In this episode of The CTO Show with Mehmet, Mehmet sits down with Laura Fu, GTM Architect at DevRev. Laura brings a RevOps and sales enablement lens to a question many GTM leaders are now facing: AI does not fix sales by sitting on top of old workflows.

The conversation reframes AI in go-to-market as an operating model problem, not a tooling problem. Laura argues that AI-native execution requires new feedback loops, better data capture, agent-readable systems, and a different view of enablement. The strongest claim is that dashboards and forecast calls become less central when agents can surface the signal directly.

If you are leading, building, or investing in enterprise sales organizations, this conversation gives you a sharper way to think about AI-native GTM, CRM architecture, RevOps, sales enablement, and pipeline execution.

About the Guest

Laura Fu is the GTM Architect at DevRev, focused on improving go-to-market efficiency and how revenue organizations operate with AI.

She is the author of Designing for Excellence: Sales Enablement in the AI Native World, a book about using AI to make sales enablement and GTM engines more fluid and operational.

Laura is the right person to frame this signal because she connects sales enablement, RevOps, CRM systems, data quality, and AI agents into one operating model.

LinkedIn: https://www.linkedin.com/in/laurazfu/

Key Takeaways

AI does not make broken sales processes better, it exposes where the process was weak.

Sales teams still move at human speed, but expectations now move at AI speed.

AI-native GTM requires workflow redesign, not summaries copied into old systems.

Traditional enablement fails when training is disconnected from the moment of need.

CRM becomes more valuable as memory and context, not as a manual reporting database.

Dashboards lose power when agents can detect revenue signals directly.

Poor data quality breaks trust in AI faster than poor user adoption.

RevOps teams will shift from analysts to GTM engineers who build and orchestrate systems.

What You Will Learn

The difference between AI adoption and AI-native sales execution.

How AI changes sales enablement from a training function into an operating system.

Why dashboards become less useful when agents can scan signals directly.

The CRM requirements that matter when agents need read and write access.

How real-time feedback loops can reshape sales messaging, pricing, and positioning.

Why data quality and change management decide whether AI tools get trusted.

What an AI-first revenue organization could look like from day one.

Episode Highlights

00:00 — Laura Fu frames AI-native sales enablement

02:30 — Sales teams face AI-speed expectations

06:00 — AI adoption does not change execution

09:30 — Traditional enablement was already broken

12:00 — Enablement becomes a system, not function

15:30 — The AI enablement flywheel takes shape

20:30 — Change management breaks AI adoption first

25:00 — Feedback loops separate messaging from delivery

28:00 — Pipeline creation remains the strongest signal

30:00 — Dashboards are dead in agent-led RevOps

36:30 — AI finds pipeline signals faster

39:00 — GTM engineers replace analyst-heavy RevOps

42:30 — Laura shares the book and podcast

Resources Mentioned

Designing for Excellence: Sales Enablement in the AI Native World by Laura Fu: Available on Amazon, Barnes & Noble, and bookstores: https://www.amazon.com/dp/B0FVBKGK4Z

State of the AI Union: Laura Fu’s podcast on Apple Podcasts: https://podcasts.apple.com/gb/podcast/state-of-the-ai-union/id1851548376

Listen Now

Available on all major podcast platforms and YouTube.

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