Productboard says better specs, not faster coding, drive AI software success

By AI, Created 1:21 PM UTC, June 01, 2026, /AGP/ – Productboard argues that product discovery and customer-grounded specifications are becoming the key to better AI-assisted software development. The Business Reporter article says companies that skip discovery risk shipping faster, but not shipping better.

Why it matters: - AI coding tools are helping teams ship software faster, but faster delivery is not automatically producing better business outcomes. - Product discovery is the step that helps teams validate real customer needs before they commit to building features. - Strong specifications now matter more because AI-native development can turn vague requirements into flawed products very quickly.

What happened: - Business Reporter published an article featuring Productboard’s view on product discovery and feature design. - The article says product discovery is the iterative process of understanding customer needs, validating core problems and testing solutions before building the final product. - The piece argues that product teams should focus on features that reflect what customers actually need.

The details: - Recent research cited in the article shows 63% of engineering organizations now deploy code more frequently using AI tools. - The same research shows 45% of AI-assisted deployments result in problems. - The research also shows 72% of AI-assisted deployments have led to production incidents. - The article says the core problem is often flawed product discovery, not coding quality. - Many organizations still rely on internal assumptions, expert opinions and business cases built to justify pre-made decisions. - Product leaders are using discovery to find bottlenecks, unnecessary handoffs and gaps between customer insight and product delivery. - That approach helps teams decide which steps need human judgment and which can be automated. - The article says this can compress discovery timelines without sacrificing quality. - In AI-native development environments, specifications have become increasingly critical. - Daniel Hejl, co-founder and Chief AI Officer at Productboard, said: “Everything has to be in the spec. Pricing plan, analytics events, onboarding impact. These are not details you resolve in a hallway conversation when your coding agent is building at three in the morning.” - Specifications need to stay grounded in real customer evidence. - Missing elements should be identified early, before development reaches engineering teams.

Between the lines: - The message is that AI can accelerate execution, but it cannot replace product judgment. - Companies that treat discovery as a formality risk building faster around the wrong problem. - The push for tighter specs reflects a broader shift toward evidence-based product management in AI-driven workflows.

What’s next: - Product teams are likely to keep investing in discovery processes that reduce rework and improve feature relevance. - More organizations may adopt AI tools for synthesis and delivery while keeping human oversight on customer evidence and product definition. - Readers can learn more in the article and review Productboard’s platform.

The bottom line: - AI speeds up code production, but customer-grounded discovery and precise specifications are what make the output useful.