The Killer Use Case Podcast

In this episode of Killer Use Case, Sheldon Wolf, founder and CEO of Intellitary AI, discusses how artificial intelligence is changing the residential real estate industry. Drawing on more than 35 years of experience, Sheldon explains how Intellitary AI uses conversational AI to qualify prospective buyers, connect customers with high-performing agents, and streamline real estate transactions. He shares his customer-first business model, the importance of selecting the right brokerage partners, and why he believes AI should help real estate professionals rather than replace them. The conversation also explores changing consumer expectations, the limitations of AI, and his approach to building scalable businesses with long-term profitability in mind.

Takeaways

• Intellitary AI focuses on acquiring and serving customers rather than simply recruiting more real estate agents.
• Conversational AI can screen potential buyers, assess their financing readiness, and connect them with suitable agents.
• Selecting high-performing real estate agents helps improve customer service and reduce missed opportunities.
• The company's business model combines AI-driven customer acquisition with brokerage partnerships and transaction-based revenue.
• AI can automate administrative tasks, paperwork, and customer interactions while allowing agents to focus on personal service.
• Human expertise remains valuable when navigating complex, high-value real estate transactions.
• Changing consumer expectations are driving demand for faster responses, easier transactions, and more personalized service.
• Understanding future consumer behavior is essential when building technology businesses designed for long-term growth.
• Choosing the right investors and brokerage partners can be more valuable than pursuing rapid expansion.
• Building a profitable business requires understanding revenue opportunities, operational efficiency, and customer needs from the beginning.

Today's guest, Sheldon Wolf, can be found at:

Company Website: https://intellitary.com/

Website: https://sheldonwolf.com/

LinkedIn: https://www.linkedin.com/in/sheldon-wolf67/

Instagram: https://www.instagram.com/sheldon.wolf/

Threads: https://www.threads.com/@sheldon.wolf/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn: https://www.linkedin.com/in/hunterjensen/

Keywords

AI, Artificial Intelligence, Business Turnaround, Culture of Sales, Financial Transparency, SaaS, Startups, Lean Startup, MVP, Vibe Coding, Leadership, CEO Strategy, Customer Discovery, Technology Adoption, Consulting, Growth Strategy, Software Development, Automation, Market Fit, Efficiency

In this conversation, Mykel Salomon, founder of the Human Protocol Group and a global customer support leader, discusses how organizations can adopt AI without losing sight of the people behind the work. He explains why AI should be treated as a business transformation rather than a simple technology upgrade, and why companies need clear goals before choosing tools or automating workflows. Mykel and Hunter also explore the challenges of measuring AI ROI, managing rising AI costs, selecting the right models for each task, and increasing employee adoption. The conversation also looks at the future of customer support, where AI could automate much of the routine work while helping teams predict problems, reduce friction, and focus human talent on higher-value work.

Takeaways

AI adoption should begin with a clear business problem, not with selecting a tool.

AI should be treated as a business transformation rather than only a technology project.

Companies need to consider how automation will change roles, skills, and responsibilities across their workforce.

Employee adoption depends on clear communication about how AI will affect their work and future opportunities.

Measuring AI ROI remains difficult because implementation costs, adoption, and usage can change over time.

Model routing can help control AI costs by matching each task with the level of AI capability it actually needs.

Giving employees AI licenses without a clear strategy often leads to low usage and wasted spending.

Public-sector organizations face barriers to AI adoption that include budget limits, data concerns, outdated infrastructure, and lack of clear strategy.

AI could allow customer support teams to identify patterns and prevent problems before customers need to contact support.

The long-term value of AI may come from helping people build, create, solve problems, and spend less time on low-value repetitive tasks.

Today's guest, Mykel Salomon, can be found at:

Website: https://thehumanprotocolgroup.com/

LinkedIn: https://www.linkedin.com/in/mykelsalomon/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

Artificial Intelligence, AI Adoption, Business Transformation, Customer Experience, Customer Support, Automation, Human-Centered AI, AI Strategy, AI ROI, Model Routing, Workforce Transformation, Upskilling, AI Implementation, Generative AI

Bennett Borden, founder and CEO of Clarion AI Partners, joins Hunter Jensen to discuss how AI is reshaping law, software development, and professional services. Drawing on his background as a lawyer, data scientist, former CIA analyst, and AI leader in Big Law, Bennett explains why organizations need to combine generative AI with deterministic systems, strong controls, and measurable telemetry. He also shares how Clarion builds AI-powered legal systems, why the billable hour is giving way to value-based pricing, and how smaller firms can use AI to compete with much larger organizations.

Takeaways

• Bennett’s career spans intelligence, law, data science, and AI, giving him a unique view of how technology changes professional work.
• Clarion AI Partners combines legal advice, AI strategy, and software development within one firm.
• Bennett argues that much of the routine work lawyers perform can now be handled more efficiently by AI.
• Effective AI systems often combine probabilistic language models with deterministic software and rules.
• Organizations should measure how AI affects the real world rather than treating models as unknowable black boxes.
• Telemetry provides evidence of how an AI system performed and whether it stayed within defined legal and operational controls.
• AI governance can be built directly into systems by translating laws, regulations, and policies into technical requirements.
• Multi-agent systems can use specialized AI agents to monitor, challenge, and improve one another.
• AI efficiency is pushing professional services away from hourly billing and toward fixed-fee, task-based, and value-based pricing.
• Smaller firms can use AI to increase capacity, improve consistency, and compete with organizations that once had a major advantage in manpower.

Today's guest, Bennett Borden, can be found at:

Website: https://clarionai.com/

LinkedIn: https://www.linkedin.com/in/bennett-borden/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

Artificial Intelligence, Generative AI, Agentic AI, Multi-Agent Systems, AI Governance, Legal Technology, AI Strategy, Telemetry, Risk Management, AI Compliance, Legal AI, AI Agents, AI Regulation, Value-Based Billing, Law Firms

In this conversation, Richard Kersey, founder and CEO of Chirpper, explains how he is building an invite-only social network designed to prioritize verified human participation, accountability, and higher-quality online interaction. Chirpper uses a lineage-based vouching system where users are responsible for the people they invite, creating trust incentives that can help expose coordinated bot networks and discourage bad actors. Kersey also discusses the growing problem of bot-driven internet traffic, echo chambers, and declining trust in online content, as well as his approach to building Chirpper as a solopreneur using multiple AI agents for strategy, coding, testing, and critical feedback. The discussion explores how AI can support human creativity without replacing accountability, why anonymity and responsibility do not have to be mutually exclusive, and how adversarial AI agents can improve software development. Kersey also shares the challenges of maintaining context across multiple AI systems, thinking beyond immediate code generation, and proving whether human-generated online communities can produce more valuable content than automated networks.

Takeaways

• Chirpper is an invite-only social network built around a ledger of human vouches and invitation lineage.
• Users are accountable for the people they invite, with both positive and negative behavior affecting trust scores.
• The platform is intentionally designed for slow growth, prioritizing network quality over rapid user acquisition.
• Chirpper aims to identify coordinated bot behavior by analyzing relationships and activity across connected account networks.
• Kersey views bot proliferation as a major contributor to declining trust, authenticity, and human participation online.
• Chirpper is exploring a diversity-based scoring model that could elevate content supported across multiple independent lineage groups instead of reinforcing echo chambers.
• AI-generated or AI-assisted content is allowed on Chirpper as long as a real human stands behind the content and accepts responsibility for it.
• The platform is designed to preserve anonymity while still creating accountability through social trust and vouching rather than identity verification.
• Kersey uses multiple AI agents to help strategize, challenge ideas, generate code, test software, and execute development plans while keeping himself involved in decision-making.
• One of the biggest challenges of working extensively with AI agents is maintaining context, anticipating long-term architectural consequences, and recognizing when an AI system lacks information needed to make a sound decision.

Today's guest, Richard Kersey, can be found at:

Website: https://chirpper.com/

LinkedIn: https://www.linkedin.com/in/richardkersey/

Your host, Scott Turman, can be found online at:

Website: https://scottturman.com/, https://brightray.com/

LinkedIn: https://www.linkedin.com/in/scottturman/

IMDb: https://www.imdb.com/name/nm14602682/

Keywords

Chirpper, artificial intelligence, AI agents, social networks, human verification, bot detection, trust scores, accountability, anonymity, Sybil attacks, dead internet theory, echo chambers, AI governance, digital trust, content moderation

In this conversation, Su Belagodu, managing partner of Intellectus Advisors, discusses why successful enterprise AI adoption depends on more than choosing the right model. Drawing on her experience working with AI startups and enterprise teams, she explains the Human in the Loop Maturity Index, a framework for evaluating how effectively people and AI collaborate within organizational workflows. The discussion covers confidence-based routing, structured feedback loops, contextual training, governance, and the role of human oversight in increasingly autonomous agentic systems. Su also explores how organizations can move from reactive AI monitoring to adaptive governance, why workflow design matters more than simply adding people or technology, and what her research comparing tools such as Claude Code and Codex revealed about transparency, trust, and enterprise readiness.

Takeaways

• Successful AI adoption depends on designing effective collaboration between humans and AI, not simply deploying more advanced models.
• Human in the loop refers to placing qualified people within AI workflows who can evaluate, influence, and improve AI-generated decisions.
• Many enterprise AI adoption problems are operational and organizational rather than purely technological.
• The Human in the Loop Maturity Index evaluates how effectively organizations structure human oversight, AI performance, and collaboration.
• Confidence-based routing can determine when AI should act independently, when humans should review decisions, and when decisions should automatically be escalated to people.
• AI systems perform better when they are given rich organizational, customer, and domain-specific context before and during deployment.
• Feedback mechanisms should be built directly into the user experience so employees can easily correct or evaluate AI outputs as part of their normal workflow.
• Agentic AI increases the importance of governance because errors can cascade across multiple agents, tools, and automated actions.
• Management agents and embedded governance policies can monitor other AI agents and alert humans when oversight or system behavior begins to degrade.
• Enterprise AI success increasingly depends on workflow design, governance, domain knowledge, transparency, and human oversight rather than differences between leading frontier models alone.

Today's guest, Su Belagodu, can be found at:

Website: https://www.subelagodu.me/

LinkedIn: https://www.linkedin.com/in/subelagodu/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

Artificial Intelligence, Enterprise AI, Human in the Loop, AI Adoption, AI Governance, Agentic AI, AI Workflows, Human AI Collaboration, AI Oversight, Responsible AI, AI Strategy, AI Agents, AI Feedback Loops, AI Transparency, AI Trust

In this conversation, Rohit Nambiar, founder and CEO of SabaiHealth, discusses his transition from a long career in insurance leadership to building an AI-powered healthcare company focused on making health support more accessible, personalized, and engaging. He explains how SabaiHealth operates through messaging platforms such as WhatsApp, Line, and Telegram rather than a traditional mobile app, allowing users to interact through text, audio, images, medical reports, and wearable data. The discussion explores the company’s multi-LLM architecture, its approach to reducing hallucinations and controlling token costs, the regulatory challenges facing AI in healthcare, and the importance of keeping doctors involved in medical decision-making. Nambiar also shares lessons from his book, The Simplicity Trap, including why simplicity requires discipline and accountability, and reflects on emerging opportunities in mental health, sexual health, hormonal health, preventive care, humanoid robotics, and patient access to health data. Looking ahead, he believes AI will help healthcare become more collaborative, preventive, informed, and personalized, with patients playing a much more active role in understanding and managing their own health.

Takeaways

• SabaiHealth was created to address the limitations of general-purpose language models in healthcare and the low engagement rates common across health technology platforms.
• The platform is built around messaging services such as WhatsApp, Line, and Telegram, reducing the friction associated with downloading, registering for, and repeatedly opening a dedicated mobile app.
• SabaiHealth positions itself as a care companion rather than a doctor, helping users understand health information while directing treatment and medication decisions back to qualified medical professionals.
• The platform can combine user conversations, uploaded medical reports, images, wearable data, and other personal health information to provide more contextualized support.
• SabaiHealth uses multiple AI models through an orchestration layer, routing simpler and more complex queries to different models based on the requirements of each request.
• Managing AI infrastructure costs is becoming an important business issue, with token expenses emerging as a significant operating cost for companies using generative AI at scale.
• Healthcare regulation around generative AI is still evolving, particularly in areas such as data residency, privacy, compliance, and the acceptable use of large language models.
• Messaging-based healthcare can create a more private and approachable environment for sensitive topics such as mental health, men's sexual health, and female hormonal health, especially in markets where social stigma remains strong.
• Nambiar's experience with automation and digital transformation reinforced the importance of simplifying processes before applying new technology, a principle that also forms part of the thesis of The Simplicity Trap.
• Nambiar believes AI will give patients greater access to usable health data, improve preventive care, and shift healthcare toward a partnership model where patients are more informed and actively involved in their own care.

Today's guest, Rohit Nambiar, can be found at:

Website: https://www.sabaihealth.com/en

LinkedIn: https://www.linkedin.com/in/rohitcnambiar/

Blogspot: https://rohitnambiar.blogspot.com/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

AI healthcare, digital health, generative AI, large language models, patient engagement, personalized healthcare, health data, wearable technology, healthcare regulation, preventive care, mental health, telemedicine, RAG, token costs, digital transformation

In this episode, Minyang Jiang, Chief Strategy Officer at Credibly, shares how the company has approached generative AI adoption in financial services, from early experimentation to building sophisticated human and AI workflows. She explains how Credibly used generative AI to improve industry classification, risk analysis, data extraction, and emerging applications such as agentic pricing, while also learning where traditional automation is a better fit. The conversation explores the risks of bias, hallucinations, security, and overreliance on AI, along with the importance of human oversight and clear organizational guardrails. Jiang also discusses how leadership, employee education, experimentation, and access to enterprise AI tools can drive adoption while reducing shadow AI. Looking ahead, she argues that the strongest organizations will not simply use AI to cut costs, but will deliberately design human and AI hybrid operating models that strengthen creativity, decision-making, innovation, and long-term growth.

Takeaways

• Generative AI adoption should begin with experimentation and learning, rather than waiting for the technology to become fully mature.
• Credibly's early AI work focused on improving business industry classification and creating risk-adjusted categories that were more useful for lending decisions.
• Generative AI can create new value by connecting and interpreting data, rather than simply reproducing existing human workflows.
• Traditional rules-based automation is often better for repetitive, predictable tasks, while generative AI is better suited to work requiring reasoning, context, and flexibility.
• Trying to make generative AI exactly replicate human processes can limit its effectiveness and lead organizations toward the wrong use cases.
• AI is particularly strong at extracting and analyzing large amounts of information, but prioritization and final decision-making often require human judgment.
• Organizations need safeguards against bias, hallucinations, incorrect interpretations, model drift, security risks, and overly persuasive AI outputs.
• Successful AI adoption requires a combination of employee experimentation, dedicated technical or innovation teams, and strategic leadership from executives.
• Giving employees secure access to enterprise AI tools can lower barriers to experimentation and reduce the incentive to use unauthorized personal accounts.
• The future of work will likely center on human and AI hybrid models where people provide judgment, creativity, strategy, taste, and organizational context while AI expands their analytical and creative capabilities.

Today's guest, Minyang Jiang, can be found at:

LinkedIn: https://www.linkedin.com/in/minyang-jiang-6981a148/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

Generative AI, artificial intelligence, fintech, financial services, business lending, small business lending, risk assessment, industry classification, data extraction, data analysis, agentic AI, agentic pricing, AI agents, automation, robotic process automation, large language models, LLMs, GPT, Gemini, human AI collaboration, human AI hybrid, AI adoption, AI strategy, AI governance, AI guardrails, model bias, hallucinations, model drift, human oversight, decision-making, enterprise AI, shadow AI, change management, employee training, workforce development, future of work, innovation, SkunkWorks, AI experimentation, AI implementation, risk management, security, business intelligence, workflow automation, AI literacy, organizational culture, employee adoption, productivity, efficiency, creativity, strategic leadership, digital transformation, customer experience, underwriting, cash flow analysis, data parsing, retrieval augmented generation, vector databases, multimodal AI, model validation, enterprise technology, AI ethics

In this episode, Will Leach, founder and CEO of Mindstate Group, explains how behavioral science can help marketers understand what drives customer decisions beneath the surface. He discusses the gap between what people say and what they do, the limits of traditional market research, and the role of goals, motivations, regulatory focus, and cognitive biases in shaping behavior. Will also shares how his behavioral intelligence platform, Bevi, uses AI to assess advertising, improve copy, and help teams create more persuasive marketing. The conversation explores practical ways smaller businesses can use tools such as Claude and Gemini to build stronger customer profiles, apply behavioral insights, and improve campaign results. It also examines where AI still falls short, why human intuition remains essential, and how future marketing systems may help companies model and share the judgment of experienced leaders.

Takeaways

• Traditional market research often fails to predict behavior because people do not always understand or accurately report why they make decisions.
• Behavioral science helps marketers address the gap between what customers say and what they do.
• AI can improve marketing efficiency, but faster and cheaper content does not always produce better results.
• Effective behavioral marketing focuses on four factors: goals, motivations, regulatory focus, and cognitive biases.
• Customer goals can be functional, emotional, or aspirational, with aspirational goals often creating the strongest emotional response.
• Marketers can frame the same product around avoiding harm or gaining a benefit, depending on the customer’s mindset.
• Cognitive biases such as scarcity and anchoring can help customers make faster decisions and shape how they judge value.
• Businesses can gain useful behavioral insight by asking customers what matters to them and why it matters.
• AI tools can help identify customer goals and motivations, improve ideal customer profiles, and create more focused marketing copy.
• Human judgment remains vital because AI can produce polished work that still feels wrong, unclear, or disconnected from the customer.

Today's guest, Will Leach, can be found at:

Website: https://mindstategroup.com/

LinkedIn: https://www.linkedin.com/in/will-leach/

Your host, Scott Turman, can be found online at:

Website: https://scottturman.com/, https://brightray.com/

LinkedIn: https://www.linkedin.com/in/scottturman/

IMDb: https://www.imdb.com/name/nm14602682/

Keywords

Behavioral Science, Behavioral Marketing, Artificial Intelligence, Generative AI, Market Research, Customer Psychology, Consumer Behavior, Mindstates, Marketing Strategy, Customer Insights, Subconscious Decision-Making, Goal Theory, Customer Goals, Functional Goals, Emotional Goals, Aspirational Goals, Customer Motivation, Regulatory Focus, Cognitive Biases, Heuristics, Priming, Anchoring Effect, Scarcity Effect, Behavioral Design, Advertising, Copywriting, Customer Profiles, Ideal Customer Profile, Marketing Effectiveness, Marketing Efficiency, Human Intuition, Intuition Modeling, AI Content, Campaign Testing, Menu Design, Decision-Making, Claude, Gemini, Bevi, Mindstate Group, Marketing to Mindstates

In this conversation, Eric Rosenfeld, Chief Information Officer at ServRx, discusses how his team uses AI to improve pharmacy billing, claims management, and business process workflows in the workers’ compensation and personal injury space. He explains how ServRx moved from early document extraction challenges to more reliable AI-assisted systems that combine LLMs, Textract, orchestration layers, database checks, and human review. The discussion covers the limits of generative AI, the need for governance, the importance of keeping humans in the loop, how to measure AI value, and why companies should focus on practical use cases rather than replacing people. Eric also shares his view on the future of AI, including model selection, cost control, workload routing, and the need to compare outputs across multiple models.

Takeaways

• ServRx helps pharmacies get reimbursed faster and more fairly in workers’ compensation and personal injury cases.
• The company’s first AI use cases focused on interpreting and extracting data from complex explanation of benefits documents.
• Early LLM-only approaches were not accurate enough for financial workflows, which led the team to combine document extraction tools with LLM interpretation.
• Human review remains critical when AI handles claims, payments, legal documents, or other high-risk workflows.
• AI should support people by reducing manual work and speeding up decisions, not replace human judgment.
• Orchestration layers help control AI systems by adding checks, database validation, and workflow routing.
• Poor inputs can limit what AI can do, especially when documents are blurry, incomplete, sideways, or missing key information.
• AI cost control matters because token use can grow fast in complex or agentic workflows.
• ServRx measures AI value through new revenue, faster decisions, improved payer follow-up, and work that could not be done before.
• The future of enterprise AI may involve sending workloads to multiple models, comparing results, and choosing the right model for each task.

Today's guest, Eric Rosenfeld, can be found at:

Linkedin: https://www.linkedin.com/in/ericrosenfeld/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

AI, generative AI, LLMs, pharmacy billing, workers compensation, personal injury, claims management, explanation of benefits, EOB, document extraction, Textract, orchestration, human in the loop, prompt engineering, hallucinations, governance, database verification, business process outsourcing, legal workflows, payer communication, revenue recovery, token costs, model selection, workload routing, cost control, automation, healthcare technology, claims database, decision making, AI strategy

In this conversation, Mike Montague, founder and CEO of Avenue9, discusses how businesses can use AI in a human-first way to improve marketing, sales, and customer relationships. He explains why AI should support people rather than replace them, how strong strategy and context shape better AI results, and why generative AI is only one part of a larger set of tools that includes automation, machine learning, predictive analytics, and answer engine optimization. The discussion also covers practical AI use cases for content creation, sales calls, customer service, podcast repurposing, and business workflows, while highlighting the need for original human thinking at the center of every AI-driven process.

Takeaways

• Human-first AI marketing starts with improving the customer experience, not just saving time.
• AI works best when it supports people like an assistant, rather than replacing full roles.
• Strong strategy must come before AI, because AI can make weak ideas fail faster.
• Context engineering helps AI create better results for a specific business, team, and audience.
• No single AI tool fits every job, so teams need the right mix of tools for each task.
• Generative AI is powerful, but automation, machine learning, and predictive analytics still matter.
• Meeting transcripts, podcast transcripts, and customer service tickets can become rich sources of business insight.
• Answer engine optimization focuses on how people find businesses through AI-driven search and direct questions.
• Original human content gives AI stronger material to repurpose into posts, summaries, keywords, and sales assets.
• Agentic workflows, AI skills, and tools like Claude Cowork may shape the next stage of knowledge work.

Today's guest, Mike Montague , can be found at:

Website: https://avenue9.com/

LinkedIn: https://www.linkedin.com/in/mikedmontague/

Instagram: https://www.instagram.com/mikedmontague/

Your host, Hunter Jensen, can be found online at:

Website: https://www.barefootsolutions.com/

LinkedIn:https://www.linkedin.com/in/hunterjensen/

Keywords

AI marketing, human-first AI, content marketing, sales conversations, customer relationships, strategy, context engineering, generative AI, automation, machine learning, predictive analytics, answer engine optimization, AEO, SEO, LLMs, ChatGPT, Claude, Copilot, Grok, Gamma, Whispr Flow, meeting transcripts, podcast transcripts, customer service tickets, trust signals, LLMs.txt, LinkedIn, YouTube, Reddit, Wikipedia, AI search, marketing automation, sales calls, customer success, content repurposing, long-form content, agentic workflows, Claude Cowork, AI skills, Perplexity Sonar, knowledge work