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. 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.

Produced by Talbot West, a digital transformation and AI consultancy.

Dr. Alexandra Pasi (Lucidity Sciences) joins Talbot West 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 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 Learn more at talbotwest.com

#AppliedAI #MachineLearning #EnterpriseAI #StructuredData #SupervisedLearning #ComposableAI #ModularAI #AIStrategy #DigitalTransformation #PredictiveAnalytics

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.

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/
• Learn about safe AI implementation: https://talbotwest.com/services/ai-implementation-and-integration
• Explore The Applied AI Podcast: 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.

Jacob Andra, podcast host and CEO of Talbot West, 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

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 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.

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.

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
• Talbot West: https://talbotwest.com