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.