The Applied AI Podcast

Jacob Andra

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

  1. AI In Restaurants: How Savory Promotes Human Interaction

    MAR 10

    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 InteractionChallenges of AI Integration in the Restaurant Industry Chapters 00:00 Diverse Range of AI Technologies and Use Cases

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

    FEB 3

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

    Send us a text 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, an AI systems engineering company that helps enterprises avoid the common pitfalls of complex digital transformation initiatives.

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

    12/20/2025

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

    Send us a text 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

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

    12/18/2025

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

    Send us a text 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

    13 min
  5. Constitutional AI With Bennett Borden and Jacob Andra

    11/11/2025

    Constitutional AI With Bennett Borden and Jacob Andra

    Send us a text 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

    39 min
  6. Will AI Take All the Jobs? Jacob Andra and Stephen Karafiath Say No

    10/31/2025

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

    Send us a text 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

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

    10/27/2025

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

    Send us a text 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.

    42 min
  8. Neurosymbolic AI and the Shortcomings of LLMs: Jacob Andra and Stephen Karafiath

    10/17/2025

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

    Send us a text 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 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

    35 min
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out of 5
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About

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

Information

  • Creator
    Jacob Andra
  • Years Active
    2025 - 2026
  • Episodes
    16
  • Rating
    Clean
  • Copyright
    © 2026 Jacob Andra
  • Show Website
    The Applied AI Podcast