How To Scale AI In Regulated Industries

The Claude 3.7 Revolution: Why AI Transformation Stalls (And How I Fix It) "Your AI strategy isn’t failing because of technology it’s failing because of how decisions get made." Claude 3.7 is making headlines. But inside Fortune 50 boardrooms where I lead transformations, AI investments are quietly stalling: • Millions spent on AI infrastructure but no real impact. • AI governance frameworks exist but slow execution to a crawl. • Executives are frustrated they’ve invested, so why isn’t AI delivering results? The Hidden AI Roadblocks in Regulated Industries > Decisions stall. McKinsey: unclear AI governance and decision rights are top barriers to implementation. > Risk is misdiagnosed. What looks like compliance hesitation is actually a lack of clear AI decision structures. > Implementation fails in silence. PwC: 70% of companies call AI a priority, but only 15% have identified roadblocks. Where AI Adoption Breaks Down • AI in regulated industries fails when governance = delay. • Executives keep the vision and delegate execution when they should keep execution accountability and delegate vision creation. This is how AI gets stuck in bureaucracy instead of driving results. How I Fix AI Decision Paralysis in Regulated Industries > Accelerated AI approvals. Governance shouldn’t mean 24-day review cycles. I help organizations establish 24-hour decision windows within compliance guardrails. > Clear AI accountability. Who approves, escalates, and acts? High-performing organizations define it upfront eliminating bottlenecks. > Front-line empowerment (within oversight). AI success happens where decisions are made, not just in governance committees. Proof? DM me. • Scaled AI-driven fraud detection by aligning decision-making with risk oversight not against it. • Structured AI approvals for faster, more accurate decision-making. • Enabled real-time AI execution while staying aligned with strict regulations. This is the exact approach I bring to organizations in finance, healthcare, and other highly regulated sectors. Are you building AI strategy for execution or just for compliance checkboxes? Let’s discuss. What is your team waiting on that could be made in 24 hours instead of 24 days?

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐚 𝐠𝐚𝐭𝐞 𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐀𝐈 𝐬𝐜𝐚𝐥𝐞. Most teams bolt governance on. Then wonder why scaling stalls. The shift: 𝐃𝐞𝐬𝐢𝐠𝐧 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. → Policies as Code Hard-code data boundaries, approvals, and retention. No PDFs on SharePoint. → Evaluation Harnesses Test safety, bias, drift, and instruction-following before release continuously. → Observability Trace every decision: inputs, tools, and model versions audits in hours, not weeks. Change Management Bake in gates, rollout plans, and feature flags. 𝐂𝐚𝐬𝐞 𝐢𝐧 𝐩𝐨𝐢𝐧𝐭: A bank deployed onboarding agents under regulatory scrutiny. ↳ Policies-as-code enforced KYC + disclosures automatically. ↳ Eval harness caught risky prompts pre-production. ↳ Deployment time dropped 60%. ↳ Incidents trended toward zero. Result? Governance wasn’t friction it became the feature buyers trusted most. Ready to turn governance from blocker into competitive advantage? ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #GenerativeAI #EnterpriseAI #AIProductManagement #LLMAgents #ResponsibleAI