The notion of plug-and-play enterprise software is a fallacy. For decades, large software implementations have secretly relied on extensive services from firms like Accenture for configuration. GenAI simply makes this reality transparent, requiring customization upfront rather than dressing it up as a simple software sale.

When a startup pivots, it often adapts its existing software instead of rebuilding. This leads to a convoluted codebase built for a problem the company no longer solves. This accumulated technical debt from a series of adaptations can hobble a company's agility and scalability, even after it finds product-market fit.

Assembled initially replaced a manual spreadsheet process. Their success came from understanding the spreadsheet was a symptom of deeper pains like headcount planning, real-time dashboards, and agent utilization. The real value was in solving these complex operational problems, not just digitizing a spreadsheet.

The frantic scramble to assemble data for board meetings isn't a sign of poor planning. It's a clear indicator that your underlying data model is flawed, preventing a unified view of performance and forcing manual, last-minute efforts that destroy team productivity and leadership credibility.

AI models are not an immediate threat to Excel because they are designed for approximation, not the precise computation required for financial and data analysis. Their 'black box' nature also contrasts with a spreadsheet's core value proposition: transparent, verifiable calculations that users can trust.

Retrofitting systems and standardizing incentive plans across a 1,400-person organization is immensely difficult. The key lesson is to implement enterprise-grade systems (like an ERP) and standardized processes when your company is still tiny. It's exponentially harder and more expensive to fix these issues at scale.

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.

Veteran tech executives argue that evolving a business model is much harder than changing technology. A business model creates a deep "rut" that aligns customers, sales incentives, and legal contracts, making strategic shifts (like moving from licensing to SaaS) incredibly painful and complex to execute.

Many companies focus on AI models first, only to hit a wall. An "integration-first" approach is a strategic imperative. Connecting disparate systems *before* building agents ensures they have the necessary data to be effective, avoiding the "garbage in, garbage out" trap at a foundational level.