Reshma Pillai: Invisible AI and Failing Fast in Corporate Finance

Reshma Pillai: Invisible AI and Failing Fast in Corporate Finance

What does it actually take to make AI work in corporate finance, not in theory, but in practice? In this episode, Talbot West [https://talbotwest.com] CEO Jacob Andra sits down with Reshma Pillai, a finance and AI transformation leader who made an unlikely journey from accountant to AI professional. Reshma brings a uniquely grounded perspective: she's not a computer scientist, and that's exactly why her insights cut through the hype. Together, Jacob and Reshma break down the four core pillars of finance (transactions, reconciliation and controls, forecasting, and narrative reporting) and explore how different types of AI (generative, agentic, predictive ML) map to each one. The conversation gets real fast: why most AI initiatives fail isn't because the use case is wrong, it's because the infrastructure isn't ready and teams are thinking in monolithic tools instead of outcome-driven workflows. They also dig into the concept of "invisible AI," the idea that AI should become the invisible thread connecting finance processes rather than a shiny tool bolted onto existing workflows, and why that framing changes everything about how you design, govern, and scale AI in a compliance-heavy environment like finance. Topics covered: - Why outcomes-first thinking beats technology-first thinking every time - How to string together heterogeneous AI tools into a value pipeline - The accessibility gap: giving non-technical finance professionals the power to build - Failing fast without wasting eight months and a massive budget - SOX controls, audit compliance, and the "human in the loop" debate - Why some of the best AI solutions come from people with zero CS background If you're a finance leader, digital transformation practitioner, or anyone navigating AI adoption in regulated industries, this episode is packed with frameworks you can actually use.

AI In Restaurants: How Savory Promotes Human Interaction

Josh Boshard, COO of Savory, discusses the integration of AI in the restaurant industry with Talbot West CEO Jacob Andra. The to talk about the challenges and opportunities that Savory faces with its complex portfolio. Josh emphasizes the importance of human interaction and the role of AI in supporting and enhancing the customer experience. The conversation also delves into the complexities of AI deployment, data integration, and the future of AI in the restaurant industry. Takeaways * AI as a Support for Human Interaction * Challenges of AI Integration in the Restaurant Industry Chapters * 00:00 Diverse Range of AI Technologies and Use Cases

Why Do AI Initiatives Fail? Cydni Tetro Joins Jacob Andra to Discuss Common Breakdowns for Digital Transformation Projects

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms] Enterprise AI projects fail at alarming rates. MIT research shows most organizations struggle to achieve meaningful ROI from their AI investments. In this episode of The Applied AI Podcast, host Jacob Andra sits down with Cydni Tetro to explore why enterprise AI transformation is fundamentally different from individual productivity gains, and what separates successful deployments from expensive failures. Cydni brings rare depth to this conversation. Her career spans six years at Disney Imagineering commercializing innovation across business units, serving as CIO at one of the largest Coca-Cola bottlers managing 8,000 employees, and now leading digital transformation across a private equity portfolio. She also founded the Women's Tech Council, which has activated over 40,000 women in technology careers and generates $32 million in annual economic value to the state of Utah. The conversation addresses a critical gap in how organizations think about AI. Most discussions focus on individual productivity. For example, using ChatGPT to draft emails faster or summarize documents. These gains are real but represent only the outer layers of what AI can accomplish. The deeper value requires tackling enterprise-wide challenges involving data integration, systems engineering, legacy infrastructure, and organizational change. Cydni identifies three distinct categories of enterprise AI projects based on data complexity: First, projects with centralized, structured data sources. She shares how her team deployed AI-powered cybersecurity tools in just 60 days because email and threat data already flowed into a single funnel. The data was accessible and structured, making implementation straightforward. Second, legacy systems with legacy data. Manufacturing environments present particular challenges. Operational technology (OT) networks have historically been isolated from IT networks. These OT networks run plant equipment and were never designed to connect to the outside world. Adding AI requires new sensor arrays, network architecture changes, cybersecurity considerations, and workforce training. Some manufacturing lines are 20 to 30 years old, and organizations must maximize their lifetime value while somehow integrating modern AI capabilities. Third, distributed datasets that must be organized before AI can deliver value. A procurement AI project Cydni evaluated would have required massive effort to create structured data from tens of thousands of contracts, serving a team of only two to three people. The ROI calculation did not justify the lift. Common failure modes discussed in the episode: * Targeting the wrong use case * Tackling the right use case but with the wrong tool * Precursor unreadiness (e.g., data not ready) * Not accounting for all the adjacencies and multidirectional dependencies * Tackling too much at once, causing delays in demonstrating value * Scope creep from stakeholders adding requirements * Distributed datasets that must be organized before AI can work * ROI not justified given the effort required * Teams overwhelmed by new responsibilities they were not trained for * Lack of alignment on what minimum viable success looks like * Inability to contain scope to demonstrate value Host Jacob Andra is the CEO of Talbot West [https://talbotwest.com], an AI systems engineering company that helps enterprises avoid the common pitfalls of complex digital transformation initiatives.

Legaltech Civil War: Talbot West CEO Jacob Andra & Advisor Adam Wardel Discuss AI Adoption in Law

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms] YouTube Video Description Law firms face a civil war over AI adoption. On one side, a model that's worked for decades, generating revenue and establishing power structures. On the other, an intelligence revolution that won't disappear in ten years. In this episode, host Jacob Andra sits down with Adam Wardel, an attorney with 12+ years of experience spanning in-house and law firm roles. Adam sits on Talbot West's advisory board, where he brings legal and compliance expertise to the firm's AI transformation work. He advises his clients and Talbot West on navigating AI adoption in regulated environments. Jacob Andra is CEO of Talbot West, an AI advisory and implementation firm, and host of The Applied AI Podcast.  Adam makes the case that AI should be thought of as an actual intelligence working alongside you. Not a dashboard you log into. Not another SaaS product adding to your tech sprawl. An intelligence that reviews contracts before you wake up, surfaces only what needs your attention, and handles the routine so you can do the deep thinking that actually requires a human brain. He describes waking up to find that an AI has already reviewed a contract, prepared a brief, and drafted an edited version. All he needs to do is put on his "deep thinking hat" and apply strategic judgment. The routine work is done. The intelligence responds to emails, sets up follow-up appointments, and works around the clock so the attorney can focus on what actually requires human expertise. The conversation turns to the trap of solving narrow problems. You find a tool that does one thing well (calendaring, discovery review, whatever) and you adopt it. Then another tool for another problem. Before long, you've got a dozen dashboards, fragmented workflows, and you've introduced as much inefficiency as you've eliminated. Jacob points out that even good platforms like Harvey, which handle a basket of related tasks, still create integration challenges with other parts of your workflow. You end up with less tech sprawl than the point-solution approach, but sprawl nonetheless. The alternative: architect the whole system. Map your workflows end-to-end. Understand where AI can handle 90% of the work versus where humans need to stay heavily involved. Build toward organizational intelligence rather than collecting point solutions. This requires understanding the full landscape of what a firm needs, then designing a set of trade-offs optimized for that specific context. Not a one-size-fits-all platform. Not a collection of tools that don't talk to each other. A coherent architecture that evolves as capabilities improve. Adam emphasizes that law firm leaders need to bring in people smarter than themselves on this topic. Partners who've reached senior positions are used to knowing the answers. But AI implementation requires different expertise. The best approach is to surround yourself with people who understand the technology deeply, then provide oversight based on your experience with the practice of law.  Jacob stresses that this outside expertise must be vendor-neutral. If your technology advisor represents specific platforms, they'll recommend those platforms whether they fit or not.  The paradigm of the future decouples functionality from interface. Jacob calls this "invisible AI." Intelligence runs in the background. It surfaces touchpoints only when needed. The old model of managing multiple tools gives way to something more integrated and seamless. You don't log into AI. AI is simply embedded in how work gets done. Jacob makes a crucial point about competitive advantage. If a solution is easy, everyone will adopt it. It becomes table stakes. The firms that pull ahead are the ones doing the harder work of architecting comprehensive systems, understanding dependencies bet

The Best Machine Learning Model, Lumawarp, Rocks the TabArena Test: Jacob Andra & Dr. Alexandra Pasi

Send us a text [https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms] Lumawarp delivers 7% higher accuracy than leading ML models while running 300+ times faster. On the TabArena HELOC default prediction benchmark, it topped the accuracy leaderboard while training on a gaming laptop in about an hour. Competing methods required hundreds of hours on large compute clusters to achieve worse results. This is the breakthrough that breaks the accuracy/speed tradeoff that has constrained machine learning for decades. In this episode, Talbot West CEO Jacob Andra sits down with Dr. Alexandra Pasi, CEO of Lucidity Sciences, to explore how Lumawarp achieves these results and what it means for enterprises building AI systems where precision is non-negotiable and milliseconds matter. The technology employs a novel mathematical framework grounded in partial differential equations and geometric manifold regularization. Rather than relying on deep learning or tree-based methods that struggle with sparse or imbalanced data, Lumawarp constructs optimal kernels directly from training data. The result: superior pattern recognition with microsecond inference times, deployable on edge devices without sacrificing accuracy. In this conversation, we cover: Benchmark results showing Lumawarp outperforming XGBoost, MNCA, and other leading models on structured data tasks Why a few percentage points of accuracy improvement translates to millions of dollars in fraud detection, clinical decision support, and risk modeling Microsecond inference enabling real-time applications in high-frequency trading, robotics, and predictive maintenance Edge deployment capabilities for wearables, industrial sensors, and environments where cloud connectivity isn't reliable The critical difference between models optimized for linguistic plausibility (LLMs) versus mathematical precision (Lumawarp) How the Talbot West and Lucidity Sciences partnership works: Lumawarp solves the prediction problem, Talbot West solves the deployment problem As Dr. Pasi explains, traditional ML forces you to choose: fast models sacrifice accuracy, accurate models require massive compute. Lumawarp sits completely outside that tradeoff curve, delivering both simultaneously. For high-stakes applications where 90% accuracy means a 1-in-10 failure rate, and 99% accuracy means 1-in-100, that difference determines whether you can deploy ML at all. This episode is essential viewing for executives evaluating AI investments, data scientists looking beyond the LLM hype cycle, and anyone building systems where accuracy and latency both matter. About the Guest: Dr. Alexandra Pasi is CEO and co-founder of Lucidity Sciences. A PhD mathematician, she spent over a decade advancing the mathematical foundations of machine learning before pioneering the GPU-parallelizable geometric manifold regularization techniques that became Lumawarp. Her work has demonstrated real-world impact across healthcare (predicting hospital-acquired conditions), finance (high-frequency trading), and scientific research (particle physics detection). About Talbot West: Talbot West is an AI enablement firm specializing in enterprise digital transformation. The firm combines full-spectrum AI expertise with Fortune 500 systems architecture methodology, helping organizations deploy the right AI technologies for the right problems. Learn more at talbotwest.com About Lucidity Sciences: Lucidity Sciences develops advanced machine learning technologies for pattern identification and prediction in structured data. Their research-driven approach addresses fundamental limitations in existing ML methods, delivering breakthrough improvements in model accuracy, generalizability, and computational efficiency. Learn more at luciditysciences.com