Companies believe AI isn't delivering because technology moves too fast, so they invest in training and agile frameworks. The real, invisible problems are structural: ambiguous decision rights, siloed data ownership, and misaligned employee incentives. Solving for 'speed' when the foundation is broken guarantees failure.

Relying on customer interviews creates a false sense of understanding. The context gap between an interviewer and a customer living their job is too massive to bridge with questions alone. This leads to building products based on flawed, incomplete information.

Instead of relying solely on regulation, the market can self-correct. An exploitative company creates 'blocked demand' by mistreating its customers. This presents a massive opportunity for a new entrant to win by simply serving those customers better and unblocking their progress.

The nascent AI agent ecosystem lacks effective discovery mechanisms for third-party tools ('skills'). This creates an opportunity for curated marketplaces that help users find, vet, and even pay for high-quality, trustworthy agent capabilities, solving a key bottleneck to adoption.

Rushing to market without data-driven pricing research is not being agile; it is a form of professional negligence. This approach prioritizes the appearance of speed over the sustainable creation of value, setting the product up for failure from day one.

Unlike other software, security product value is hard to prove. If a tool finds nothing, it's unclear if the tool failed or if there were no issues to begin with. This shared uncertainty for both buyer and seller makes it difficult to assess true value.

An expert reveals two shocking statistics: 80% of new founders fail their first diligence attempt, and 85% of early-stage investors don't perform confirmatory diligence. This highlights a massive, systemic weakness and inefficiency in the startup ecosystem, creating significant risk on both sides of the table.

Enterprises often default to internal IT teams or large consulting firms for AI projects. These groups typically lack specialized skills and are mired in politics, resulting in failure. This contrasts with the much higher success rate observed when enterprises buy from focused AI startups.

AI agents will constantly analyze and switch services (from databases to financial products) based purely on performance and cost. This eliminates brand and marketing moats, forcing companies to compete solely on objective product quality.