Podcast Summary: Service Business Mastery for Skilled Trades

Episode: How Service Businesses Lose Millions from Missed Calls and Recover Revenue with AI – Paul C. Cebulak
Hosts: Tersh Blissett & Josh Crouch
Guest: Paul C. Cebulak, CEO of Lace AI
Release Date: October 1, 2025

Episode Overview

In this action-packed episode, Tersh and Josh sit down with Paul Cebulak of Lace AI to dissect how home service businesses (HVAC, plumbing, electrical, etc.) are unknowingly losing millions due to missed and mishandled calls. The conversation zeroes in on the power of AI—in particular, AI call analysis and AI voice agents—to stop revenue leaks, improve lead conversion, and streamline operations at every customer touchpoint. With clear, real-world examples and memorable analogies, the trio explores the practical and cultural shifts required to embrace automation without sacrificing the human touch.

Key Discussion Points & Insights

1. The Cost of Missed and Mishandled Calls

• Big Problem, Hidden in Plain Sight: Most service business owners dramatically overestimate their booking rates, leading to massive missed revenue opportunities.

  • Data Example: One client’s CRM showed a “70–75% booking rate,” but manual audit revealed it was 29–42% instead (04:30–06:17).
  • Paul: “A 1% increase for Tommy [Mello] in booking rate, just booking 1% more jobs, was $3 million.” (05:20)

• Manual Call Review Is Unsustainable: Businesses consider hiring full-time staff to audit calls, but this is costly and covers just a fraction of call volume.

  • Paul: “They were going to hire three full-time people...$180,000 worth of salary and benefits, [to cover only] 15% of their calls.” (00:00, 14:35)

2. Measurement and Trustworthy Data as Foundation

• Why Data Integrity Matters: Differing definitions (gross bookings, qualified bookings, etc.) confuse everyone; solid decisions require accurate, trustworthy numbers across all calls.

  • Paul: “Measurement is really, really hard...how can you make bet-the-company decisions if you don’t actually know what’s working?” (06:39)
  • Paul: “We need to analyze 100% of your outbound calls, 100% of your inbound calls. And tell you what is converting and what isn’t.” (08:59)

• Automation Eliminates Garbage Data: Relying on CSRs to self-classify calls introduces human error—people get tired, distracted, or take shortcuts.

  • Paul: “People aren’t really great at classifying data at scale reliably because it’s not our core...we’re not robots.” (12:06)

3. Coaching & Empowering CSRs for Big Gains

• Actionable Intelligence Over Info Overload: More data isn’t always better. What matters is actionable insights surfaced to owners and managers.

  • Josh: “It’s almost paralysis analysis of all of this stuff, but I don’t know what any of it means or what to do with it.” (10:32)

• Focus and Personalization Required: Effective coaching hinges on flagging which behaviors need attention and why, not generic feedback.

  • Paul: “How do you guess what 19 of those [suggestions] might be, at best not helpful, and at worst, maybe breaking something that’s already working.” (09:24)
  • Paul: “The best teams…incentivize CSRs that…want to get better. Empower a CSR to see exactly what’s working and where they need to focus.” (37:17, 39:17)

4. Technological Evolution: AI Voice Agents & The ‘Super Team’ Model

• Build on Human + AI, Not Just AI: While some tech firms went straight to AI voice agents, Lace AI focused first on perfecting data and human coaching.

  • Tesla vs. Waymo Analogy:
    • Waymo Model: Fully self-driving (all tech, no human fallback)—not broadly ready or practical yet.
    • Tesla Model: Tech assists the human driver—today’s winning approach for most.
  • Paul: “We think the model that’s going to win…is this hybrid super team where it’s the best AI and best technology…helping and bolstering the best employees.” (28:36)

• When and Where to Deploy AI Agents:

  • After-Hours and Peak Demand: Filling gaps where staff are offline or overwhelmed.

  • Paul: “Our employees need to sleep…[but] we can replicate what great sounds like with an AI CSR that has…full functionality.” (41:01)

  • Flexibility: Must build a system flexible enough to serve both those who want fast, automated booking and those who demand human interaction—even within the same household.

5. Cultural Changes & Transparency in AI Adoption

• Setting Expectations and Disclosing AI: Disclosure helps manage customer expectations and avoids disappointment.

  • Paul referencing Kevin O’Leary: “His take was…he thinks you should [disclose]…you can set expectations such that people then don’t get disappointed.” (47:00)

• Market and Demographic Sensitivities: Some markets and customer bases may not be ready for, or want, voice AI; must honor those realities.

  • Paul: “There’s a journey that everybody’s on…there is opportunity in most call centers…but yeah, there’s some markets where businesses are like, look, we don’t feel comfortable with that.” (48:32)

Notable Quotes & Memorable Moments

• Paul: “If you’re booking at 1% higher, that sticks with me to this day. For Tommy…just 1% more jobs was $3 million.” (05:20)

• Josh: “Stop telling me you need more [leads] because you have plenty in your pipeline. Just gotta go get it.” (06:18)

• Paul: “No points for cramming in a technology just for using…a certain type of tech. What’s most important to the business?” (20:21)

• Paul (on vibes data): “Very few…successful businesses don’t have a really good handle on what excellent is and what needs attention…Vibes isn’t enough.” (31:24)

• Josh (on AI reception): “After hours calls, I believe people are becoming more and more okay with it being an AI agent.” (42:44)

Segment Timestamps

• [00:00–04:35] — Why missed calls cost you millions; the futility of manual call review
• [04:36–09:21] — Booking rate data confusion; the real cost of unreliable reporting
• [09:22–13:49] — How data drives targeted coaching and raises conversion rates
• [14:35–17:05] — Real-world example: $180K for 15% call auditing vs. AI reviewing 100%
• [19:47–24:35] — The rationale behind Lace AI’s strategy; Tesla vs. Waymo business analogy
• [28:36–29:53] — The role of AI in augmenting—not replacing—people (“hybrid super team”)
• [31:24–37:17] — Identifying A/B/C players, the pitfalls of “delegation by abdication”
• [39:35–42:44] — AI CSRs: Capabilities, future roadmap & use cases
• [44:34–48:32] — Generational and market comfort with AI; transparency and acceptance strategies

Takeaways for Service Business Owners

• You’re almost certainly losing more revenue to missed/mishandled calls than you think—start measuring and trusting your data.
• Manual review is too expensive and partial; AI can analyze every call and empower your team, not just replace jobs.
• Coaching and improvement rely on pinpointing the one or two behaviors that matter, not generic feedback.
• AI voice agents work best when filling specific gaps—after hours, overflow—not as a replacement for personable CSRs.
• Transparency with customers about AI is key; tailor tech deployment to fit your market and business culture.
• Use AI to maximize the people you have, not as a catchall substitute.

How to Connect with Paul & Learn More

• Direct email: paul@lace.ai
• Website: lace.ai
• Industry events: Watch for Lace AI at Nexstar, Pantheon, Service World Expo, and other events.

This episode is a must-listen for owners and managers in the home services sector who want to plug the holes in their revenue bucket, get the most out of their team, and stay ahead of the technology curve without losing sight of what makes their business personal and profitable.