When knowingly incurring tech debt to meet a deadline, trust between product and engineering is key. Don't just hope to fix it later; establish a formal agreement for an 'N+1 fast follow-up' release. This ensures time is explicitly scheduled to address the shortcuts taken.
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Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.
Engineering often defaults to a 'project mindset,' focusing on churning out features and measuring velocity. True alignment with product requires a 'product mindset,' which prioritizes understanding the customer and tracking the value being delivered, not just the output.
To overcome the paralysis of perfectionism, create systems that force action. Use techniques like 'time boxing' with hard deadlines, creating public accountability by pre-announcing launches, and generating financial stakes by pre-selling offers. These functions make backing out more difficult and uncomfortable than moving forward.
To get product management buy-in for technical initiatives like refactoring or scaling, engineering leadership is responsible for translating the work into clear business or customer value. Instead of just stating the technical need, explain how it enables faster feature development or access to a larger customer base.
Instead of over-analyzing and philosophizing about process improvements, simply force the team to increase its cadence and ship faster. This discomfort forces quicker, more natural problem-solving, causing many underlying inefficiencies to self-correct without needing a formal change initiative.
When hypergrowth causes you to fail internal stakeholders (like Operations), apologies are insufficient. Rebuild trust by going to the CEO and board *together* with the slighted team to advocate for a drastic roadmap pivot that prioritizes their needs, demonstrating true commitment to their success.
Instead of fighting for perfect code upfront, accept that AI assistants can generate verbose code. Build a dedicated "refactoring" phase into your process, using AI with specific rules to clean up and restructure the initial output. This allows you to actively manage technical debt created by AI-powered speed.
Instead of arguing for more time, product leaders should get stakeholder buy-in on a standardized decision-making process. The depth and rigor of each step can then be adjusted based on available time, from a two-day workshop to an eight-month study, without skipping agreed-upon stages.
The misconception that discovery slows down delivery is dangerous. Like stretching before a race prevents injury, proper, time-boxed discovery prevents building the wrong thing. This avoids costly code rewrites and iterative launches that miss the mark, ultimately speeding up the delivery of a successful product.
