Madan Moudgal is helping healthcare organizations use AI to improve operations without removing the human judgment that sensitive clinical decisions require. As Chief Digital Officer at Sagility, he leads technology transformation for a healthcare operations company serving U.S. payers and providers. Sagility’s Nurse Assist solution recently won an AI Excellence Award for helping clinical teams review prior authorization cases faster and more accurately.

In this episode, Russ and Madan explore why healthcare is one of the hardest industries to modernize with AI. Madan explains how legacy systems, strict regulation, data privacy requirements, and complex workflows make healthcare transformation different from other industries.

They dive into prior authorization, one of healthcare’s most difficult and controversial processes. Madan explains why the process exists, how it helps address waste, fraud, and abuse, and why the challenge is balancing cost control with patient access to appropriate care.

The conversation also covers why Sagility uses the term augmented intelligence instead of full automation. Madan explains that AI can summarize documents, extract relevant clinical details, compare information against guidelines, and provide recommendations, but nurses and clinical experts still need to make the final decision.

Along the way, Madan discusses domain-specific AI models, clinical language models, guardrails, PHI protection, data curation, AI governance, change management, and why successful healthcare AI requires careful testing, incremental rollout, and trust-building over time.

Topics Covered:

[00:00] Welcome and intro, Madan Moudgal and Sagility’s AI Excellence Award win

[00:32] Sagility’s background as a healthcare operations company

[01:21] Why healthcare and payment systems are so complex

[01:43] The challenge of adopting AI in a regulated healthcare environment

[02:37] Lessons from implementing technology change in healthcare

[02:47] Working around large legacy healthcare systems

[03:45] Why prior authorization is such a difficult healthcare problem

[03:57] Balancing waste reduction, cost control, and patient access to care

[05:27] Why Sagility uses augmented intelligence instead of automation

[05:40] Keeping humans in the loop for clinical decision-making

[06:46] Where AI can help and where humans must remain accountable

[09:14] Extracting and summarizing clinical data from case documents

[10:27] Why Sagility focuses on domain-specific AI models

[11:03] Building trust through clinical language models

[12:01] Why accuracy is essential in healthcare AI

[13:18] Guardrails for compliance, PHI, and regulatory requirements

[14:37] Reducing review time and what that means for patients

[15:35] Reviewing medical records, clinical guidelines, and recommendations

[16:34] How Nurse Assist supports nurse reviewers

[17:43] Early benefits from speed, efficiency, and lower costs

[18:38] Integrating AI with legacy healthcare systems

[18:56] Why data curation matters before AI can work effectively

[20:24] AI governance and aligning with client policies

[20:47] Change management in enterprise healthcare workflows

[21:50] Balancing innovation and risk management in healthcare

[22:42] Why healthcare AI rollouts cannot be rushed

[24:44] Whether healthcare will ever become fully automated

[25:06] Why healthcare is more likely to remain augmented than fully automated

[27:44] Other healthcare areas ready for AI transformation

[28:22] Automating simpler member and patient interactions

[29:27] Virtual agents and consumer expectations in healthcare

[30:50] Claims accuracy and payment integrity opportunities

[32:04] What healthcare may look like in the next 30 years

Asaf Goldstein is helping IT support teams use AI to create faster, more proactive, and more human customer experiences. As Senior Director of Global Customer Care at SysAid, he leads a global support organization using AI to identify trends, resolve issues faster, deflect routine tickets, and give agents better context before they ever speak with a customer.

In this episode, Russ and Asaf explore how AI is changing customer care from a reactive cost center into a proactive driver of customer loyalty. Asaf explains why the future of support is not just automation, but the right balance between AI, human empathy, and expert problem solving.

They dive into how SysAid uses AI across its support organization, including AI copilots, internal AI agents, sentiment analysis, ticket deflection, quality scoring, and proactive issue detection. Asaf shares examples of how AI helped his team identify critical issues, assemble engineering and DevOps teams quickly, and resolve customer problems before they escalated.

The conversation also covers the risks of over-automation. Asaf explains why AI should solve simple questions, summarize context, and guide agents, but also know when to hand a customer to a human. He shares how SysAid reduced unhappy customer survey responses from around 40 per quarter to 5 by combining AI-enabled insights with personal follow-up from team leads.

Along the way, Asaf discusses the rise of AI managers, the skills support agents need to stay relevant, why guardrails matter, and how companies can create customer service experiences that feel faster, smarter, and more personal.

Topics Covered:

[00:01] Welcome and intro, Asaf Goldstein and SysAid’s customer service award win

[00:28] SysAid’s background in IT service management software

[01:10] What is changing in AI-powered customer support

[01:26] Moving support from reactive to proactive

[02:33] Managing global customer care in an AI-driven environment

[02:39] How AI helps identify trends before customers report issues

[03:20] Using AI and Discord to detect critical customer issues

[04:22] Why a great support experience can increase customer loyalty

[04:57] Creating a “wow experience” in support

[05:41] Where pressure to automate support comes from

[05:57] How SysAid uses AI to resolve routine tickets

[07:30] Why complex support still needs expert human agents

[07:50] The risk of over-automating customer interactions

[08:55] Defining the human layer in modern support organizations

[09:26] Why personal touch still matters in technical support

[10:18] When humans are still absolutely critical

[10:30] Reducing unhappy customers through personal follow-up

[13:00] Empathy as a core part of customer service

[13:36] Where AI works well and where it falls short

[15:38] Deciding what gets automated and what stays human

[17:14] How AI changes the role of support agents

[19:04] Skills that matter most for the future of customer care

[20:38] The rise of AI managers inside support teams

[21:13] How AI helps agents personalize customer interactions

[22:27] How customer expectations will change as AI becomes common

[23:27] Common mistakes when rolling out AI in support

[23:47] Why AI answers need validation, formatting, and guardrails

[25:23] Lessons from the shift from phone support to chat support

[26:10] Metrics AI support managers should track

[27:52] Principles for adding AI without losing the human experience

[28:15] Guardrails, monitoring, and continuous improvement

[29:43] Final thoughts on creating wow moments with AI and people

Tim Ray is helping apartment communities verify renters faster, more fairly, and with greater trust. As co-founder and CEO of Verifast, he leads an AI-powered verification platform built for the U.S. multifamily market, where property managers need to confirm identity, income, assets, and fraud risk at scale without relying only on traditional credit scores.

In this episode, Russ and Tim explore why renter verification is becoming more complex as more people earn income through gig work, self-employment, side hustles, benefits, investments, and nontraditional financial paths. Tim explains why credit scores only show historical payment behavior, not a renter’s real-time ability to pay.

They dive into how Verifast uses open banking, direct-source data, AI, biometrics, phone carrier checks, email history, document analysis, and behavioral signals to help determine whether someone is who they say they are, makes what they say they make, and has what they say they have.

The conversation also covers the rise of sophisticated rental fraud, including fake IDs, synthetic identities, credit profile manipulation, fake documents, and organized fraud playbooks shared across social media. Tim explains why legacy systems often miss these risks and why property teams need purpose-built tools rather than asking leasing agents to act like private investigators.

Along the way, Tim discusses explainability, renter trust, Trustpilot reviews, human-in-the-loop review, AI-assisted workflows, gig worker verification, and Verifast’s vision for portable renter trust that could help applicants avoid paying multiple application fees when they do not qualify for a specific property.

Topics Covered:

[00:00] Welcome and intro, Tim Ray and Verifast’s AI Excellence Award win

[00:35] Tim’s founder background and Verifast’s focus on multifamily housing

[01:00] Powering trust at scale for large apartment communities

[02:11] Why nontraditional renters need better verification options

[02:39] How Tim joined Verifast as an investor and late-stage co-founder

[03:54] Scaling quickly with a lean team and limited capital

[05:08] Why credit scores do not tell the full renter story

[05:31] Propensity to pay versus ability to pay

[07:13] Why some financially stable renters are invisible to traditional screening

[07:54] Modern rental fraud and how fraudsters exploit apartment screening

[09:03] CPNs, synthetic identities, and social media fraud playbooks

[09:46] Why legacy property management systems miss these risks

[11:36] Why deposits and debits both matter in income verification

[11:46] Detecting cash cycling and fake income patterns

[13:34] Verifying renters with assets or investment income instead of W-2 income

[14:17] Biometrics, fake IDs, phone carrier checks, and email history

[15:51] Layering documents, bank data, criminal records, and identity signals

[17:13] Building explainability and transparency into renter verification

[17:42] Using Trustpilot reviews as a quantitative trust signal

[19:50] Why human-in-the-loop review matters for housing decisions

[20:18] Why every renter matters when application fees and housing are on the line

[21:58] How years of real-world data strengthen Verifast’s models

[23:34] How Verifast changes the property manager workflow

[25:41] Assessing gig workers, contractors, and side-hustle income

[26:16] Grouping income sources and showing the math behind approvals

[28:08] How Verifast speeds up verification compared with manual review

[29:34] Portable renter trust and reusing verified applicant data

[31:07] Helping renters find properties where they actually qualify

[31:29] Tim’s advice on earning trust as an entrepreneur

[32:42] Final thoughts on reputation, accountability, and trust at scale