AI ROI for Fiduciaries

Everyone's watching for AI to change what advisors do. The real change is in what they no longer have to do. The conversation about AI in wealth management often focuses on flashy use cases such as predictive analytics, robo-advisors, or algorithm-driven rebalancing. When in reality, the most transformative AI work is happening behind the scenes, in the mundane but critical tasks that consume advisor time daily. AI's biggest impact so far has been streamlining back-office operations, not reinventing the client-facing side. This aligns with what we see across the industry with the real ROI comes from reducing the time between client request and advisor response. Think about the difference between spending hours reconciling statements versus instantly having a clean dataset ready to analyze. The latter, being faster, also fundamentally changes how often and how deeply you can engage with clients. When document processing takes minutes instead of hours, you can afford to be more responsive, more thorough and more proactive. Wealth management remains a relationship business. That said, relationships suffer when they're squeezed between administrative deadlines and manual processes that should have been automated years ago. As AI tools mature, the winners will be the firms that use them to remove invisible barriers to great service. In the end, clients don't care how you got the data ready, they care that you were ready when they needed you most.

📝 📝 📝 2024 AI Field Notes #2: Winning CFOs' Green Light: A Practical ROI Guide for AI Proposals What separates AI projects that get funded from those that don't? It's usually not about the technology or cost. Through my work with CFOs evaluating AI proposals, I've noticed a pattern: successful proposals don't just pitch isolated projects. They build ROI models that connect the dots between project returns and company-wide impact. Here's the framework that more likely wins approval: First, they map the complete cost story: 👷 Project Implementation: Software licensing, security reviews, initial data preparation, integration with existing systems 👷 People & Process: Training, workflow redesign time, documentation creation, governance setup 👷 Ongoing Operations: Regular retraining needs, support desk capacity, system monitoring, data quality maintenance, compliance and audit Then, they track value streams at two levels: 🎯 Project-Level Returns: 1) Time savings: Task completion speed × volume × fully loaded cost 2) Quality gains: Error reduction × average resolution cost 3) Capacity created: Hours freed × reallocation value 🎯 Business-Level Impact: 1) Revenue acceleration: Faster market response × sales conversion 2) Cost avoidance: Automated workflows × labor cost saved 3) Risk reduction: Fewer errors × compliance incident costs This approach helps CFOs sequence investments for maximum P&L and balance sheet impact. High-performing finance teams optimize based on compound returns. In my conversations, they evaluate each initiative on both individual metrics and portfolio multiplication effects. They avoid the trap of funding duplicative efforts while ensuring every investment strengthens their capability foundation. ---- This is part of my "2024 AI Field Notes series", running through the end of the year, where I share observations and thoughts from my work and conversations with business leaders and board directors on AI. Follow or comment to join the conversation and catch the next Field Note in your LinkedIn feed! #AIfieldnotes #ROI #CFO #AIinfinance

Still trying to justify your AI investments using a 3-year ROI model? That approach belongs to the ERP era—not the age of AI. A mid-sized insurance advisory firm I worked with recently hit this wall. They had a game-changing vision: use NLP to transform risk assessment. But the CFO kept asking for a traditional ROI template. Here's what we did differently—and why you should too. We scrapped the "financial return over 3 years" narrative. Instead, we created a 90-day PoC to demonstrate insight value: ✔️ Reduced manual review time by 35% ✔️ Increased quote speed by 22% ✔️ Detected risk factors humans missed We built a case around learning return and value of insight (VOI)—metrics far more relevant for AI than fixed IRR and NPV calculations. 🔄 AI doesn’t fit the clean predictability of IT systems. AI is messy, emergent, and requires a co-owned approach between business and tech leaders. CxOs must now: ✔️ Replace rigid ROI models with phased, agile frameworks ✔️ Focus on insights as early wins, not just financial metrics ✔️ Build trust by educating boards on how AI learns before it earns ✔️ Stop retrofitting AI into legacy thinking 📣 If you're building an AI business case this quarter—let’s talk. I’ll walk you through what worked, what failed, and how to pitch the story that actually lands. #AILeadership #DigitalDecisionMakers #CxOStrategy #AIExecution #TechLeadership #AgileStrategy #AIinInsurance #ProfessionalServices #FutureOfBusiness #ValueOfInsight #EmergentTech #BusinessAgility #AIUseCases #EnterpriseAI #DataDrivenLeadership #PASH