Full Episodes | Insurtech Leadership Podcast

Introduction

What is an insurer actually buying when it signs an AI contract? Dan Griffith's answer is that the failures came from companies buying AI because they needed AI, and the wins came from companies buying a migration that finishes faster at a fixed cost. Tim Vieyra runs Comotion AI, a Cape Town firm that spent twelve years building analytics and policy administration software other companies put their name on, and is now selling in the US under its own. Dan is advising Comotion on that entry, so this conversation runs two threads at once: what has to be true about an insurer's data before any AI project is real, and what has to be true about a company before it tries to sell into the American market.

Guest Bios

Tim Vieyra is a qualified actuary who left corporate financial services in 2013 to co-found Comotion, a software and data firm serving life insurance, investment, and healthcare clients. Comotion built the analytics engine behind TAI Insights and has worked with RGA South Africa. Comotion AI is the platform version of that work, putting governed, queryable structure over insurance, finance, and mortgage data, with data migration and profiling as the lead use case. Dan Griffith spent thirty years in enterprise sales, including a run as the first US hire for a South African company he helped scale from $3M to $150M, and now runs Greater Gain Group, which helps international software companies land and scale in US insurance and financial services.

Key Topics

-Buying an outcome, not AI - Griffith argues the failures came from companies that bought AI because they needed AI. What Comotion's customers buy is a migration that lands faster at a fixed acquisition cost.

-Profiling in week one instead of month six - Vieyra explains that migration teams have historically found their data problems only after loading a target system and running reports. Comprehensive profiling on ingest moves that discovery to week one or two.

-Governance resonated least - Griffith expected governance to carry the pitch and found it landed weakest, because governance is not a problem until it becomes one and the industry has not settled what the word means.

-Narrowing the message on purpose - Vieyra describes shifting from "we can help with a broad array of problems" to two named use cases. That limits bandwidth, but it cuts through the noise faster.

-South Africa versus the US - A smaller market rewards land-and-expand relationships and tolerates a muddier value proposition. The US demands a dialed-in problem statement and credibility, and then it opens up.

-ICP first, everything else after - Griffith walks through the full-day workshop that set Comotion's ideal customer profile, and why a pilot should be priced as a percentage of the full deal and bounded by either time or scope.

-Reset advice for stalled AI programs - Vieyra's answer for anyone whose AI initiative is not working is to re-evaluate the project against what the team knows now, not what it knew when the decision was made.

Notable Quotes

"Hold on to your paradigms loosely. Be humble enough to know that what you know now is not what you knew when you made the decision. And be willing to cut your losses and move to things that work."

"We see failures all over the place because people just bought AI because they needed AI."

"Governance isn't a problem until it's a problem."

"In the US you have to have a much more dialed in and focused value proposition. You have to have a very, very clear problem that you're solving and credibility on why people should believe you. If you tick those boxes, the market is open to you."

Resources

Guests:

Comotion AI: https://www.comotion.ai

Comotion Business Solutions: https://www.comotion.us

Tim Vieyra on LinkedIn: https://www.linkedin.com/in/timalive/

Greater Gain Group: https://www.greatergaingroup.com

Dan Griffith on LinkedIn: https://www.linkedin.com/in/dangriffithsr/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Why would an industry that governs almost everything still leave AI sitting on the back burner in the workflows where it would matter most? Earnix research puts AI in most or some workflows at 81% of insurers, and 92% of them run formal AI governance reviews, but fewer than one in three executives strongly agree those reviews are keeping pace with regulation. Aaron Wright, VP of Professional Services for the Americas at Earnix, spends his working life on the far side of that gap, and he argues it is not really a technology problem at all.

In this episode, Josh Hollander and Aaron work through what separates transformation from transformation theater, why modernization programs tend to fail three months before go-live rather than at it, and where AI is already moving numbers a CFO would recognize.

Guest Bio

Aaron Wright is VP of Professional Services, Americas at Earnix, and a Fellow of the Casualty Actuarial Society. He started out of school with a statistics degree as an actuarial analyst at Farmers Insurance, and was on the phone with a Department of Insurance defending the math behind a rate change within six months. He then spent roughly twenty years at USAA across pricing, underwriting, state management, catastrophe management and data science, closing out with a modernization mandate. He now leads the teams that arrive after a carrier has bought the technology and someone has to make it actually work.

Key Topics

-The governance gap - Formal reviews are nearly universal, but the confidence that they track where regulation is heading is not, so real operational decisions move at a measured pace.

-"We have AI" versus AI on the balance sheet - Aaron separates the places where AI is moving cost per ticket and handle time from the places where it is still hype.

-Where programs actually fail - Not at go-live, but months earlier, when somebody has to kill a customization or force a business unit to change and nobody holds the authority to make that call.

-The scaffolding problem - Carriers have built so many workarounds around legacy systems that the workarounds, not the systems, are often what hold them back.

-Parallel-process fatigue - Running the old process alongside the new one creates burnout that can kill a project's momentum, and only leadership can set expectations around it.

Vibe coding has a ceiling - Aaron will build a prototype to show the art of the possible, and he will not put it into a governed insurance environment.

-AI that enables expertise - One Earnix customer sees about twelve months of average tenure in the call center against eighteen months to train a representative fully, which is the kind of math that makes embedded, governed AI worth the effort.

Notable Quotes

"Saying you have AI and actually having it be something that hits the balance sheet that your CFO cares about is a very different thing."

"Most programs are not going to fail at the go live. They're going to fail three months before it, when somebody has to make that unpopular call and there's nobody with authority to actually make it happen."

"The average tenure for call center representatives is about twelve months at their company, and it takes eighteen months to get them fully trained. Well, that doesn't work."

"Our view of how AI should be used within insurance is to enable expertise."

Resources

Guest:

Earnix: https://earnix.com/

Aaron Wright on LinkedIn: https://www.linkedin.com/in/aaron-wright-fcas/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Ask an insurance operator to write down how a policy endorsement actually gets processed, and you will usually get about six steps. The real number is closer to fifty. The other forty-four live in the head of whoever has been doing the job longest, and they leave when that person does.

Giancarlo Stanton, Co-Founder and CEO of Magiq, has built a company around that gap. His argument is that you have to know what the process actually is before you automate it, and most operations don't. The industry is buying agents to handle work nobody has written down, which is how you end up with a system whose decisions nobody can explain.

In this episode, Josh Hollander and Giancarlo get into what it takes to pull a process out of a competent person's head, why the exceptions matter more than the normal path, and what changed in one support operation once someone finally mapped the work.

Guest Bio

Giancarlo Stanton is Co-Founder and CEO of Magiq. He started in complex commercial litigation, moved in-house, and became General Counsel and VP of Claims at Swyfft before serving as Chief Operating Officer at Cover Whale. Along the way he advised Obie, FRG Specialty, and Sunfish. That path put him in the back office of regulated businesses during periods of fast growth, which is where he formed the view that the minutiae are the whole game. He founded Magiq with Nick Eich, previously at Groupon and then on the engineering team at Kin, to build the thing he kept needing as an operator: a way to go from what should get done to proof that it actually happened.

Key Topics

-Your best people cannot describe their own jobs - When something becomes routine it becomes invisible, so asking an expert to write down what they do all day produces a partial answer at best.

-The mirror problem - Magiq shows an organization its own process as steps one through fifty, and the response is almost always that leadership knew about step one and step fifty and assumed the rest.

-Insurance runs as an ecosystem, not as silos - When billing misses a cancellation for nonpayment, claims pays a loss that should never have existed, which is why Giancarlo argues point solutions aimed at one function cannot fix the operation.

-The exceptions are where the value sits - The flat cancel and rewrite that one person handles differently today than yesterday is what turns a bordereau into a conversation with your reinsurer about why the math doesn't math.

-What mapping the work actually did - A support operation went from a one-in-five call response rate and an 8.8 day email response time to 96-97% call response and 5.8 hours, seven months in.

-Sales is a process too - Mapping the inbound lead decision tree and empowering producers to end calls that were never a fit drove a 23% improvement in per-producer output and a $10 million gross written premium lift over a seven month engagement.

-The staffing question - Giancarlo makes the case that doing more with the team you have lands better with buyers than headcount reduction, and that a business only profitable with no employees was never really profitable.

Notable Quotes

"You can't automate things you don't understand. And that, I think, is just objectively true. But that doesn't stop people from trying."

"No one is sitting there running a business saying, I need more AI. They're saying, I need less cost, or I need more sales, or ideally both. And probably fewer errors."

"People don't do things incorrectly because they want to. They do things incorrectly or inefficiently because they think they're doing them correctly, but it's eight thirty at night and it's their fiftieth endorsement."

"If the only way your business is profitable is for there to be no employees, then you probably just don't have a profitable business to begin with."

"It's really about making sure the mistakes don't happen, not making sure the people aren't there."

Resources

Guest:

Magiq: https://usemagiq.com/

Giancarlo Stanton on LinkedIn: https://www.linkedin.com/in/giancarlo-stanton-1b011a59/

Magiq on LinkedIn: https://www.linkedin.com/company/usemagiq

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What does an insurer actually own after a few years of buying AI one use case at a time? Feathery co-founder Zack Khan argues that the answer decides whether AI ever produces more than incremental gains, because a workflow you cannot change without filing a vendor ticket is a workflow you are renting. Fresh off a $30 million round led by Portage, with Allstate and Erie both in as strategic investors, Khan walks host Joshua R. Hollander through what it takes to put workflow control in the hands of the operating teams themselves.

Guest Bio

Zack Khan is Co-Founder of Feathery, the AI operating and decisioning system for financial services. He started as a software engineer building complex forms at Robinhood and Nextdoor alongside co-founder Peter Dun, then became the fifth employee at Hightouch, where he led marketing as the company grew toward a $3 billion valuation. Khan and Dun founded Feathery in 2021 as a developer-focused form builder and grew it into a platform that now serves more than 300 firms, including Tokio Marine, Hiscox, Baldwin Group, and Hylant, orchestrating submission intake, virtual inspections, quoting, and benefits workflows across carriers, MGAs, and brokers. Feathery ran profitably before raising its $30 million round in July 2026.

Key Topics

-Vendor-led vs. team-owned workflows - Why the last generation of insurance software put every change behind a vendor ticket, and what changes when underwriting and operations teams define workflows in natural language instead.

-Copilot licenses vs. step changes - Giving everyone a copilot produces small gains; redefining one workflow end to end took a carrier from an eight-hour time to quote down to fifteen minutes.

-Orchestrate rather than replace - Feathery's agent, Robin, works across existing rating engines, AMSs, and even legacy desktop applications, automating the data entry instead of ripping out the system.

-Rules where you want them, judgment where you need it - Deterministic guardrails handle rating inputs, while objective-driven agents handle tasks like checking a virtual inspection for a pool or a tree touching the roof.

-The Baldwin Group benefits example - Custom employee benefit guides that took 20 to 30 or more hours to build now generate in minutes, across 10 to 15 assets and multiple languages, and the team's hours moved to consultative work.

-What a platform actually is - Khan's test is control: if you cannot apply your own business logic to your own workflow without a vendor ticket, you bought a point solution.

-Start in specialty lines - Fast-growing lines with less tech debt and less red tape are where AI transformation finds its first champions inside a large carrier.

Notable Quotes

"An actual platform gives you control. If you don't have fundamental control over the actual experience, and you're applying your own firm-specific business logic to your workflow, that is a point solution."

"If your rating engine is working great, it's just the annoying part is typing data into it. You don't rip out the whole rating engine; you just automate the data entry part of it."

"Some of our customers have gone from an eight-hour time to quote to less than fifteen minutes responding to producers. That's the stuff that actually will impact your bind ratio."

"For AI to truly have the impact you want, you need to redefine the workflow, think about the end-to-end ideal process that you want your best underwriter, your best claims adjuster, your best producer to be following."

Resources

Guest:

Feathery: https://www.feathery.io/

Zack Khan on LinkedIn: https://www.linkedin.com/in/zackkhan101/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Most carriers can say how many AI pilots they are running. Far fewer can say where every automated decision in the business sits. That gap is the subject of this episode.

Josh Hollander is joined by Karl Canty and Paul Laurent of Artefact, the global data and AI consultancy, for a working conversation about the four frameworks converging on US insurance: the NAIC model bulletin, California's ADMT rules, Colorado's algorithm governance regime, and New York's DFS circular letter. Paul argues that this combination has made US insurance the most completely defined AI governance environment anywhere, and that the scope reaches well past the AI programs carriers are proud of. Rules engines, credit scores, telematics feeds, and vendor models are all inside it.

Karl's argument is that carriers moving on this for the first time are not first movers, they are late, and that the inventory work the rules force on you is the same work that makes your own data usable for growth.

Guest Bio

Karl Canty is Partner and US Insurance Lead at Artefact, where he is building the firm's North American insurance practice. He has spent about twenty-five years in insurance data, technology, and operations, including advisory roles at PwC and EY, a data and AI leadership role at EXL, and a partnership at Capco. He writes for Digital Insurance on where AI actually pays for carriers.

Paul Laurent is Head of AI Risk and Data Trust at Artefact, running the firm's AI governance advisory practice across North America. He describes himself as a technical lawyer: criminal prosecutions early on, a master's in computer science, then a decade at Oracle translating regulatory obligations into working architecture, followed by identity, data security, privacy, and now AI. He has contributed to NIST framework work across cybersecurity, privacy, and AI risk management.

Key Topics

-The four timelines - New York in force since July 2024, an NAIC pilot ending in an AI systems evaluation, California live with enforcement from January 2027, and Colorado already amended toward California's standard.

-Scope is wider than AI - California's rules reach deterministic tools and legacy rules engines, which pulls decades-old underwriting and claims infrastructure into the conversation.

-The vendor chain is not a place to put the risk - The NAIC treats external consumer data and information systems, including credit scores and telematics, as the carrier's responsibility. -Compliance cannot be delegated to a third-party vendor.

-Human in the loop rarely survives the test - California wrote three bright-line conditions for human review. In Paul's assessments, more than eighty percent of processes carriers describe as human-reviewed do not meet them.

-Nobody can produce the inventory - The first question in every engagement is where the business makes significant decisions with automation. Paul gets the same answer every time: we have some, we don't know where all of it is, and there is probably shadow.

-CalPrivacy is already staffed and fining - Roughly 150 complaints actioned, capacity for a hundred at a time, and eight-figure fines appearing.

-Where the work turns into growth - Karl's case for entity resolution, a real AI roadmap, and a durable enterprise AI competency coming out of the same assessment that satisfies the regulators.

-The pause happening right now - Paul's most governance-mature client has stopped agentic AI development until these questions are answered, and the conversation inside carriers has moved from the CIO to the chief data officer.

Notable Quotes

"The most completely defined spot on the global AI governance workspace right now is the US insurance industry."

"The vendor chain for NAIC is explicit. You cannot delegate compliance responsibility to a third-party vendor."

"I don't think that companies who are moving right now for the first time are first movers. I think you're already late."

"If you could tech your way out of this problem, it would have happened already."

Resources

Guests:

Artefact: https://www.artefact.com/

Karl Canty on LinkedIn: https://www.linkedin.com/in/karlcanty/

Paul Laurent on LinkedIn: https://www.linkedin.com/in/paullaurent/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What happens to a company that has run two hundred proofs of concept and still cannot point to one that paid for itself? Rafael Broshi says that is roughly where financial services sits after two years of generative AI experimentation, and he does not think the models are the reason.

Broshi has an unusual vantage point on this. He built a brand new insurance product, took it to the admitted market, watched it fail to find buyers, and rebuilt his company around the software his team had written to run it. In this episode he walks host Joshua R. Hollander through why demos survive and production launches do not, what has to be written down before a process can be automated at all, and where an AI agent should never be allowed to improvise.

Guest Bio

Rafael Broshi is co-founder and CEO of Notch, an AI platform for building and deploying agents inside highly regulated industries. He spent nearly ten years in the Israeli military, as did both of his co-founders, and the three of them started Notch about five years ago as something else entirely: a specialty product insuring digital assets including crypto and social media accounts, launched in the admitted market across 47 states with Hartford Steam Boiler. That product did not find a market. The platform the team built to run their own MGA became the company. Notch now deploys agents across customer experience, claims intake, document ingestion, and underwriting workflows for insurers, banks, and asset managers, and is backed by Lightspeed, Jibe Ventures, Munich Re Ventures, and Phoenix.

Key Topics

-Why a new insurance product is the wrong startup bet - Cyber insurance launched in 1997 and took until roughly 2015 to catch on, and no startup survives an eighteen year runway regardless of how much it raises.

-Innovate on distribution, not on the product - Broshi argues that the insurtechs that generated real returns over the past decade changed how insurance was sold rather than inventing new coverage.

-What happens between a working POC and a failed launch - Agents are non-deterministic, so they break as knowledge, guardrails, and process complexity scale, and by the time a project reaches that point its trajectory is easy to change.

-Why he does not believe in forward deployed engineers - He calls the FDE model system integration under a new name, and asks what happens at use case number two hundred inside a five thousand person carrier.

-Why breaking down the process is the part that actually fails - He ties the 75% digital transformation failure rate to the difficulty of mapping a process across departments, and describes AI agents as RPA 7.0 with the same underlying requirement.

-Where agents stay probabilistic and where they must not - Empathy in a first notice of loss conversation cannot be scripted, but the order of the fifteen intake questions cannot be left to a model.

-The subagent architecture behind auditability - Fifteen to twenty subagents run concurrently in a single conversation, each owning one question, so no action happens without a model that can explain it.

-What disqualifies a process entirely - No API access to the data, prediction problems that belong to older machine learning, and any process with no written SOP.

Notable Quotes

"With Gen AI it's extremely easy to show a demo and to show something working in a POC, but when it reaches production, things go south extremely fast."

"Launching a new agent is a business transformation to a specific department. And it's an HR problem as much as it's an IT problem."

"The scars that you now carry on your back, those are your only assets that are worthwhile."

"Everyone says they're doing AI. Don't choose vendors according to presentations. Always do multi-vendor POCs."

Resources

Guest:

Notch: https://www.notch.cx/

Rafael Broshi on LinkedIn: https://www.linkedin.com/in/rafael-broshi-435a77105/

Notch on LinkedIn: https://www.linkedin.com/company/notchapp/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What happens to a claims operation when the manipulated photo comes from an ordinary policyholder instead of a fraud ring? Verisk's 2026 fraud study found that 55% of Gen Z consumers and 49% of millennials would make a small rule-bending edit to a claim photo or document, and Aviva stopped £233 million of suspected claims fraud last year with a growing share of it built on AI-generated images and manipulated paperwork.

Jeppe Nørregaard spent seven years building AI systems to detect fake content before deciding the detectors were going to lose. He now runs a company built on the opposite bet, which is that you protect real content instead of chasing fake content. In this episode he walks host Joshua R. Hollander through why detection breaks down under generative AI, what signing content at the point of capture actually asks a carrier to change, and why a flat fraud number next year should worry a claims leader rather than reassure them.

Guest Bio

Jeppe Nørregaard is co-founder of InReality, a Copenhagen company that cryptographically signs photos, video, and documents at the moment they are captured so whoever receives them can prove the content is real and unaltered. He holds a PhD from the Technical University of Denmark, where he worked on AI and misinformation, and his research on how fake news detection degrades over time and under attack is among his most cited work. He did a research stay with an American research group in upstate New York studying AI for misinformation detection, and he sat alongside the researchers building deepfakes while he was building the detectors. InReality works today with news organizations and insurers, and is backed by the Deloitte Innovation Accelerator, the IBC Accelerator, and Antler.

Key Topics

-Why detection loses the arms race - Across seven years of research, Jeppe watched AI strengthen the attacker faster than it strengthened the defender, first with bots and automation and then with deepfakes.

-Flipping the problem - InReality does not evaluate fake content at all. It builds cryptographic guarantees around real content and treats everything outside that pipeline the way the internet already works.

-What a flat fraud number actually means - If detected fraud stops climbing while deepfake use keeps rising, the likelier explanation is that carriers have stopped seeing it rather than that it stopped happening.

-Where signing happens inside a claim - The policyholder photographs the damage through the carrier's own app, and the security sits inside that capture rather than as a review step afterward.

-Cryptography instead of prediction - Detection produces false positives because it is a prediction system. A signature either verifies or it does not, and InReality uses post-quantum signatures.

-Why they refused to build on a blockchain - A blockchain proof cannot be removed, which collides with GDPR's right to be forgotten, so they built an alternative way to produce the same cryptographic proofs.

-What to do with a detection budget already spent - Jeppe separates deepfake detection, which he would stop funding, from the rest of the AI claims stack, which he says carriers should keep investing in.

Notable Quotes

"We don't actually deal with fake content at all in our company. We completely ignore fake content. Instead, we build security guarantees for real content."

"I sat next to the guys who did deepfakes. They tried to deepfake Barack Obama, and I tried to find misinformation. So I knew where this was headed."

"A lot of money has gone into deepfake detection, and fundamentally we believe that it was a mistake, but it's a mistake that everyone made across the planet."

"If we start to see a flattening or a decrease in the amount of fraud that we find, then I would say we have a real problem, because it most likely means that we don't catch it."

Resources

Guest:

InReality: https://inreality.io/

Jeppe Nørregaard on LinkedIn: https://www.linkedin.com/in/jeppe-n%C3%B8rregaard/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What does it take to launch an insurance product that did not previously exist, get it live in twelve states in under five months, and run it with a deliberately small team? Vital Coverage Insurance Services did exactly that, and in this episode Josh Hollander brings both sides of the build into the same conversation.

 

Arvind Kaushal, Co-Founder and CEO of Cogitate, and Justin Krone, COO of Vital Coverage Insurance Services, join Josh for a rare three-way conversation on the show. They cover why Justin left a twenty-year carrier career to run an MGU, why Arvind tells founders to underwrite their technology partner the way they would underwrite a risk, and where each of them draws the line on AI in an underwriting workflow. They do not fully agree on that last one, which is the most useful part of the discussion.

 

Guests

Arvind Kaushal co-founded Cogitate in 2012 and leads the company as CEO. He came to insurance software from the operator side. Before founding Cogitate he was CFO and CIO at Insurance House, which meant, in his words, that he was signing the checks and responsible for the systems at the same time. The first version of what became the DigitalEdge platform was not a product, it was a fix for his own company's broken workflows. Earlier in his career he spent eight years at Delta Air Lines, including work as an architect on Delta.com, and started out at Wipro in Mumbai.

 

Justin Krone spent twenty years at CNA Insurance, the last ten running small business and small commercial strategy, before becoming COO of Vital Coverage Insurance Services. Vital is a lean, single-product MGU selling a first-of-its-kind business continuity coverage for small medical practices, protecting physician, dental, and veterinary offices against the loss of a key practitioner. The program launched across twelve states in under five months and is now live in seventeen.

 

Key Topics

-Underwrite your partner. Arvind's rule for choosing a technology vendor is to run the same diligence on the partner that you would run on a risk, because every provider in the market will tell you they can do everything.

-Principles over capabilities. Justin has seen most of the policy administration platforms on the market, and argues the deciding factor was not the feature comparison but whether the two organizations approached problems the same way.

-The lean MGU thesis. Vital was designed so that a tenfold jump in quote volume would not require materially more staff, and that constraint shaped the underwriting model as much as the technology stack.

-AI as a guardrail rather than an autopilot. Arvind's position is that AI should surface the risk a human misses under volume pressure and flag what falls outside appetite, while the decision stays with the underwriter.

-The case for keeping AI out of underwriting. Justin built a deliberately tight, binary risk-selection model and uses AI heavily across marketing, research, and operations while leaving underwriting alone for now.

-Why the culture change is harder than the tooling. Cogitate began its AI-first push three years ago and stumbled early, and Arvind argues the models were never the difficult part.

-Waiting for regulation to catch up. Both guests work through the NAIC AI model bulletin and what compliance looks like in practice when the carrier remains responsible for what a vendor's AI does.

 

Notable Quotes

"I don't think of AI in underwriting as an autopilot. I think of it as a guardrail."

 

"It's not about the tools or LLM models. It's actually about the mindset. The tools are the easy part. Shifting how people think is the hard part. And everyone underestimates it."

 

"It's not that they have the best capabilities out there. It's that our principles aligned."

 

"Ideas are easy and execution is hard. And the one thing that I think AI might be kind of changing is that second piece."

 

Resources

Guests:

Cogitate: https://cogitate.com/

Arvind Kaushal on LinkedIn: https://www.linkedin.com/in/arvind-kaushal-cogitate-digital-insurance/

Vital Coverage Insurance Services: https://myvitalcoverage.com/

Justin Krone on LinkedIn: https://www.linkedin.com/in/justin-krone-78b96563/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

 

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

What would it take for businesses to treat flood the way they treat fire? At a flood-exposed site, flooding is now twice as likely as fire, and 99% of those businesses have a fire plan. Only 8% have a flood action plan, down from 30% a year ago.

In this episode, Joshua Hollander talks with Jonathan Jackson, CEO of Previsico, about why preparedness collapsed while adoption of early warning systems rose from 35% to 49%, and what the insurance industry could do about it, because it has closed a gap like this before. Listeners will leave with a working picture of how property-level flood forecasting gets built, bought, and priced into insurance programs, and what a founder-led CEO succession actually takes.

Guest Bio

Jonathan Jackson is the CEO of Previsico, the flood forecasting company he spun out of Loughborough University in January 2019 while serving as the university's entrepreneur in residence. Previsico predicts surface water flooding at individual property level up to 48 hours ahead, generates a new forecast every hour, and is live with Zurich, Liberty, Generali, Marsh, and around 200 corporates. Previsico is Jackson's fourth start-up, after ventures in telecoms, agriculture (Farming Online, the UK's longest-running B2B internet business), and CPG digital promotions. He hands the CEO role to Mark Trumper at the end of October and will stay on through the transition.

Key Topics

-Why flood plans collapsed from 30% to 8% - A broader survey base pulled in mid-sized firms with thinner risk management, and warning users discovered their generic emergency response plans don't actually cover flood.

-The smoke detector playbook - US insurers drove fire alarm adoption from 25% to over 70% in about six years through premium incentives, and Jackson argues flood needs the same treatment now that US direct flood losses run five times fire.

-What 48 hours of warning buys - Balfour Beatty went from a multimillion dollar loss on a rail bridge project to zero loss on the next flood, with a 36-hour first warning, staged action plans, and a sensor-triggered evacuation.

-How insurers package prevention - Zurich Municipal embeds Previsico warnings in its premium at no added cost, while Liberty issues risk management bursaries (underwriting credits in US terms) to nudge exposed clients toward prevention.

-Why surface water is the hardest flood to forecast - Every storm floods differently depending on track and intensity, which is why a live hydrodynamic model, rather than a static flood map, is required.

-Cracking the US market - Under 5% of US properties carry flood insurance, carriers have limited appetite, and brokers and captives carry more of the load, so Previsico entered customer-led, with proof points at the Port Authority of New York and New Jersey, the MTA, and Governors Island.

-A three-year CEO succession - The handover stretched from a board conversation through a three-investor fundraise and a long search before Mark Trumper's arrival this October.

Notable Quotes

"Flood is five times bigger now than fire in terms of direct loss in the US. From an insurance point of view, it's the new fire."

"They went from a multimillion dollar loss to a zero loss and were able to resume work the next day."

"The original reason for us going into the US is because the customers wanted us over there, particularly the insurers."

"It doesn't work like that in my life, it seems, because we then decided that actually we needed to go and do a fundraise."

Resources

Guest:

Previsico: https://previsico.com/

Jonathan Jackson on LinkedIn: https://www.linkedin.com/in/jonathan-jackson-a393102/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Introduction

Most carriers have stopped arguing about whether the model is accurate. So why does an MIT study keep finding that roughly 95% of AI pilots show no return on what was spent on them?

Dr. Gleb Tsipursky spent fifteen years as a behavioral scientist in academia before moving into consulting, and his answer is that the failure is emotional rather than technical. He names three emotions that block adoption inside insurance organizations: fear of losing the job, threat to professional identity, and shame about being seen using the tools. The third is the one Josh pushed back on, twice, and Gleb did not give ground.

The episode is a companion to the July conversation with Stan Smith at Gradient AI, who argued that an accurate model gets ignored when an underwriter cannot follow its reasoning. Gleb takes it one layer down. Even when the reasoning is legible, the underwriter, the adjuster, and the auditor may still decline to use it, and the fix belongs to executives and middle managers rather than the data science team.

Guest Bio

Dr. Gleb Tsipursky is the CEO of Disaster Avoidance Experts, a Columbus, Ohio consultancy advising leadership teams on AI adoption and decision making. He holds a PhD from UNC-Chapel Hill, spent seven years as a professor at Ohio State studying behavioral economics and cognitive bias, and has consulted for Fortune 500 companies including Aflac, Wells Fargo, Honda, and Xerox. The New York Times called him the "Office Whisperer." His eighth book, "The Psychology of AI Adoption at Work: From Resistance to Results," comes out from Georgetown University Press in September 2026 with a foreword by Nick Bloom of Stanford, and draws on more than a hundred consulting projects and fifty executive interviews. He recently ran a leadership workshop for Citizens Property Insurance in Florida.

Key Topics

-The three emotions - Fear of job loss, threat to professional identity, and shame about being seen using AI. None of the three are addressed by a standard technology rollout.

-Training your own replacement - Employees resist because they believe adoption speeds their own obsolescence, a fear he ties to displacement among junior staff and recent graduates.

-The shame problem - People use AI privately and will not tell colleagues, which preserves individual gains and destroys the team handoffs insurance workflows depend on. Josh pushed back on this twice.

-AI alarmists and pragmatic resistors - Two of the eight psychographic profiles in his book, each needing a different fix from a different level of the organization.

-The skill of the future is editing - Professional pride has to move from drafting the claims letter to evaluating what the tool drafted, which is also what regulators require.

-Why license-plus-training fails - Buying seats and running online education worked for previous technology and does nothing for the emotional blockers under AI.

-Middle managers become supervisors of supervisors - Employees become managers of agents, middle managers manage those supervisors, and leadership is left doing strategy.

-Start in claims - Of every department he has worked with, claims gets a short-term return fastest, beginning with an initial coverage read an adjuster then decides on.

Notable Quotes

"It doesn't matter how accurate the model is, if the underwriter doesn't want to use it. And if the claims adjuster doesn't want to use it and if the auditor doesn't want to use it."

"The biggest resistance to change is not the hassle. The biggest resistance to change is the emotions."

"People are very reluctant to use AI because they feel that they are training their own replacement."

"The skill of the future is not the initial generation. It's the editing and evaluating of that product."

Resources

Guest:

Disaster Avoidance Experts: https://disasteravoidanceexperts.com/

"The Psychology of AI Adoption at Work": https://press.georgetown.edu/Book/The-Psychology-of-AI-Adoption-at-Work

Dr. Gleb Tsipursky on LinkedIn: https://www.linkedin.com/in/dr-gleb-tsipursky/

Host & Organization:

Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/

Horton International (USA): https://www.horton-usa.com/

Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show

Subscribe & Review

If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.