In this episode of Tales from the Pros, Eric Lawrence sits down with Robert Levitan, co-chair of the Ethical Tech Project and a serial entrepreneur who has founded six companies, including iVillage, to discuss why AI's biggest challenge may not be the technology itself, but what happens when something goes wrong.

Drawing on his experience building companies during the early internet era, Robert explains why he believes AI is heading toward a major moment of public accountability. He shares a powerful historical parallel from the 1911 Triangle Shirtwaist Factory fire and what it can teach businesses about the consequences of ignoring foreseeable risks.

They also discuss the business case for responsible AI, why customer trust matters, and how companies can establish practical AI governance without slowing innovation.

If your organization is adopting AI, building AI-powered products, or deploying autonomous agents, this conversation offers a valuable perspective on managing risks before they become serious business problems.

🎯 Highlights You Won't Want to Miss

• Why Robert believes AI is approaching a major accountability moment
• What the 1911 Triangle Shirtwaist Factory fire can teach today's tech companies
• How AI failures could affect thousands of customers at once
• Why responsible AI can become a competitive advantage
• What the early internet and social media reveal about unintended consequences
• Why trustworthy data matters for AI reliability and customer confidence
• How businesses can establish AI governance beyond written policies
• What companies should do now to prepare for future AI risks

🎧 Listen and Subscribe

Spotify:
https://open.spotify.com/show/6QkUtrcNllUkqtq1fjlwnZ

Apple Podcasts:
https://podcasts.apple.com/us/podcast/tales-from-the-pros/id1371067192

YouTube:
https://www.youtube.com/@Imaginovation/podcasts

SoundCloud:
https://soundcloud.com/talesfromthepros

💡 Key Takeaways

• New technologies create unintended consequences, and AI is no exception
• Companies should anticipate potential AI failures rather than react after harm occurs
• Responsible AI practices can strengthen customer trust and business relationships
• AI governance should reflect a company's values, industry, and acceptable risk boundaries
• Reliable data and clear data provenance are essential for trustworthy AI outcomes
• Effective governance requires ongoing monitoring, not just documented policies
• Businesses need systems to detect and correct AI issues before they cause serious harm
• Companies that establish clear AI principles and guardrails now may be better positioned to manage future risks

🗂 Topics We Cover

• The coming AI accountability crisis
• Lessons from the early internet and social media
• The Triangle Shirtwaist Factory fire and AI regulation
• AI risks and unintended consequences
• Responsible AI as a competitive advantage
• Customer trust and AI adoption
• AI governance frameworks and ethical principles
• Data quality, privacy, and provenance
• AI monitoring and risk management
• Preparing businesses for AI failures

⏱️ Chapters

00:00 — Introduction
00:43 — Meet Robert Levitan and the coming AI reckoning
02:12 — Lessons from 30 years of building technology companies
04:32 — The Triangle Shirtwaist Factory fire and its warning for AI
06:51 — What a major AI failure could look like
08:22 — Why responsible AI is a competitive advantage
10:18 — Real-world examples of AI risks and safeguards
14:45 — What the early internet teaches us about regulation
20:20 — The tobacco industry parallel and tech accountability
25:58 — AI security risks and the need for safeguards
29:13 — Making the business case for responsible AI
31:55 — Why trusted data and AI governance matter
33:46 — Building an AI governance framework
35:30 — The two pillars of responsible AI
40:17 — What separates companies that survive major AI failures
44:05 — Preparing for AI's unintended consequences
46:24 — Final advice and how to connect with Robert Levitan

In this episode of Tales from the Pros, Eric Lawrence sits down with Matt Soltau, Global Director of Strategy and Operations at IntelliPaaS, to discuss why so many AI projects struggle to move beyond the pilot stage.

Matt explains why even promising AI projects can fail when they're introduced into real business environments. The problem often isn't the AI model itself. It's the disconnected systems, unreliable data, and outdated infrastructure behind it.

They also discuss what data silos look like in everyday business operations, why companies accumulate integration debt, and how organizations can prepare their existing systems for AI without replacing everything.

If you're planning to implement AI or struggling to scale an existing project, this conversation offers practical advice on building a reliable data foundation, managing risks, and getting your organization AI-ready.

🎯 Highlights You Won’t Want to Miss

• Why successful AI pilots often fail when deployed in real business environments
• How poor data quality and disconnected systems affect AI performance
• What messy data infrastructure looks like in everyday business operations
• How manual spreadsheets and conflicting reports reveal deeper integration problems
• Why data integration matters when scaling enterprise AI projects
• How organizational culture and employee readiness affect AI adoption
• Why data governance, privacy, and security need attention before scaling AI
• Matt's practical 90-day approach to preparing a business for AI

🎧 Listen and Subscribe

Spotify:
https://open.spotify.com/show/6QkUtrcNllUkqtq1fjlwnZ

Apple Podcasts:
https://podcasts.apple.com/us/podcast/tales-from-the-pros/id1371067192

YouTube:
https://www.youtube.com/@Imaginovation/podcasts

SoundCloud:
https://soundcloud.com/talesfromthepros

💡 Key Takeaways

• AI projects can perform well in controlled tests but struggle when exposed to real-world business data
• Clean, accessible, and trustworthy data is essential for reliable AI performance
• Disconnected systems create integration debt that becomes harder to manage as businesses grow
• Companies don't necessarily need to replace their existing software to become AI-ready
• A connected data infrastructure can make it easier to change vendors and introduce new technologies
• AI readiness depends on both technical infrastructure and an organization's willingness to adopt change
• Proper governance and access controls help businesses use AI while protecting sensitive information
• A practical starting point for AI readiness is to map your data, connect your systems, and establish governance before scaling

🗂 Topics We Cover

• Why AI projects fail to scale
• AI pilots vs. real-world deployment
• Data quality and trustworthy AI
• Data silos and integration debt
• Enterprise data integration
• Legacy systems and scalable infrastructure
• Vendor lock-in and system connectivity
• Organizational AI readiness
• AI governance, privacy, and security
• Building a 90-day AI readiness roadmap

⏱️ Chapters

00:00 — Why AI needs trustworthy data
02:30 — The problem with disconnected systems
03:07 — Why AI pilots fail in production
06:24 — AI implementation and access to business data
08:31 — What a messy data stack looks like
11:18 — Warning signs of data silos and integration debt
15:12 — Building scalable data infrastructure
17:13 — Legacy systems, vendor relationships, and data integration
22:37 — What separates innovative organizations from the rest
25:20 — Why organizational readiness matters for AI
30:07 — AI regulation, data privacy, and security
34:59 — Managing shadow AI and protecting sensitive data
36:29 — A 90-day AI readiness plan: Map, Connect, Govern

In this episode of Tales from the Pros, Michael Georgiou and Eric Lawrence sit down with Tim Condon, CRO at Clutch, to talk about how B2B buying is changing.

Tim shares what he has learned from growing products, working closely with product and revenue teams, and listening to what customers actually want. He also talks about why good products still need strong distribution and why data matters when teams disagree.

The conversation then shifts to AI. Tim explains how AI search and AI agents could change the way companies find and evaluate service providers. He also shares how Clutch is preparing for a future where buyers may start their search with AI instead of Google or a company website.

If you work in software, B2B services, sales, marketing, or product, this episode gives you a practical look at where buyer behavior may be heading and what businesses can do to stay visible and trusted.

🎯 Highlights You Won’t Want to Miss

• Why product and revenue teams need to stay closely aligned
• How data can help settle competing opinions and priorities
• Why even a great product still needs the right distribution
• How Clutch turned underused traffic into a meaningful revenue stream
• Why AI could reshape the entire B2B buying journey
• How AI agents may eventually help buyers scope projects and evaluate providers
• Why reviews and third-party trust signals matter for AI visibility
• What AI means for the future of software development and B2B services

🎧 Listen and Subscribe

Spotify:
https://open.spotify.com/show/6QkUtrcNllUkqtq1fjlwnZ

Apple Podcasts:
https://podcasts.apple.com/us/podcast/tales-from-the-pros/id1371067192

YouTube:
https://www.youtube.com/@Imaginovation/podcasts

SoundCloud:
https://soundcloud.com/talesfromthepros

💡 Key Takeaways

• Strong product and revenue alignment starts with trust, communication, and shared goals
• Customer feedback is more useful when teams understand which requests truly matter
• Data works best when teams agree on the right metrics before seeing the results
• Great products still need strong distribution to reach the right customers
• AI is making B2B buyers more informed before they ever speak with a provider
• Reviews, verification, and reputation are becoming important signals for AI recommendations
• B2B service companies are unlikely to disappear, but how they deliver and price their expertise may change
• Businesses need to actively build their reputation across the web, not just on their own websites

Tim also notes that Clutch sees a strong relationship between reviews, verification, and how frequently companies are surfaced in AI-driven results.

🗂 Topics We Cover

• Product, sales, and revenue alignment
• Customer feedback and data-driven decision-making
• Product growth and distribution
• B2B marketplaces and buyer behavior
• AI search and AI agents
• Reviews, reputation, and trust signals
• The future of B2B services
• AI and software development
• Outcome-based pricing
• Google, AI search, and the changing discovery landscape

⏱️ Chapters

00:00 — Why product and revenue teams drift apart
01:33 — Tim’s journey from The Washington Post to Homesnap and Clutch
03:42 — Building alignment between product and revenue
08:58 — Using data to make better product decisions
14:04 — Why great products still need distribution
17:49 — Joining Clutch and finding new growth opportunities
24:02 — Building a marketplace around buyer trust
26:01 — How revenue and product teams can work better together
30:03 — Clutch’s AI project brief and the future of B2B buying
35:36 — AI search, trust signals, and changing buyer behavior
40:47 — What AI means for software development companies
44:23 — How businesses can become more visible to AI
47:15 — Google, AI competition, and the future of search