#291 Why Most AI Projects Fail to Deliver ROI, Sinohe Terrero, CFO and COO, Envoy

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/6raW3lf3gJuwTrYNbdkf0F Too many organisations are pouring time and money into AI only to find that the promised efficiency gains and cost savings never materialise, leaving CFOs struggling to justify the investment. Understanding why most AI projects fail to deliver ROI, and what finance leaders can do differently, is now a critical skill for anyone responsible for steering strategy, systems, and spend. In this GrowCFO Show episode, host Kevin Appleby sits down with Sinohe Terrero, CFO and COO of Envoy, to explore why so many AI initiatives fall short and how finance leaders can change the outcome. Drawing on his experience as a serial startup CFO and operator in high-growth tech companies, Sinohe reframes AI as a practical toolkit for augmentation, task automation, and application development, and explains how confusion between these use cases leads to poor deployment and weak returns. Throughout the conversation, Sinohe shares real examples from Envoy’s finance function, from AI-powered reconciliations and automated interview workflows to custom dashboards that bring data together in one place. He also dives into AI governance, describing the AI council he leads and the data policies that allow innovation while protecting sensitive information, ultimately positioning the CFO as a hands-on AI leader focused on both value creation and risk management. Key topics covered: Companies misunderstand what AI can do, deploy it inappropriately (e.g., trying to “fully automate everything”), and often lack in-house application developers who can tailor solutions to their actual workflows. Sinohe breaks AI use into augmentation, task automation, and application development, arguing that most ROI today comes from targeted task automation and small, purpose-built tools, not sweeping end-to-end automation projects. Envoy’s finance team used AI to automate health insurance and other reconciliations, identifying about $40,000 in recoveries and turning tedious, quarterly work into a largely automated process. Sinohe personally builds AI-powered applications to reconcile accounts, summarize emails and Slack, prep and debrief interviews, and create a “morning coffee” dashboard that consolidates operational and financial insights into a single pane of glass. As head of Envoy’s AI council, Sinohe has helped design a data governance matrix that clarifies what data can be used in which tools, allowing experimentation and creativity while strictly protecting company and customer data. Sinohe is bullish on increased data accessibility (e.g., via banks and platforms like Salesforce) and predicts a shift toward custom, CFO‑designed dashboards and tools, with legacy point solutions being displaced by in‑house applications that do exactly what the business needs. Links Sinohe Terrero on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps:  0:01:36 – Sinohe explains Envoy as a workplace technology platform focused on managing physical spaces (visitor check-in, security, emergency notifications, desk allocation) with 6,000+ global customers and around 250 employees. 0:03:35 – He shares how timing, a tight investor story, and demonstrating strong cash flow and operational discipline were critical to a successful Series C raise during a turbulent market. 0:04:47 – Sinohe lays out the core reasons AI fails in many organizations and introduces his three-part framework: augmentation, task automation, and application development. 0:07:11 – He describes teaching himself to build AI-powered applications, including an asset-account reconciliation tool that cut a two-hour monthly process down to about two minutes. 0:12:21 – Using tools like Scribe to document workflows, Envoy’s finance team identifies automation candidates; a payroll-led AI skill for health insurance reconciliations surfaced roughly $40,000 owed to the company. 0:17:53 – Sinohe explains Envoy’s AI council, clear AI policies, and a data governance matrix that defines what data can be used where, enabling safe experimentation at scale. 0:21:17 – He details his personal AI setup: automated interview briefing/debriefing via Granola + Claude, daily digests of emails/Slack/meetings, and automated summaries of operational metrics and customer activity. 0:24:58 – Sinohe predicts job disruption in large teams (e.g., 100 accountants potentially shrinking to 60) but sees smaller teams using AI to focus on higher-value, advisory work rather than basic reconciliations. 0:26:30 – He describes replacing tools like Flowcast, Asana/Monday, and other SaaS products with custom AI-enabled applications that do 75% of what generic tools do—but 100% of what Envoy actually needs. 0:33:36 – Sinohe forecasts greater bank and platform data accessibility, more automated reconciliations, and a shift that frees CFOs from operational drudgery so they can focus on higher‑value strategic work. 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