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

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 Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value. Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com]  Subscribe to our newsletter at https://just-curious-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] Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value. Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com]  Subscribe to our newsletter at https://just-curious-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 Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value. Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com]  Subscribe to our newsletter at https://just-curious-ai.beehiiv.com [https://just-curious-ai.beehiiv.com]

Justin Massa & Jason Rubinstein - Remix Partners | How to Operationalize AI: 32 Kickstarts, One Consistent Pattern

Justin Massa and Jason Rubinstein are the co-founders of Remix Partners, an AI strategy consultancy that has now run 32+ kickstarts with small and medium-sized businesses across sectors and geographies. Their perspective is grounded in what actually works — not what looks good in a slide deck — and this conversation is full of practical frameworks for PE investors and operators trying to move AI from an experiment into the business. You'll Learn: * Why executive engagement is the single non-negotiable success factor in any AI rollout, and what it actually looks like in practice * The difference between augment and automate — and why 95% of consulting firms stop at automation when the real leverage is in augmentation * How to use a three-tier deployment framework (Experiment / Pilot / Implement) to prioritize where AI goes next, and why you need to revisit it every quarter * What "agent-legible" means — restructuring business information so agents can navigate it efficiently — and a simple mental model for where to start * A detailed manufacturing case study: how a 70-year-old custom parts company cut its RFQ turnaround time nearly in half using a Claude Code workbench built in 3.5 weeks Chapters:  00:00 – Intro 00:33 – Welcome & what Justin and Jason hope listeners take away 01:41 – Who are Justin & Jason / Remix Partners origin 03:19 – Jason's background and why they're doing this together 04:36 – Reflecting on the past year: what changed in the market 07:21 – What's driving the shift in appetite for AI 11:50 – Going wide vs. going deep on AI opportunities 12:55 – Data on AI adoption: where companies actually are 14:42 – Why small businesses are now advantaged over enterprise 15:39 – Operationalizing AI: the Remix kickstart approach 18:21 – Executive engagement as the #1 success factor 19:13 – Show and tell culture: banning secret cyborgs 21:33 – How the approach scales from SMBs to Fortune 25 23:56 – The leader's mandate: making time for AI 25:03 – What went wrong with other consultants 26:48 – The augment vs. automate framework 28:07 – Getting from automation to augmentation 30:33 – The jagged frontier and workforce implications 31:55 – How to navigate capabilities without a static view 33:35 – Deployment zones: Experiment / Pilot / Implement 35:29 – How zones shift quarterly / software stack decisions 37:50 – Manufacturing case study: the setup 40:26 – What they built (workbench in Claude Code, RFQ automation) 42:54 – Who was involved across the organization 44:27 – Impact: clock speed and capacity nearly halved 46:00 – Engineers' time reallocation; divorcing revenue from headcount 47:33 – Agent legibility: what it means and why it matters 49:38 – Making your business agent-legible: the practical how 52:54 – This year we edit documents, we don't create them 53:32 – Where to start for a 50-person company 56:16 – Plug into your core work application and play 58:07 – What happens to companies that don't experiment now 59:11 – Playing to Win in an AI era: differentiation AND low cost 01:02:33 – Closing * Watch on YouTube: https://www.youtube.com/watch?v=RwMibqytKFA [https://www.youtube.com/watch?v=RwMibqytKFA] * Connect with Justin Massa: https://www.linkedin.com/in/justinmassa/ [https://www.linkedin.com/in/justinmassa/] * Connect with Jason Rubinstein: https://www.linkedin.com/in/jason-rubinstein-94815/ [https://www.linkedin.com/in/jason-rubinstein-94815/]  * Explore more interviews: https://checkpluris.com/expert-interviews [https://checkpluris.com/expert-interviews] Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value. Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com]  Subscribe to our newsletter at https://just-curious-ai.beehiiv.com [https://just-curious-ai.beehiiv.com]

Kris Krisco - Synopsis | Why 97% of AI Projects Fail in the Middle Market (And How to Fix It in 1 Week)

Kris Krisco, Co-founder of Synopsis, is building AI-native data infrastructure designed specifically for the middle market. After scaling and selling a company under private equity, he saw firsthand why most AI initiatives fail — not because of ambition, but because of broken data foundations. In this episode, he explains how to move from fragmented systems and unreliable dashboards to real-time, operational AI that actually drives EBITDA. You’ll Learn Why 97% of middle-market AI projects fail — and the hidden infrastructure gap most companies ignore The critical role of master data management (MDM) and entity resolution in making AI outputs trustworthy How AI agents can replace months of traditional data engineering work with one-week iteration cycles Real examples of workflow automation that reduce operational waste and improve field execution in real time How private equity-backed companies are using AI in live board meetings to answer strategic questions instantly Chapters 00:00 Intro 01:00 Why Most AI Initiatives Fail 04:40 The Synopsys Approach to Data Infrastructure 10:40 The MDM Problem No One Talks About 17:15 How Much of Your Data Are You Actually Using? 20:45 Workflow Automation & Real-World Use Cases 25:15 Disaster Recovery Case Study 33:00 AI Inside Private Equity Board Meetings 36:30 The Future: Rule of 40 Becomes Rule of 70+ Links * Explore more expert interviews: https://checkpluris.com/expert-interviews * Watch on YouTube: https://youtu.be/a6H8SxJTsOc * Connect with Kris Krisco: https://www.linkedin.com/in/kris-krisco/ Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value. Explore more interviews and connect with experts at https://www.checkpluris.com [https://www.checkpluris.com]  Subscribe to our newsletter at https://just-curious-ai.beehiiv.com [https://just-curious-ai.beehiiv.com]