David Stifter spent 20 years as head of technology at Colony Capital, managing systems for a $60 billion private equity real estate firm. When a longtime AP specialist retired, the company lost its institutional knowledge for coding complex invoices across thousands of entities and tenant relationships. After a year evaluating RPA, template-based approaches, and early OCR solutions, David recognized that structured historical data—invoices paired with their coding—could train AI models to capture implicit business rules. Five years ago, at 40 with young children, he left his executive role to build PredictAP. The company now processes tens of thousands of invoices monthly for firms including Bridge Investment Group, demonstrating how operational expertise combined with AI can solve problems that pure technology approaches miss. Topics Discussed Identifying AI use cases with structured annotated data and human feedback loops  Moving from CTO buyer to vendor founder and discovering which networks actually convert  Building repeatable sales motion after exhausting warm introductions  Technology adoption barriers in real estate and the domain expertise requirement for vertical SaaS  Hiring sales leadership to scale from founder-led to systematic pipeline generation  Solving complete workflow integration challenges beyond isolated technical problems GTM Lessons For B2B Founders Match technical approach to problem structure, not trend: David identified three critical elements for his AI application: structured annotated data from historical invoice coding, recognizable patterns in implicit business rules, and human review as a feedback mechanism. He notes many founders "try to shove AI, the AI hammer to smash any nail, but they're not always the best use case." Six years ago, before modern LLMs, he used historical invoice-coding pairs as training data—solving the annotation problem that plagued early machine learning. Founders should evaluate whether their problem has the structural characteristics that make a given technology approach viable, rather than applying trending solutions to force market fit. Network quality reveals itself when you need something: David contrasts two early investors: a former acquisitions executive who promised extensive connections but delivered "not a single callback" after leaving their role, versus an asset manager who generated "hundreds" of leads through genuine relationships. The acquisitions person experienced "an existential crisis" realizing "my network was based upon my ability to have a massive checkbook behind me." Founders should recognize that network strength isn't tested until you're asking rather than giving—those who built relationships through consistent helpfulness rather than transactional power will see different response rates when they launch. Architect the founder-led to systematic sales transition: After two years of founder-led sales, David "hit that wall" and brought in Steve Farrell, prioritizing experience scaling from $3-5M to $20M ARR over industry-specific expertise. He notes warm intro calls are "very to the point" while cold outreach "starts hostile or skeptical"—requiring entirely different trust-building approaches. The shift required adding BDRs, AEs, and systematic content generation. Founders should hire sales leadership with specific stage experience before network depletion forces reactive hiring, and expect to rebuild positioning for skeptical buyers who lack pre-existing trust. Integrate solutions into existing workflow infrastructure: David emphasizes the failure mode of optimized point solutions: "They have a perfect solution from the technical problem but it's not going to work for this firm because it's not going to fit into their workflow." He maps the complete experience including integration with existing systems, training requirements, user experience, consistency, and speed. Technical superiority in isolation leads to "problems with adop