products.
Instead of debating individual features, establish a clear "perspective" for your product. Artist's perspective as a "push-based product for quick insights" makes it easy to reject requests that don't align, like building an in-house video hosting tool. This aligns the entire organization and simplifies the roadmap.
When teams constantly struggle with prioritization, the root cause isn't poor backlog management. It's a failure of upstream strategic filters like market segmentation, pricing, and product discovery. Without these filters, the feature list becomes an unmanageable mess of competing demands.