The Applied AI Podcast

About The Applied AI Podcast

A hype-free zone to discuss the practical applications of artificial intelligence and machine learning technologies to real-world use cases in business, government, nonprofits, and other types of organizations. Calibrated to business executives who want to know, "What does AI mean for my industry and my company," it keeps the emphasis on value creation and actionable strategies. The Applied AI Podcast is produced by Talbot West, a leading digital transformation consultancy and AI enablement partner for mid-market and enterprise companies. Jacob Andra, CEO of Talbot West, brings in-the-trenches insights from real companies implementing AI and machine learning technologies. Additionally, Talbot West clients, partners, and other executives feature prominently in our guest line-up. With real-world experience and a wealth of applied AI perspectives, The Applied AI Podcast avoids both the hype and the nay-saying surrounding AI technologies. We cover the actual value creation that AI is driving in enterprise, as well as the risks, pitfalls, and limitations. We bring you a balanced view. Just like Talbot West clients trust us to be their digital transformation advisor, our listeners trust The Applied AI Podcast to be a bias-free, no-nonsense zone. Learn more at https://appliedaipod.com Learn about Talbot West at https://talbotwest.com Learn about BizForesight (an AI-powered M&A platform from Talbot West): https://bizforesight.com Learn about the Talbot West AI 2030 Thesis and our vision of total organizational intelligence: https://talbotwest.com/ai-insights/the-talbot-west-5-year-ai-thesis Learn about Cognitive Hive AI (CHAI), Talbot West's modular, composable ensemble architecture: https://talbotwest.com/ai-insights/what-is-cognitive-hive-ai-chai Learn about AI Prioritization and EXecution (APEX), Talbot West's methodology for prioritizing AI initiatives: https://talbotwest.com/ai-insights/apex-framework-for-ai-prioritization Read Talbot West's response to the August 2025 Wall Street Journal article on how McKinsey is adapting to AI: https://talbotwest.com/ai-insights/wsj-mckinsey-talbot-west Read how Talbot West approaches the "buy vs build" question with our clients: https://talbotwest.com/ai-insights/ai-buy-vs-build Read Talbot West's description of composable AI and why it's the future: https://talbotwest.com/services/cognitive-hive-ai/composable-ai How AI is driving value creation in M&A: https://talbotwest.com/industries/mergers-and-acquisitions-manda/how-ai-makes-mergers-and-acquisitions-more-efficient Why a system-of-systems approach is the future of AI deployment: https://talbotwest.com/ai-insights/system-of-systems-in-ai An examination of the DoD's Modular Open Systems Approach (MOSA) and its implications for AI deployment: https://talbotwest.com/industries/defense/what-is-mosa-in-defense-systems Ways AI can make government more efficient: https://talbotwest.com/industries/government/how-can-ai-make-government-more-efficient Examining the importance of explainability in AI and why it doesn't exist with commercial large language models but does with Cognitive Hive AI: https://talbotwest.com/services/ai-governance/what-is-explainability-in-ai Let's not forget about small language models: https://talbotwest.com/ai-insights/what-is-a-small-language-model-slm Where AI change management often fails: https://talbotwest.com/services/change-management-for-ai-implementation/understanding-change-management-in-ai

Publisher

Jacob Andra

Category

news · technology

Language

en

Explicit

No

First episode

21 Aug 2025

Latest episode

27 Jul 2026

Latest episodes

19 episodes in the feed.

1. 

   27 Jul 2026

   A Better Way to Dive AI Value in Revops

   Jacob Andra and Steve Larsen argue that the SaaS point-solution model has failed revops, and not for the reason most people assume. The problem is not that the tools are bad. It is that they are disconnected from one another and generic to every company that buys them, so no amount of additional tooling produces differentiation. The conversation makes the case for a harness, the configured logic, rails, and systems wrapped around a model, and walks through the tiered path for building one. Discussed: Why revops teams have more tooling than at any point in history while performance stays flat or declines. Islands of intelligence: what actually happens when AI is embedded in twelve separate point tools and nowhere else. The harness as a third option between off-the-shelf software and full custom development. How a harness built for pre-call planning extends to rep onboarding, call post-mortems, comp planning, and competitor analysis for a fraction of the original build cost. Why that same structure preserves institutional knowledge through attrition. The implementation ladder, from a configured Claude Team environment through multi-environment tool calls to direct Salesforce and ERP integration. Two orthogonal axes of AI cost management, platform subscription and tokenization, and why right-sizing the model to the task is the lever most teams miss. Steve Larsen is Managing Director at Talbot West, with 30 years across pharmaceuticals, healthcare, big data, and SaaS. Hosted by Jacob Andra, CEO of Talbot West (https://talbotwest.com). Read the recent Talbot West report on AI and revops (https://talbotwest.com/insights/revops-ai-report), a companion article to this episode.

2. 

   8 Jun 2026

   Bharathi Rajan of Swire on AI Integrated Into Complex CPT Manufacturing & Operations

   Using AI on your own is easy. Deploying it across an organization of tens of thousands of people is a different problem entirely. In this episode, host Jacob Andra sits down with Bharathi Rajan, who leads enterprise digital, data, and AI at Swire Coca-Cola USA, one of the top five Coca-Cola bottlers in the country, with six manufacturing plants across 13 states and brand partners including Coca-Cola, BodyArmor, Monster Energy, and Dr Pepper. Bharathi spent more than four years building Swire's data capability from the ground up before taking on its digital and AI agenda. Drawing on earlier work at UnitedHealth Group and Goldman Sachs, she lays out what it actually takes to move AI from personal novelty to enterprise infrastructure: understanding a process before automating it, keeping business impact ahead of the technology, and treating change management as the core of the work rather than an afterthought. The conversation turns on the orchestration problem at the heart of enterprise AI. When every major vendor (SAP, Salesforce, and the rest) ships its own agents, the advantage goes to the organization that can make those siloed systems talk to each other behind a single, seamless experience for the user. Jacob and Bharathi also get into prioritizing operational efficiency against top-line growth, why augmentation beats wholesale automation, and why even a one percent error rate is enough to lose a user's trust. What the episode covers: Why enterprise AI is a fundamentally different game than personal AI Change management as the make-or-break factor in adoption Production prioritization, supply-chain planning, and predictive maintenance in manufacturing Orchestrating across SAP, Salesforce, and other siloed systems Augmentation over automation, with a human kept in the loop Sequencing long-term bets alongside short-term wins so capabilities compound on each other

3. 

   30 Apr 2026

   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.

4. 

   10 Mar 2026

   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

5. 

   3 Feb 2026

   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.

6. 

   20 Dec 2025

   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

7. 

   18 Dec 2025

   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

8. 

   11 Nov 2025

   Constitutional AI With Bennett Borden and Jacob Andra

   Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Talbot West CEO Jacob Andra interviews Clarion AI CEO Bennett Borden on ensemble AI approaches. Bennett Borden served eight years as a CIA data scientist identifying patterns in digital trails, he went to Georgetown Law and specialized in automated decision systems. Now as CEO of Clarion AI, he runs the only law firm that operates as both legal counsel and development shop, building AI systems that drive business value while maintaining legal compliance. This episode explores multi-agent AI architectures. Borden explains constitutional AI, developed by Anthropic, which programs AI behavior through plain language directives rather than thousands of lines of code. Building with generative AI resembles forming psychology rather than writing deterministic algorithms. Jacob pushes on the practical challenges of large context windows, where language models become unreliable when processing massive amounts of information. He describes the wobbliness that emerges when models forget what's over here when they're processing over there, and discusses neurosymbolic approaches that use ontological skeletons to help LLMs maintain context. This leads to a deeper discussion of ensemble architectures where specialized agents handle bounded contexts rather than expecting single models to manage everything. Real implementations combine retrieval augmented generation with constitutional AI and adversarial oversight modules that audit primary agent behavior. These patterns, where modules challenge each other's findings rather than simply cooperating, create robust outcomes that monolithic systems cannot match. The conversation covers practical enterprise applications. Back office automation handles repetitive, data centric tasks where companies apply the same judgments repeatedly. Knowledge worker augmentation transforms how lawyers, consultants, and accountants work. Borden estimates 80% of legal work can be better handled by AI, freeing professionals to focus on the quintessentially human 20% that requires judgment and strategic thinking. Jacob probes the definition of agentic AI, noting that almost no one knows what they mean when they use the term. He identifies at least four or five common but conflicting connotations. Borden clarifies that agentic AI is fundamentally a recommendation engine on steroids, where an AI subcomponent makes decisions based on parameters it's given as part of a larger orchestration. This aligns with Talbot West's emphasis on coordinated systems rather than autonomous agents making high stakes decisions without oversight. Data value extraction emerges as a critical theme. Companies sit on information locked in emails and file systems. Properly curated knowledge bases combined with constitutionalized AI surface insights that distinguish products and services. A retail client's app pulls weather and event data to adjust operations dynamically, increasing cookie production before predicted afternoon rushes. Borden describes predictive compliance systems that monitor for behavior patterns correlating with fraud. The discussion addresses ensemble architectures that scale from individual modules to nested systems of systems. Specialized modules handle discrete tasks, feeding into domain ensembles that synthesize insights. Higher level meta-ensembles correlate patterns across domains, identifying coordinated activities invisible when viewing any single domain alone. Both speakers emphasize explainability and human oversight, with clear audit trails for every decision. Talbot West delivers Fortune 500 AI consulting to midmarket and enterprise organizations through its APEX framework and Cognitive Hive AI architecture. Visit talbotwest.com (http://talbotwest.com)

9. 

   31 Oct 2025

   Will AI Take All the Jobs? Jacob Andra and Stephen Karafiath Say No

   Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) While people fear wholesale workforce replacement, the actual transformation is far more complex and ultimately more optimistic for organizations willing to adapt strategically. This episode cuts through the hype to examine three distinct zones of AI capability. First, tasks where AI excels at things humans never could do well, like fraud detection algorithms or protein folding analysis. Second, uniquely human domains like relationship building and creative problem solving across diverse contexts. And third, the contested middle ground where AI augments but doesn't replace human workers. Jacob and Stephen share real insights from Talbot West's consulting work, including an aerospace manufacturer case where their top recommendation wasn't an AI solution at all. It was hiring a human to orchestrate digital transformation across departments. This reveals a fundamental truth: the future isn't humans versus AI. It's humans working with AI as force multipliers. Large language models get conflated with AI itself, but they represent one narrow slice of available technology. They excel within certain domains but fail catastrophically when pushed beyond those boundaries. That's why Talbot West pursues two complementary approaches to expand AI capabilities beyond current LLM limitations. Neurosymbolic AI combines neural networks with symbolic logic structures. Think of AlphaGo, which paired a neural network exploring game possibilities with a mathematical language enforcing the rules. The neural component provides creativity and pattern matching. The symbolic structure keeps everything grounded in reality and prevents hallucinations. Cognitive Hive AI takes a different approach by orchestrating multiple specialized AI modules into coordinated systems. A single large language model might serve as just one small component, perhaps handling translation between machine language and human users. Other modules handle specific tasks like sentiment analysis, predictive analytics, or compliance monitoring. Together, they create business capabilities no single AI could achieve alone. The MIT study claiming 95% of AI projects fail to see ROI likely reflects implementations that lacked this level of strategic thinking. When you bring proper analysis and architecture to AI deployment, returns become inevitable. Talbot West's customer feedback suggests near-universal satisfaction when projects are scoped correctly from the start. Organizations face a choice in how to handle this productivity multiplier. The short-term approach fires people and maintains current output with fewer workers. The strategic approach keeps the workforce intact and uses AI augmentation to scale operations dramatically without proportional headcount increases. Companies taking the second path position themselves for massive competitive advantage. This gets incredibly nuanced when you consider all the variables at play. Different job types face different displacement risks. Various AI technologies have different strengths and limitations. Neurosymbolic systems excel at different tasks than ensemble architectures. Single machine learning algorithms solve different problems than large language models. Understanding these distinctions matters enormously when planning organizational transformation. You absolutely need humans in your company, but the nature of their work will shift. AI involvement will vary dramatically across roles from 1% to 100% depending on the specific tasks and available technology. Success requires bringing rigorous analysis to determine exactly where and how AI augments your workforce. Learn more about Talbot West's approach to AI implementation: https://talbotwest.com (https://talbotwest.com)

10. 

    27 Oct 2025

    Agentic AI and Neurosymbolic AI: Jacob Andra Interviews Dr. Alexandra Pasi of Lucidity Sciences

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Two major ideas are shaping the next era of artificial intelligence: agentic AI and neurosymbolic AI. Talbot West CEO Jacob Andra and Lucidity Sciences CEO Dr. Alexandra Pasi bring together their complementary perspectives. They unpack the confusion surrounding the term “agentic.” The most common misuses fall into three categories. 1.Digital employee. This use assumes an AI can fully replace a human role. In practice, jobs consist of overlapping tasks that depend on judgment, context, and social understanding. Substituting a human one-to-one with an AI system oversimplifies work and introduces risk. 2.AI interacting with humans. Many products describe themselves as agentic simply because they interact with people. Yet a chatbot or outbound assistant is not necessarily intelligent or autonomous. Interface does not equal agency. 3.Autonomous executor. Another common assumption is that an AI that performs tasks independently qualifies as agentic. Yet there are non-AI autonomous systems. Jacob proposes a definition that is specific enough for real-world planning: an AI function able to complete a task as part of a larger ensemble or capability. This definition treats agentic systems as modular and composable. Each agent performs a defined function within a coordinated network of systems. This approach moves the conversation from vague marketing language to measurable performance outcomes. From there, the discussion turns to large language models. Both Jacob and Alexandra acknowledge their extraordinary power but also their limitations. LLMs have made AI accessible to everyone through natural language, allowing rapid knowledge retrieval, summarization, and idea generation. At the same time, language itself is a constraint. Human language was not built for exact quantitative reasoning or precise logical relationships. LLMs lose reliability when they are asked to maintain long context or handle tightly coupled data. The guests agree that these models should be viewed primarily as interface layers that help people and organizations communicate with structured information systems. The conversation then transitions to neurosymbolic AI, which combines neural networks and symbolic reasoning into a single architecture. The neural components are probabilistic and pattern-oriented. They generalize and infer. The symbolic components operate on defined rules and logical constraints. They ensure structure, coherence, and traceability. When combined you get an intelligent system that is both adaptive and verifiable. Dr. Pasi explains how this concept has deep roots in earlier AI research. In some early mathematics experiments, language models were paired with formal systems like Lean to verify every logical step. In modern enterprise applications, this same hybrid pattern provides a way to reconcile innovation with control. It creates a bridge between the flexibility of learning models and the accountability required by governance and compliance. Jacob shares two Talbot West use cases that illustrate these ideas. The first involves enterprise evaluation and roadmapping. Many organizations have complex, organically grown processes and data flows that are difficult to map or optimize. The second example is BizForesight, a platform to help business owners understand and improve company value. It combines document ingestion, interviews, and machine learning within a defined symbolic framework. The symbolic layer enforces valuation logic and methodological integrity, while the neural layer interprets unstructured data and provides adaptive recommendations.

11. 

    17 Oct 2025

    Neurosymbolic AI and the Shortcomings of LLMs: Jacob Andra and Stephen Karafiath

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Large language models have captured headlines, but they represent only a fraction of what AI can accomplish. Talbot West co-founders Jacob Andra and Stephen Karafiath explore the fundamental limitations of LLMs and why neurosymbolic AI (https://talbotwest.com/ai-insights/what-is-neurosymbolic-ai) offers a more robust path forward for enterprise applications. LLMs sometimes display remarkable contextual awareness, like when ChatGPT proactively noticed specific tile flooring in a photo's background and offered unsolicited cleaning advice. These moments suggest genuine intelligence. But as Jacob and Stephen explain, push these systems harder and the cracks appear. The hosts examine specific failure modes that emerge when deploying LLMs at scale. Jacob documents persistent formatting errors where models swing between extremes—overusing lists, then refusing to use them at all, even when instructions explicitly define appropriate use cases. These aren't random glitches. They reveal systematic overcorrection behaviors where LLMs bounce off guardrails rather than operating within defined bounds. More troubling are the logical inconsistencies. When working with large corpuses of information, LLMs demonstrate what Jacob calls cognitive fallacies—errors that mirror human reasoning failures but stem from different causes. The models cannot maintain complex instructions across extended tasks. They hallucinate citations, fabricate data, and contradict themselves when context windows stretch too far. Even the latest reasoning models cannot eliminate certain habits, like the infamous em-dash overuse, no matter how explicitly you prompt against it. Stephen introduces the deny-affirm construction as another persistent pattern: "It's not X, it's Y" formulations that plague AI-generated content. Tell the model to avoid this construction and watch it appear anyway, sometimes in the very next paragraph. These aren't bugs to be patched. They're symptoms of fundamental architectural limitations. The solution lies in neurosymbolic AI, which combines neural networks with symbolic reasoning systems. Jacob and Stephen use an extended biological analogy: LLMs are like organisms without skeletons. A paramecium works fine at microscopic scale, but try to build something elephant-sized from the same squishy architecture and it collapses under its own weight. The skeleton—knowledge graphs, structured data, formal logic—provides the rigid structure necessary for complex reasoning at scale. Learn more about neurosymbolic approaches: https://talbotwest.com/ai-insights/what-is-neurosymbolic-ai About the hosts: Jacob Andra is CEO of Talbot West and serves on the board of 47G, a Utah-based public-private aerospace and defense consortium. He pushes the limits of what AI can accomplish in high-stakes use cases and publishes extensively on AI, enterprise transformation, and policy, covering topics including explainability, responsible AI, and systems integration. Stephen Karafiath is co-founder of Talbot West, where he architects and deploys AI solutions that bridge the gap between theoretical capabilities and practical business outcomes. His work focuses on identifying the specific failure modes of AI systems and developing robust approaches to enterprise implementation. About Talbot West: Talbot West delivers Fortune 500-level AI consulting and implementation to midmarket and enterprise organizations. The company specializes in practical AI deployment through its proprietary APEX (AI Prioritization and Execution) framework and Cognitive Hive AI (CHAI) architecture, which emphasizes modular, explainable AI systems over monolithic black-box models. Visit talbotwest.com to learn how we help organizations cut through AI hype and implem

12. 

    7 Oct 2025

    Data Security in AI: Talbot West CEO Jacob Andra Interviews Scott Peiffer of i4Ops

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Most enterprise AI projects fail because companies hold back their data. They spend hundreds of thousands of dollars training models on sanitized datasets, afraid to expose sensitive information. They get generic answers that create no competitive advantage. In this episode, Scott Peiffer from i4Ops cuts through the AI hype to address the real challenge facing enterprises: how to deploy AI systems that actually create value while keeping proprietary data secure. What you'll learn Scott Peiffer brings 35 years of data storage and security experience from Intel, NetApp, and now i4Ops. He explains why the current approach to enterprise AI deployment produces disappointing results and what companies should do instead. The FOMO problem Companies receive mandates from the C-suite to "do AI" without clear objectives or strategy. Research shows 90% of these models fail to deliver value because organizations train them on limited data subsets, withholding their most valuable information out of security concerns. Employee data, sales conversations, customer support transcripts, and strategic documents remain locked away, resulting in AI systems that cannot deliver insights specific to the business. The challenge compounds when companies lack a systematic approach. They bolt new AI tools onto poorly designed foundations without addressing underlying digital infrastructure issues. Why digital transformation comes first Successful AI deployment requires a foundation in broader digital transformation strategy (https://talbotwest.com/ai-insights/what-goes-into-a-good-digital-transformation-strategy). Companies need to start with a clear end vision, map current systems and processes, and create a stepwise progression rather than bolting new tools onto poorly designed foundations. This means defining where you want to go (higher efficiency, preparing for acquisition, competitive advantage), understanding your current state through systems mapping, and identifying a practical path forward that does not break the bank or disrupt operations. Knowledge management as competitive advantage The future requires every competitive organization to maintain an in-house fine-tuned RAG system trained on company-specific knowledge. This means addressing fundamental questions about documentation, data quality, and information flow before implementing AI solutions. Scott emphasizes that approximately 75% of companies now use local data models rather than cloud solutions when dealing with sensitive information. The security wrapper stays in private data centers where organizations maintain complete control. The data security gap While data at rest and data in transit receive encryption protection, data in use remains vulnerable. When you download an Excel file to analyze it, that data sits unencrypted on your machine. You can copy it, manipulate it, send it to competitors by accident or malicious intent. When employees ask public AI models to summarize files, that unencrypted data gets ingested into public language models. i4Ops' approach Rather than plugging holes after they appear, i4Ops uses a patented virtual machine approach that starts with a default of zero data egress. Data cannot leave the protected environment unless explicitly whitelisted, regardless of credentials or authentication methods. Where AI creates the most value Beyond the obvious cost savings in customer support and repetitive tasks, AI delivers transformational value when companies train models on their complete proprietary datasets to solve specific business problems. Scott describes how his team solved a weeks-long coding problem in hours by training a model exclusively on their kernel code. They asked two questions and had their answer. Produc

13. 

    29 Sept 2025

    AI is Much More Than LLMs: Jacob Andra Interviews Dr. Alexandra Pasi on Cutting-Edge Machine Learning

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Dr. Alexandra Pasi (Lucidity Sciences) joins Talbot West (https://talbotwest.com) CEO Jacob Andra to explore why conflating AI with large language models creates blind spots in enterprise technology strategy. With 15 years in machine learning, Dr. Pasi brings mathematical rigor to practical AI deployment. Key discussion topics Jacob identifies the linguistic synecdoche in AI discourse: taking LLM characteristics like hallucination and incorrectly applying them to all AI. Dr. Pasi expands on this, explaining that LLMs are just one application of AI to language data. The broader landscape includes supervised learning, computer vision, anomaly detection, and time series forecasting that operate on different principles. When Jacob presents real-world scenarios, Dr. Pasi demonstrates technology selection. For supply chain optimization, she recommends supervised structured learning over LLMs. These problems need historical data analysis and forecasting under new conditions. LLMs lack organizational context and carry irrelevant noise. For structured data in spreadsheets or databases, specialized models outperform language models. The generalizability problem Dr. Pasi reveals why machine learning often fails: models excel on training data but collapse in production. Auto ML combines multiple models for good initial fit but poor generalization. Her company's AF1 technology addresses this through new mathematical frameworks that find non-linear patterns traditional algorithms miss. Three implementations demonstrate this approach. In clinical care, AF1 predicts ICU pressure injuries better than 80 Auto ML models combined. Financial trading applications find actual market dynamics rather than historical coincidences. Particle physics implementations detect rare events without losing signal in noise. Digital transformation insights Organizations miss opportunities by automating tasks without questioning why they exist. Dr. Pasi explains how companies created siloed roles that now reveal workflow gaps when automated. The real value comes from reorganizing information flow, not just automating existing processes. For problems without historical data, she describes using directed acyclic graphs to map causality, then generating synthetic data with controlled variations. This enables simulation and optimization without costly real-world experiments. Practical implementation guidance Both experts emphasize starting with business problems, not technology. Many challenges need basic algebra, not complex AI. Dr. Pasi advocates explicit modeling for understood problems, adding machine learning only where external variables create uncertainty. She addresses risk concerns, noting hallucination affects only certain AI types, not supervised learning on structured data. Warning against compute-heavy solutions with surprise cloud bills, she recommends lightweight alternatives that maintain accuracy while enabling edge deployment on mobile devices and wearables. Success requires identifying where AI impacts the P&L. The best executed project means nothing without clear financial outcomes or defined steps in a roadmap showing ROI. About The Applied AI Podcast The Applied AI Podcast (https://talbotwest.com/podcast) delivers practical AI implementation guidance for enterprise, government, and defense sectors. Produced by Talbot West, episodes feature practitioners deploying AI in production, translating capabilities into business outcomes. Subscribe at https://appliedaipod.com (https://appliedaipod.com) Learn more at talbotwest.com #AppliedAI #MachineLearning #EnterpriseAI #StructuredData #SupervisedLearning #ComposableAI #ModularAI #AIStrategy #DigitalTransformation #PredictiveAnalytics

14. 

    16 Sept 2025

    Jason duPont On AI in the Mortgage Industry: An Interview with Talbot West CEO Jacob Andra

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Jason duPont, COO of NEXA Mortgage, reveals how AI transforms mortgage operations from 54-minute loan processing to autonomous underwriting systems. NEXA processes $10 billion annually through 3,300 loan officers and targets $15 billion using agentic AI partnerships. This episode demonstrates practical AI implementation beyond chatbots. DuPont shares NEXA's partnership with Tidalwave, which handles the mortgage application process starting at the 1003 form submission. The platform conducts intelligent conversations with clients to gather missing information, routes loans to appropriate lenders based on overlays and requirements, and connects to credit services for automated qualification decisions. KEY INSIGHTS: Tidalwave's front-end focus: Starting with point-of-sale, not back-office operations Building AI agent hierarchies that spawn additional agents autonomously Voice AI converting cold prospects into Thursday Zoom call attendees Personal AI experiments with n8n orchestration for health and crypto trading Achieving "one-touch close" through comprehensive upfront data collection Future vision: Loan officers become influencers while AI handles technical work ENTERPRISE VALUE FOR DECISION-MAKERS: DuPont predicts loan officers will evolve into relationship managers and influencers, with AI handling income calculations, debt ratios, and document processing. This transformation model applies across industries where knowledge workers currently manage complex compliance and documentation. His approach to testing platforms while maintaining flexibility demonstrates practical AI adoption strategies. The discussion covers how Tidalwave pulls back control of the entire process rather than relying on individual language models, building guardrails while maintaining agility. DuPont's personal experiments showcase enterprise concepts: agents building other agents, recursive feedback loops improving voice AI prompts, and hierarchical systems with built-in auditors. TECHNICAL IMPLEMENTATION FROM THE TRANSCRIPT: Tidalwave handles applications from 1003 submission through qualification System determines optimal loan products (VA, FHA, conventional, non-QM) Intelligent routing based on lender overlays and income calculation methods Voice AI for recruitment and lead generation with recorded call analysis OCR and document categorization for comprehensive data processing Future roadmap includes full front-to-back loan lifecycle automation BUSINESS TRANSFORMATION INSIGHTS: DuPont emphasizes speed: "We move very fast. The broker channel is faster than the IMB space." This agility enables rapid testing and iteration. His 54-minute submission-to-clear achievement with human processing sets the benchmark for AI automation targets. Market dynamics will shift dramatically: "Those that don't embrace will just feed everyone else." Early adopters become evangelists while skills develop gradually. The loan officer who masters AI-driven lead generation and conversion will dominate market share. ABOUT THE GUEST: Jason DuPont serves as COO and top recruiter at NEXA Mortgage. With industry experience since 1996, he drives NEXA's digital transformation through strategic partnerships and personal AI experimentation spanning health optimization, investment strategies, and autonomous agent development. ABOUT THE HOST: Jacob Andra, CEO of Talbot West, leads AI enablement for mid-market companies, Fortune 500 enterprises, government agencies, and defense organizations. Talbot West's proprietary frameworks help organizations distinguish safe from unsafe AI applications while moving quickly toward comprehensive intelligence systems.

15. 

    8 Sept 2025

    Rick Meekins is 6X More Productive With AI + Automation: An Interview With Talbot West CEO Jacob Andra

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Rick Meekins achieves 4-6x productivity gains using ChatGPT and automation tools in precisely the ways Talbot West recommends to enterprise clients: low-risk, high-reward applications that deliver immediate ROI without exposing sensitive data. Rick uploads podcast transcripts to ChatGPT, not proprietary business data. He generates social media content, not confidential reports. This demonstrates exactly what Talbot West helps executives understand: which use cases are safe for commercial LLMs and which require more secure approaches. The difference determines whether you get value or vulnerability. Rick's greatest efficiency gains come from combining ChatGPT with technologies that have existed for years. Zoho CRM automations. Zapier integrations. These tools remain underutilized in most organizations. When you pair them with modern AI capabilities, the multiplicative effect becomes clear. Rick processes 8 podcast episodes weekly in the time it once took for 2. Large language models excel at specific functions: ingesting text and outputting formatted content. Rick uses ChatGPT Projects exactly this way, turning transcripts into platform-specific social posts. He doesn't force the tool into tasks where it struggles. This selective deployment is what separates successful AI implementation from expensive experiments. These are practical applications any company can implement today. The tools Rick uses cost less than a single consultant day rate. They require minimal technical expertise. Most importantly, they demonstrate that effective AI implementation starts with understanding what to automate and what to leave alone. Rick's approach validates Talbot West's core message: AI drives maximum value when you know where to deploy it safely. No sensitive data uploads. No security risks. No vendor lock-in. Just measurable productivity gains using tools properly matched to tasks. The companies winning with AI aren't the ones using it for everything. They're the ones using it correctly for the right things. Resources: Listen to Rick's podcast: https://aepiphanni.com/relentless-pursuit-of-winning-podcast/ (https://aepiphanni.com/relentless-pursuit-of-winning-podcast/) Learn about safe AI implementation: https://talbotwest.com/services/ai-implementation-and-integration (https://talbotwest.com/services/ai-implementation-and-integration) Explore The Applied AI Podcast: https://appliedaipod.com (https://appliedaipod.com) About the Guest Rick Meekins is a 30-year entrepreneur, founder of Aepiphanni Consulting, and host ofThe Relentless Pursuit of Winning Podcast. He has co-created dozens of companies and guided hundreds of founders, equipping them with the clarity and strategies to build disruptive, sustainable businesses. About the Host Jacob Andra is the CEO of Talbot West, a digital transformation consultancy that guides organizations through AI implementation and integration. Jacob brings proven methodologies to organizations of all sizes. Jacob writes and publishes extensively on the intersection of AI, enterprise strategy, economics, and policy, covering critical topics including AI governance, explainability, responsible AI implementation, and the practical applications of machine learning in complex organizational environments.

16. 

    3 Sept 2025

    Bill McCalpin on AI in Mergers and Acquisitions

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Jacob Andra, podcast host and CEO of Talbot West (https://talbotwest.com), interviews Bill McCalpin, investment banker and chair of The Alliance of Merger and Acquisition Advisors. Bill McCalpin reveals why companies with identical revenue and profits sell for vastly different prices: one for $2 million, another for $12 million. The difference comes down to digital transformation readiness. In this Applied AI Podcast episode, McCalpin shares data from thousands of M&A transactions showing a 6x variation in sale price between similar companies. Often, much of this differential is driven by how well companies leverage technology to compete on speed, quality, and efficiency. Digital transformation impacts all three value drivers buyers evaluate. Existing assets like customer base and brand become more valuable when backed by integrated systems. Growth potential multiplies when technology enables rapid scaling. Risk factors diminish when automated processes reduce owner dependency and increase revenue consistency. Companies that digitize their data, integrate their processes, and build technology platforms around their core offerings command premium valuations. Those that don't increasingly sell for parts. Different buyers value digital transformation differently. Private equity firms split into two camps: those seeking unsophisticated targets they can improve post acquisition (paying bargain prices), and those hunting for technology platforms to build upon (paying premiums). Corporate strategic buyers often pay the highest multiples for smaller companies with superior technology capabilities, viewing them as their technology roadmap. Companies without digital strategies face a choice: accept lower valuations from buyers planning to modernize them, or invest now to become the platform others want to acquire. BizForesight is an AI platform that condenses months of sale preparation into one week. The platform solves two critical problems in the M&A ecosystem. For business owners, it provides AI driven assessment and preparation that clarifies goals, benchmarks readiness, and identifies value creation opportunities before going to market. For M&A advisors, it creates a steady stream of qualified, educated clients who understand why they need professional services. BizForesight streamlines the M&A preparation process by using AI to conduct interviews, analyze data, and generate insights that previously required months of manual work. Human experts still review and guide critical decisions, but AI handles the heavy lifting of data collection and initial analysis. Business owners gain clarity on their readiness and next steps. Advisors receive clients who are prepared and committed rather than just browsing options. The platform creates a referral ecosystem where advisors who contribute clients receive referrals back from other advisors' clients. Unlike lead generation services, BizForesight delivers engaged clients at the moment they need specific services. Investment bankers, attorneys, wealth managers, and accountants all participate, solving their biggest challenge: finding qualified clients without spending half their year on business development. Talbot West: https://talbotwest.com Capitalize Network: https://capitalizenetwork.com BizForesight: https://bizforesight.com Website: https://appliedaipod.com YouTube: https://www.youtube.com/@TheAppliedAIPodcast Spotify: https://open.spotify.com/show/6QOlkWGyyn2Ue0SrCckIE8 Apple Podcasts: https://podcasts.apple.com/us/podcast/the-applied-ai-podcast/id1834499760

17. 

    29 Aug 2025

    Kevin Williams On De-Risking AI Implementations

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Kevin Williams, founder and CEO of Ascend Labs AI, joins Jacob Andra, CEO of Talbot West, to discuss the reality of enterprise AI adoption. Despite the hype, most companies still aren't implementing AI effectively. Kevin breaks down the four pillars that drive real ROI: dev tools, product augmentation, workflow optimization, and strategic positioning against disruption. Insights from this episode: Why 30-40% productivity gains in development are achievable with proper AI tool adoption The "bug hunt" strategy that gets dev teams comfortable with AI-assisted coding How vibe coding works for experienced developers (and why beginners struggle) Product augmentation strategies using unstructured data analysis Workflow optimization opportunities hiding in every business process The insurance auditor case study: focusing human attention where it matters Sales augmentation through AI-powered call analysis and coaching Why cycle time reduction is a better ROI metric than headcount reduction The existential threat facing marketing agencies, modeling agencies, and SEO-dependent businesses Real risks vs. perceived risks in enterprise AI deployment High-risk AI applications: healthcare, credit data, insurance, hiring The BYOAI problem: 80% of employees under 30 using unauthorized AI tools Compliance challenges with upcoming state regulations (Texas, Colorado) How bias creeps into AI-powered hiring processes The importance of organizational manifestos and guardrails for AI use Kevin shares specific implementation examples: Commercial insurance policy auditing (automating signature verification) Customer service transcript analysis for actionable insights Automated credentialing for healthcare staffing Dynamic coaching for sales teams based on playbook adherence Meeting intelligence systems that capture institutional knowledge Both Kevin and Jacob emphasize that AI literacy across the organization is essential. While technical teams need deep expertise, every employee benefits from understanding AI's capabilities and limitations. The conversation addresses the human element of AI adoption: getting beyond the early adopters to achieve organization-wide implementation. Companies discussed include those in insurance, healthcare staffing, professional services, and manufacturing. Kevin explains how each industry faces unique opportunities and challenges in AI adoption. About the guest: Kevin Williams brings direct-to-consumer brand experience combined with machine learning expertise. After exiting his previous companies, he pivoted entirely to generative AI implementation. Ascend Labs provides both AI literacy training and hands-on implementation services, working with companies from small businesses to organizations with tens of thousands of employees. Kevin also serves as a fractional Chief AI Officer and contributes to the Utah Office of AI Policy and Responsible AI Initiative. About the host: Jacob Andra is the CEO of Talbot West (https://talbotwest.com) and host of The Applied AI Podcast. He leads Talbot West implementing AI solutions across enterprise, government, and defense sectors. Jacob brings hands-on experience spanning the complete AI implementation lifecycle, from feasibility studies through production deployment. He writes and speaks extensively on practical AI adoption. Jacob focuses on bridging the gap between AI's theoretical potential and operational reality for organizations navigating digital transformation. His work emphasizes measurable ROI and sustainable adoption strategies that align with business objectives.

18. 

    21 Aug 2025

    Jacob Andra & Stephen Karafiath Discuss Applied AI

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Jacob Andra sits down with Stephen Karafiath, technical co-founder of Talbot West, for a wide-ranging discussion on practical AI implementation in enterprise. They cut through the hype to address what actually matters: how businesses can achieve concrete efficiency gains through thoughtful AI deployment while avoiding common pitfalls. Topics Covered The State of Enterprise AI Innovation - Why large corporations like Oracle struggle to innovate with AI despite massive resources - The disconnect between Fortune 500 pace and mid-market agility - How smaller firms can move faster and deliver value more efficiently LLMs vs. The Full AI Spectrum - The false equivalence between AI and large language models - Machine learning algorithms that have been solving business problems for decades (fraud detection, predictive analytics, recommendation engines) - When LLMs excel and where traditional ML still dominates Cognitive Hive AI (CHAI) Architecture - Talbot West's ensemble approach to composable AI - Building modular capabilities that work together - The biological inspiration: how bee colony intelligence informs distributed AI systems - Antagonistic modules that interrogate results to reduce hallucinations The 2030 Thesis: Total Organizational Intelligence - By 2030, competitive organizations will have end to end AI integration - Building toward a central nervous system for business - The sci-fi vision made practical: companies with awareness of all their components - Why this outcome is evolutionarily inevitable (technologically feasible + competitive advantage) Security and Risk Management - AI hallucinations require process controls similar to managing human error - What executives overlook: data hierarchy and access controls - The Microsoft Copilot vulnerability demonstrated at DEFCON - Building proper authentication and authorization for both humans and AI systems Implementation Strategy: APEX Framework - AI Prioritization and Execution methodology - Five evaluation criteria for AI initiatives: 1. Most pressing needs (squeaky wheel syndrome) 2. Biggest impact on revenue and margins 3. Technical feasibility ("slam dunks" vs "meh" outcomes) 4. Cost and complexity (money, time, resources) 5. Strategic alignment (building toward total organizational intelligence vs creating silos) The Human-AI Partnership - Why 100% automation isn't the goal for the foreseeable future - Processes range from 20% to 80% AI involvement - Humans excel at relationships and oversight - AI handles repetitive tasks, freeing humans for higher value work Resources Mentioned Talbot West: https://talbotwest.com Cognitive Hive AI (CHAI): https://talbotwest.com/services/cognitive-hive-ai The Talbot West 5-Year AI Thesis: https://talbotwest.com/ai-insights/the-talbot-west-5-year-ai-thesis About the Speakers **Jacob Andra** is the CEO of Talbot West and co-founder of BizForesight, an AI-powered M&A platform built and partially owned by Talbot West. He focuses on pushing the limits of AI capabilities, especially in high-stakes use cases. **Stephen Karafiath** is the technical co-founder of Talbot West with 30 years in enterprise software. Former leader of the Developer Innovations Team at Oracle, left to found an AI SaaS startup before joining Talbot West to focus on consulting and implementation.

19. 

    21 Aug 2025

    Intro to The Applied AI Podcast

    Send us a text (https://www.buzzsprout.com/twilio/text_messages/2529203/open_sms) Welcome to The Applied AI Podcast. We're all about practical results. Hosted by Jacob Andra, the CEO of Talbot West, The Applied AI Podcast focuses on value creation from AI. We also cover the pitfalls and concerns companies need to address when implementing AI. Talbot West is a digital transformation consultancy and AI enablement partner for companies and other organizations. We help our clients adopt AI and machine learning technologies safely and effectively. *Bringing Clarity to AI Decisions* The only thing more expensive than a bad tech decision is indecision. **AI Prioritization and EXecution (APEX)**: Cuts through noise to identify which AI initiatives deliver maximum value. APEX evaluates opportunities across multiple dimensions for a clear roadmap from current state to AI-enabled future. *Strategic Consulting* Yes, we build AI systems. But first, we help you think through the strategic implications. Should you build or buy? Which capabilities matter for differentiation? How do you sequence initiatives for maximum impact? What are the hidden costs of waiting? Our consulting practice addresses the full spectrum. *De-Risking AI Implementation* AI carries legitimate risks. Data privacy concerns. Regulatory compliance. Operational disruption. Vendor lock-in. Black-box decision-making. We address these systematically. **Strategic Frameworks**: APEX for prioritization and execution Total Cost of Ownership (TCO) models for AI Maturity assessments for AI readiness Risk/reward analysis for initiative selection **Technical Capabilities**: Ensemble AI orchestrating multiple model types Composable architectures that evolve with needs Explainable AI for regulated environments Integration strategies for legacy systems **Organizational Enablement**: Change management programs AI literacy training Governance framework implementation Performance measurement systems *The 5-Year Thesis: Move Now or Get Left Behind* By 2030, AI-enabled organizations will operate at fundamentally different speeds than traditional competitors. This isn't speculation—it's already happening. The gap widens daily between organizations that act and those that analyze. *Common Misconceptions* "AI is just hype" - We separate signal from noise, focusing on proven applications with measurable ROI. "We're not ready for AI" - Perfect readiness is a myth. We help organizations start where they are. "AI will replace our workforce" - AI augments human capability. We show you how to enhance, not replace. "It's too risky" - Inaction is the biggest risk. We systematically address legitimate concerns. "We need to transform everything" - Start small, prove value, scale systematically. Jacob Andra serves on the board of 47G, an aerospace and defense consortium. His expertise spans high-stakes AI applications where both opportunity and risk are magnified. Connect: Podcast: https://appliedaipod.com (https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbWRZSW91OWxOVldBTHpYeG5TanFmMy10TlRid3xBQ3Jtc0tuOHFWU3RDbTZ5aFpQekFzREVwc0F0cC16dktJOGw3MDlIZWdoR2dZdFZ6OGM3OEVWblphSW9aaEFRYmpCX2lxVnU0Q3dBamdKNzZ3dHl6b2NsTDM0NWd1SVhiLXpzTTBkV29vOXRwQ0NIS2Rfal85WQ&q=https%3A%2F%2Fappliedaipod.com%2F&v=FzDPu5HILsI) Talbot West: (https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbmJrMWFyR05VOWVFaTNsUF9iblRfc20zTGQxQXxBQ3Jtc0ttN1huTGJhNEZMU0s2SVhVc0FMT2EwTkhZM2U0dFRiT01KWE05a0wxRXpKX05xNVgteHRsQVg0OVVxQUg0ckp6V1Y1TkJkcXFrak9faVp0TVpZcFhNOVRuNi1xaHh4RHJiMzVQMzhzWjRtekVYVTRhSQ&q=https%3A%2F%2Ftalbotwest.com%2F&v=FzDPu5HILsI)

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