AWS structured itself as independent P&Ls, like AWS Marketplace. This autonomy allowed teams to make decisions and ship code with startup-like speed, avoiding corporate bureaucracy and fostering rapid innovation, even within a large organization.
AI tools that automatically write applications often pull assets from open-source libraries. This creates a massive security risk, as these agents must be explicitly directed to use secure, vetted repositories to avoid introducing vulnerabilities at scale without human oversight.
For complex AI solutions, a "fewer but deeper" partner strategy is more effective than a wide, transactional channel. This focus enables co-learning and true solution-selling with select partners, which is critical in a dynamic market where customer needs are still being discovered.
The high failure rate (87%) of AI proofs-of-concept isn't about the model's quality. It's because underlying system dependencies of the POC environment don't match production, and CISOs block deployment due to vulnerabilities from unvetted open-source components used during experimentation.
It is acceptable for an AI application to "fail" by not delivering its intended business outcome (e.g., ROI). This is part of the learning process. However, it is unacceptable to fail on security. Vulnerability management is non-negotiable and poses a direct risk to the company.
Inspired by ESG's Scope 3, which assesses supplier impact, building secure AI requires preemptively vetting the entire software supply chain. Companies must treat open-source packages and dependencies as suppliers, ensuring every component is secure from the start, rather than reactively scanning for flaws.