In a recent LinkedIn post, Lenny Rachitsky shares key takeaways from a discussion with Aishwarya Naresh Reganti and Kiriti Badam regarding the unique challenges and strategies for building successful enterprise AI products. Rachitsky emphasizes that AI products fundamentally differ from traditional software, requiring new approaches to development and product management.
Fundamental Differences in AI Product Development
Rachitsky highlights that AI products are inherently non-deterministic and necessitate a constant balancing act between user agency and system control. He quotes the insights from Reganti and Badam, stating:
AI products differ from traditional software in two fundamental ways: they’re non-deterministic, and you need to constantly trade off agency vs. control. Traditional product development processes break when your product gives different answers to the same input and can do things on its own.
This non-deterministic nature, according to Rachitsky, means that standard product development processes are insufficient. The core design decision, as framed by Reganti and Badam, revolves around this agency-versus-control spectrum. Successful enterprise AI products, Rachitsky explains, typically fall in the middle, dynamically adjusting control based on factors like confidence scores and context.
Execution Over Model Performance
A significant point Rachitsky addresses is the common pitfall of blaming AI models for product failures. He relays Reganti and Badam’s observation that most AI product failures stem from execution missteps rather than inherent model limitations. As Rachitsky puts it:
Most AI product failures come from execution missteps, not model limitations. Aish and Kiriti see teams blame the underlying LLM when the real issue is unclear product scope, missing guardrails, or poor user onboarding.
Rachitsky advocates for auditing product design, evaluation coverage, and user flows before seeking improvements to the underlying model. He stresses that disciplined execution often trumps raw model performance.
Strategic Product Launch and Continuous Calibration
For initial AI product launches, Rachitsky, drawing from the discussion, recommends focusing on a narrow, high-value problem with strict guardrails. He cautions against attempting to build general-purpose assistants from the outset. Instead, he advises:
Your V1 AI product should solve a narrow, high-value problem with tight guardrails. Teams fail by trying to build a general-purpose assistant or agent on the first try.
Furthermore, Rachitsky underscores the critical importance of observability and logging for AI products due to their non-deterministic behavior. He also touches upon the concept of ‘continuous calibration,’ which replaces traditional iterative development cycles. This ongoing process of measuring real-world performance and adjusting prompts, guardrails, or model versions is essential to prevent silent degradation and user churn.
The Role of Evals and Monitoring
Rachitsky also clarifies the role of evals, noting that while necessary, they are not sufficient. He explains that evals measure performance on known test cases but fail to capture the full production experience. To address this, he suggests combining evals with continuous monitoring, user feedback loops, and robust observability tooling.
Ultimately, Rachitsky’s summary of Reganti and Badam’s insights provides a practical framework for businesses navigating the complexities of enterprise AI product development, emphasizing strategic execution, careful control balancing, and continuous adaptation.
📝 About This Content
This article is based on insights shared by Lenny Rachitsky on LinkedIn.
📅 Originally posted on January 12, 2026 | View original post on LinkedIn →