Instead of inventing new features, Prepared identified its most lucrative expansion opportunity by seeing users' painful workarounds. They noticed 911 dispatchers manually copy-pasting foreign language texts into Google Translate—a clear signal of a high-value problem they could solve directly.

The critical flaw in most sales tech is its failure to correlate rep behavior with performance outcomes like quota attainment. The real value is unlocked not just by knowing what reps do, but by connecting those actions to who is succeeding, thus identifying true winning behaviors and separating A-players from C-players.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

A critical error in AI integration is automating existing, often clunky, processes. Instead, companies should use AI as an opportunity to fundamentally rethink and redesign workflows from the ground up to achieve the desired outcome in a more efficient and customer-centric way.

Assembled knew they had a real business when they discovered that Stripe, Casper, and Grammarly—all unaware of each other's efforts—had independently built the same color-coded spreadsheet to solve workforce management. This pattern of convergent, homegrown solutions signals a powerful, unmet market need.

Run HR, finance, and legal using AI agents that operate based on codified rules. This creates an autonomous back office where human intervention is only required for exceptions, not routine patterns. The mantra is: "patterns deserve code, exceptions deserve people."

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Before building a complex feature, validate its value by manually creating the desired output for customers. The Buildots team used Excel to generate performance insights from their data. Only after seeing customers act on these manual reports did they productize the feature.

Users exporting data to build their own spreadsheets isn't a product failure, but a signal they crave control. Products should provide building blocks for users to create bespoke solutions, flipping the traditional model of dictating every feature.