Vendors pitch $4.7M platform replacements to unlock AI capabilities. But you don’t need a rip-and-replace strategy. David Ohnstad shows how smart teams layer AI functionality onto existing stacks—saving millions while avoiding implementation freezes.
A 94% accurate model means nothing if nobody defined what business outcome it should drive. David Ohnstad exposes the critical gap between data science validation and executive expectations that derails nine months of enterprise AI development.
Most teams believe AI requires tearing out legacy infrastructure. David Ohnstad explains why this myth costs companies weeks of debate and delayed shipping. Your existing SaaS foundation can support enterprise AI—here’s how.
Most enterprise AI initiatives stumble not because of poor models, but because teams skip the critical step of defining what success actually looks like. Without clear business outcomes first, even production-ready ML systems deliver no value.
Most organizations drastically underestimate the true cost of deploying AI agents at scale. Discover the four myths about API optimization, infrastructure, and monitoring that turn $50K budgets into six-figure bills—and how to prevent it.
We built an AI-powered vendor risk platform to automate 340 security questionnaires. Fourteen months later, we deprecated it entirely. Here’s what we learned about when enterprise AI fails, and the hidden costs of automation theater.
We built an AI-powered vendor risk tool with impressive metrics—then watched 89% of users ignore it for manual checklists. The problem wasn’t the model. It was how we approached enterprise adoption. Here’s what we learned.
We built an AI anomaly detector that worked perfectly—until 89% of users disabled it on first login. The problem wasn’t accuracy. It was inserting sophisticated prediction into a workflow that never needed it. Here’s why most enterprise AI pilots never escape the PoC phase.
