Doyl Burkett — Integrity Growth Partners | AI Won't Break Private Equity, It'll Sort It

Doyl Burkett — Integrity Growth Partners | AI Won't Break Private Equity, It'll Sort It

Doyl Burkett is the founder and Managing Partner of Integrity Growth Partners, an $800M+ AI-enabled growth private equity firm in Los Angeles. Most GPs think about what AI does to the companies they own. Doyl is equally focused on what it does to the firm itself, and he's been building Integrity as an AI-native investor since 2018. In this conversation he argues that AI won't compress private equity evenly, it will sort it, and he shows what that sorting looks like for companies and for the GPs who back them. This is an episode of Proof of Work: AI Value Creation (formerly Just Curious), the podcast from Pluris on how AI is actually being adopted in the mid-market. You'll Learn: * Why the "AI kills software" narrative is too simple, and the three-bucket framework Doyl uses instead (AI-native, defensible-moat, undifferentiated) * The six kinds of moat he underwrites for in the AI age, from proprietary data to regulatory to human-in-the-loop * How an AI-native firm actually runs day to day: CAPE across sourcing, reporting, and operations * Why GPs themselves are about to be sorted, and what separates the firms that can still raise from the ones that can't * How Integrity used its own AI sourcing engine to find the AI acquisition that nearly doubled a portfolio company's revenue line * The AI-adjacent decision he'd most want to redo, and why market reaction can matter more than the technology itself * Why there's no AI quick fix, and how to tell a real capability from a wrapper Chapters: 00:00 - Intro: what makes Doyl's seat unusual 01:04 - Who is Doyl, what is Integrity, and why "AI-enabled" from day one 03:06 - The one thing to walk away with: why "SaaS is dead" is too simple 04:24 - Bear case 1: does tech risk become existential, not manageable? 07:02 - What defensibility actually looks like: the six moats 12:03 - Bear case 2: did PE buy the disrupted, not the disruptors? 14:16 - Leverage as an amplifier: how AI changed the capital-structure posture 18:38 - The three-bucket framework for how AI sorts the asset class 23:43 - The Adobe parallel, and how to tell AI-native from AI-marketing 27:14 - What an AI-native firm looks like: building CAPE Sourcing 32:55 - The payoff: doing more with fewer, getting there first, winning on trust 35:53 - CAPE Operations: codifying the playbook and the CEO network 38:59 - The portfolio conversation: AI as opportunity and threat, every time 41:20 - Prop-tech case study: using AI sourcing to find an AI acquisition 44:46 - Fund-level AI: CAPE Reporting, automating DDQs, the future of investing 50:28 - "I want to be the wheat, not the chaff. Where do I start?" 52:26 - What not to do: the AI quick-fix trap (Ferrari vs. Ford Focus) 57:05 - Closing thoughts 🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI 🔗 More Expert Interviews: https://www.checkpluris.com/expert-interviews 📩 Subscribe to the newsletter: https://proof-of-work-ai.beehiiv.com/ Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses. Learn more at checkpluris.com Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com] Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/ [https://just-curious-ai.beehiiv.com]

Alex Lirtsman — CorralData | Why Most Companies Are Only "Data-Adjacent"

Alex Lirtsman spent ~15 years solving data problems by hand for brands like the NBA, Pfizer, and Sweetgreen — then sold his agency and built CorralData to automate the entire thing. In this episode, he makes the case that most "data-driven" companies are really just "data-adjacent," explains why the metrics leaders celebrate are often vanity metrics, and walks through what it takes to move from read-only dashboards to AI that actually takes action. You'll Learn: * Why most companies are "data-adjacent" instead of data-driven — and how to tell which one you are * Why appointments, leads, and ROAS are vanity metrics, and what to optimize instead (LTV:CAC by channel and location) * How bridging EMR, CRM, call tracking, and ad data unlocks profit you currently can't see * What "we need AI" really means — and why effectiveness matters more than efficiency * How to move from read-only dashboards to AI that acts, safely (guardrails + human-in-the-loop) * What a PE-backed med-spa platform changed after unifying its data * Why brand and data are the only two assets you can't rebuild Chapters: 00:00 - Intro: who is Alex Lirtsman and what is CorralData 01:07 - The one thing he wants you to do: just get started 01:47 - From digital agency to automating data ("Shopify for data") 02:47 - Why he turned 15 years of consulting into a platform 04:06 - Why every report and dashboard has a dead end 05:33 - What changed - technologically and culturally - to make this possible 07:41 - How CorralData actually works: connect, warehouse, semantic layer 10:32 - Beyond dashboards: talking to your data and acting on it 12:26 - The hidden value in EMR/ERP data - and bridging systems 14:55 - Data-adjacent vs. data-driven 17:23 - What teaching at Columbia/NYU taught him about usable data 18:54 - From read-only AI to autonomous execution 19:55 - Guardrails: just because you can doesn't mean you should 22:10 - Where leaders go wrong when they say "we need AI" 25:27 - Case study: a PE-backed med-spa optimizing appointments vs. revenue 29:15 - How long it takes to unify EMR + CRM + paid media 32:47 - What changed first once the data was connected 34:40 - ROI: customers growing ~7x faster 🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE [https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE] 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI [https://www.youtube.com/@JustCuriousAI] 🔗 More Expert Interviews: https://checkpluris.com/expert-interviews [https://checkpluris.com/expert-interviews] Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses. Learn more at checkpluris.com Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com] Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/ [https://just-curious-ai.beehiiv.com]

Tom Scott — Wrike | Why Most SaaS CEOs Are Sequencing AI Wrong

Tom Scott is the CEO of Wrike, a $250M+ ARR intelligent work management platform owned by Symphony Technology Group. Three years into PE ownership and squarely inside what he calls the AI "rebuild moment," Tom is running the experiment in real time — and this conversation is the operating playbook, not the LinkedIn version of it. He walks Stu through what's defensible in enterprise software now, where most CEOs are getting the AI sequence wrong, and the actual mechanics of rebuilding a SaaS business while the numbers still look fine. You'll Learn: * Why the market is pricing every SaaS company as if they're disrupted equally — and why the real story of the next two years is execution variance, not disruption itself * What's actually defensible when coding agents can clone a UI in a weekend (and why "system of record" companies get demoted while "system of agency" companies don't) * The 5-step rebuild playbook Tom is running inside Wrike — including why subtraction matters more than addition, and why personal disruption is non-negotiable for the CEO * How AI transformation actually rolls out in engineering and marketing (with the J-curve, the early-adopter dip, and the specific numbers Wrike has seen: 25% engineering velocity lift, 3x marketing article output, 87% faster account research) * What PE investors should be asking in diligence now that almost none are — and the three failure modes that quietly deconstruct companies over 2–3 years while their numbers still look fine * Tom's 12-month playbook for a $100M–$500M CEO starting today: one thing to do, two things not to do, and how to know in 90 days whether you're on the right side of the J-curve Chapters: 00:00 – Intro 01:00 – Who is Tom Scott / what is Wrike 02:11 – Should SaaS CEOs be bearish on SaaS in an AI world? 05:11 – Commoditization: what AI flattens vs. what survives 07:01 – What's actually defensible: "Know your customer" 08:51 – System of record vs. system of agency 11:02 – Accruing memory: where the value of agent training accrues 12:39 – What Wrike is doing internally: agency, MCP, governance investments 15:27 – Will Microsoft / OpenAI / Anthropic just own this layer? 17:50 – The future of seat-based pricing (and Apex) 20:25 – What "rebuild the company" actually means 23:43 – What happens to companies that don't rebuild 25:08 – Why hands-on personal disruption is non-negotiable 28:56 – The first thing Tom changed: his own approach 31:29 – The 5-step rebuild playbook 35:37 – AI's impact on engineering velocity (25% Q4 lift) 36:36 – Where most CEOs get the sequence wrong 38:30 – Inside Wrike's marketing transformation: real numbers 41:26 – Advice for $100M–$250M+ company CEOs starting today 42:51 – The leader asking you to slow down: how Tom responds 44:46 – Failure of imagination and the real source of resistance 47:30 – The PE playbook under pressure: new diligence questions 51:25 – ROI timeline and the J-curve in practice 54:39 – Offense vs. defense in an AI-disrupted category 56:02 – What separates the winners from the deconstructed 57:07 – Advice for CEOs over the next 12 months 🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI 🔗 More Expert Interviews: https://checkpluris.com/expert-interviews Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses. Learn more at checkpluris.com Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com] Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/ [https://just-curious-ai.beehiiv.com]

Yann Magnan & Joe Zein — 73 Strings & Soal Labs | AI & Portfolio Monitoring in Private Equity

Most of the AI conversation in private equity has been about sourcing, diligence, and value creation. But a quieter transformation is underway inside portfolio teams — closing the gap between when something goes wrong at a portfolio company and when the GP actually finds out. Stu is joined by Yann Magnan, CEO and Co-Founder of 73 Strings (AI-powered valuation and monitoring platform, backed by Goldman Sachs, Blackstone, Fidelity International, Golub, and Hamilton Lane), and Joe Zein, Co-Founder of Soal Labs (custom AI infrastructure for PE and private credit). One builds the platform. One builds the custom architecture that sits around it. Together they lay out why portfolio monitoring is uniquely hard to automate, and what operators should actually do next. You'll Learn: * Why the real bottleneck in portfolio monitoring is infrastructure, not cadence — and why LP demand for weekly/daily reporting is about to force the issue * What a governed data model actually requires (auditability, versioning, rules, entity resolution) and how it differs from a pile of Excel files * Why private capital needs 100% accuracy, not 99.5%, and what that means for how LLMs get deployed * When lighter, cheaper models beat frontier LLMs — and the case for combining ML, NLP and LLMs inside a single ingestion pipeline * Why "vibecoded" portfolio monitoring tools fail the moment they touch real LP reporting — and how to think about buy vs. build * The entity-resolution problem (the same company named three different ways across 73 Strings, Salesforce, and your file system) and how to solve it * What changes in 18 months: daily mark-to-market, AI-surfaced alerts in your inbox, and the prerequisite data foundation that makes it possible Chapters:  00:00 – Intro & the 60-day information gap  02:33 – Why monthly monitoring is brutal: data, process, bandwidth  06:10 – The real bottleneck at the GP: how reports actually get assembled  09:20 – Best and worst case timelines for a quarterly close  10:44 – Is the problem cadence, or infrastructure?  13:40 – Signals: what shows up before EBITDA moves  18:57 – New data sources, covenants, and the credit boom  21:31 – Why "vibecoded" monitoring tools fail  25:20 – The 100% accuracy problem in private capital  26:55 – What a robust data model actually looks like  29:45 – Moving from file systems to governed structured data  35:49 – Entity resolution across 73 Strings, Salesforce, and the file system  37:00 – Light vs. frontier LLMs: which do you actually need?  40:17 – Confidentiality, enterprise plans, and the open-source option  43:59 – What most people miss about AI and portfolio monitoring  46:29 – Portfolio monitoring in 18 months  49:35 – One thing to start doing right now 🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE [https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE] 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI [https://www.youtube.com/@JustCuriousAI] 🔗 More Expert More interviews: https://checkpluris.com/expert-interviews [https://checkpluris.com/expert-interviews] Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses. Learn more at checkpluris.com Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com] Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/ [https://just-curious-ai.beehiiv.com]

Jonathan Hansing — Wallabi | How to Go AI-Native: The J-Curve, 10,000 Prompts, and What "Sprinkling AI" Really Signals

Jonathan Hansing is the co-founder of Wallabi, a data, technology, and AI strategy firm helping growth-oriented mid-market companies go AI-native. His team works with CEOs, COOs, and CIOs who feel the pressure to do something with AI — but need a clear path from experimentation to execution. This conversation is full of sharp frameworks and hard-won patterns from the field. You'll Learn: * Why "sprinkling AI" is simultaneously a signal of opportunity and a warning sign — and how to tell the difference * How to assess whether a leadership team actually has the appetite for real AI transformation (not just the desire for it) * What the J-curve of technology adoption means for AI specifically — and why Jonathan believes companies have about two years to get on the right side of it * The difference between a company that uses AI and one that is genuinely AI-native — and the single most important shift in mindset that separates them * A detailed case study: how a media and events company's request for an email automation workflow revealed — layer by layer — a need for full data architecture overhaul, delivered in 8 weeks Chapters: 00:00 – Intro 00:37 – What Jonathan hopes listeners take away: 10,000 prompts is the new 10,000 hours 02:16 – Who is Jonathan Hansing / what is Wallabi 04:00 – The Wallabi platform: forward-deployed coaches backed by AI 06:30 – A machine learning case study: 18-month problem solved in a week 07:03 – The journey: West Point → Army → Narrative Science → Salesforce → Wallabi 10:08 – "Sprinkling AI": green flag and red flag 12:35 – How Wallabi assesses appetite: the 2-week sprint approach 17:03 – Data philosophy: use case first, architecture second 18:20 – What's happening beneath the surface for pressured CEOs 20:48 – The J-curve: from 20 years to 2 years 25:03 – Dual Transformation: managing two change curves at once 25:49 – AI-native vs. companies that use AI 28:00 – Case study: media & events company — the layered problem 33:09 – What they built on top: Snowflake Intelligence, Tableau, reverse ETL 34:15 – Claude Code and context management: shared learning across repos 36:04 – The ROI of AI is the ROI of WiFi 38:09 – The bifurcation: who becomes unstoppable vs. who plateaus 41:03 – One thing to remember: 10,000 prompts is the new 10,000 hours 🔗 Connect with Jonathan Hansing: https://www.linkedin.com/in/jonathanhansing 🔗 Explore Wallabi: https://www.wallabi.ai 🔗 Explore more interviews: https://checkpluris.com/expert-interviews Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses. Learn more at checkpluris.com Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com] Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/ [https://just-curious-ai.beehiiv.com]