Proof of Work: AI Value Creation

Jimmy Bijlani is the founder and CEO of AI Momentum Partners, a senior-led firm that puts a dollar figure on the AI opportunity before a PE-backed company spends a cent, then embeds operators and engineers to capture it. He spent close to a decade at Google running professional services for Gemini and built the tech strategy practice at BCG. In this conversation he lays out why most AI spend never reaches the P&L, and what the companies getting real margin out of AI are doing differently.

You'll Learn:

- Why "AI adoption" is a feeling, not a number, and the Tuesday-to-Wednesday test that exposes it

- How the bar in private equity shifted from the rule of 40 to the rule of 60, and what that demands of an AI program

- Why handing AI to IT or a Chief AI Officer usually stalls, and who actually needs to own it

- The case for starting with the P&L and letting the tooling decision fall out of the strategy

- How a 90% license-adoption number hid a structural zero in EBITDA impact

- How one automation on software the client already owned produced about $700K in NPV

- What "sticking around to deliver" looks like: forward-deployed engineers and an embedded senior operator

- The three questions a CFO should ask on Monday to tell a real AI program from activity

Chapters:

00:00 - Activity vs. a real AI program

00:27 - Who Jimmy is and who AMP serves

01:21 - The one takeaway: tie AI to the P&L

03:08 - What AI Momentum Partners actually does

05:24 - Where the AI-to-profit gap first showed up (inside Google)

07:52 - Who should own AI? The Chief AI Officer problem

11:26 - Where to start: the P&L before the platform

14:31 - Case study: a PE-backed vertical SaaS company

17:29 - Why the CEO brought them in

18:14 - What the diligence found: 90% licensed, 60% legacy

22:00 - Telling people the hard truth without losing them

25:34 - Getting managers onside when headcount is at stake

28:42 - Why they built on tools the client already owned

32:00 - The first automation: pro-serve docs and $700K NPV

34:08 - The after-state and the real return

36:49 - What "sticking around to deliver" looks like

40:16 - Why PE targets don't connect to AI work

42:28 - What boards will demand in a few years

45:28 - Close

Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE

Read the episode write-up: https://www.checkpluris.com/insights/why-most-ai-spending-never-reaches-the-pl-jimmy-bijlani-ai-momentum-partners

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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

🔗 Read the episode write-up: https://www.checkpluris.com/insights/ai-wont-break-private-equity-itll-sort-it-doyl-burkett-integrity-growth-partners

📩 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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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

📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI

🔗 Read the episode write-up: https://www.checkpluris.com/insights/why-most-data-driven-companies-are-fooling-themselves-alex-lirtsman-corraldata

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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

🔗 Read the episode write-up: https://www.checkpluris.com/insights/the-rebuild-moment-why-most-saas-ceos-are-sequencing-ai-wrong-tom-scott-wrike

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI 🔗 Read the episode write-up: https://www.checkpluris.com/insights/why-you-cant-vibecode-portfolio-monitoring----yann-magnan-joe-zein-on-ai-in-private-equity

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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
🔗 Read the episode write-up: https://www.checkpluris.com/insights/why-sprinkling-ai-is-both-a-green-flag-and-a-red-flag-jonathan-hansing-wallabi

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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
• Connect with Justin Massa: https://www.linkedin.com/in/justinmassa/
• Connect with Jason Rubinstein: https://www.linkedin.com/in/jason-rubinstein-94815/ 
• Read the episode write-up: https://www.checkpluris.com/insights/how-to-operationalize-ai-32-kickstarts-one-consistent-pattern-massa-rubinstein-e5204

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

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

• Read the episode write-up: https://www.checkpluris.com/insights/why-97-of-ai-projects-fail-in-the-middle-market-and-how-to-fix-it-in-1-week
• Watch on YouTube: https://youtu.be/a6H8SxJTsOc
• Connect with Kris Krisco: https://www.linkedin.com/in/kris-krisco/

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

Mollie Amkraut Mueller, Founder of Watch Me AI and former strategist at IDEO and EY, helps non-technical leaders turn AI into a chief of staff, analyst, and revenue engine. In this episode, she breaks down a practical, business-first approach to AI adoption—showing how one recorded meeting can become 10 high-value assets. If you lead a team or operate in the middle market, this is a tactical blueprint for saving time, increasing output, and unlocking ROI without hiring a dev shop.

You’ll Learn

• How to turn a single meeting transcript into LinkedIn posts, proposals, client deliverables, and course content in minutes
• Why most AI projects fail—and how to scope high-ROI use cases that actually stick
• A simple diagnostic framework to uncover 1–10 immediate AI opportunities inside any workflow
• How off-the-shelf tools like Claude and Relay can replace expensive, over-engineered automation projects
• A real-world PR agency case study that cut multi-hour manual research down to 10 minutes per client request

Chapters

00:00 Intro
01:58 Mollie’s background and founding WatchMe AI
05:09 The 90-minute AI diagnostic that reveals hidden ROI
12:17 One transcript → 10 revenue-generating assets
18:55 Why jumping into automations can backfire
20:32 PR agency case study: automating conference research
25:16 Busting myths about AI adoption
29:09 Who should start using AI now

Links

• Watch on YouTube: https://www.youtube.com/watch?v=XEP3Vs5ml4g
• Connect with Mollie Amkraut Mueller: https://www.linkedin.com/in/mollieamkraut/
• Explore more on Just Curious https://www.checkpluris.com/insights

Read the episode write-up on Pluris: https://www.checkpluris.com/insights

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

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

Ryan Kurt, CEO & Founder of The AI Lab, advises middle-market CEOs on how to turn AI from hype into measurable operational advantage. A 13-year veteran of generative AI, he’s helped leaders move beyond “AI strategy decks” to real workflow transformation — including a healthcare network that used computer vision to improve care quality and performance-based pay with 99% accuracy. His perspective is clear: the technology isn’t the hard part — leadership ownership is.

You’ll Learn

• Why most AI initiatives fail because CEOs delegate ownership instead of leading transformation themselves
• How to rebuild workflows from first principles instead of layering AI onto broken processes
• What “total ownership” actually means — and why third-party vendors can’t carry your AI strategy
• How agentic workflows unlock capacity by automating repetitive work before it hits your desk
• A behind-the-scenes case study of using computer vision and human-in-the-loop systems to improve care quality, culture, and compensation

Chapters

00:00 Intro
01:00 Ryan’s path from early AI startup to founding The AI Lab
04:15 Why “AI strategy” is the wrong starting point
08:10 The leadership readiness test most companies fail
12:00 What total ownership actually looks like
18:00 Why agentic workflows change the economics of work
23:00 Case study: Using AI to improve caregiver performance
27:30 Human-in-the-loop systems and hybrid AI architecture
30:00 Cultural and operational impact
31:45 How to sequence AI adoption the right way
34:25 Who The AI Lab works with

Links

• Watch on YouTube: https://youtu.be/bdSq4S2_PZU
• Connect with Ryan Kurt: https://www.linkedin.com/in/ryankurt/
• Explore more interviews: https://www.checkpluris.com/insights/how-ai-caught-what-bonuses-training-and-pay-raises-couldnt-fix"

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

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