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Matt Magne has seen this movie before. Twenty years ago, as a sales engineer in the master data management (MDM) space, he watched companies spend millions trying to create a “single view of the customer.” Today, as Senior Enablement Manager at LaunchDarkly, he’s watching the same pattern unfold with sales enablement tools.
“People spent millions of dollars on a solution to do that, and they still haven’t fixed that problem,” Matt reflects about his MDM days. Now he sees sales reps juggling 20 to 30 different applications, each containing a fragment of truth about their performance and capabilities. The irony isn’t lost on him that adding AI role-play tools to this mix, no matter how sophisticated, just creates another silo.
About Matt Magne
Matt describes himself as the “AI-powered Silicon Valley sales enablement guy” and brings an eclectic background to the role. From coder to sales engineer to product marketer, his career spans technical and creative pursuits, including a band featured on MTV’s Road Rules and a guitar-playing TED Talk. Eight months into his role at LaunchDarkly, he’s experimenting with voice-enabled AI for sales practice while maintaining healthy skepticism about technology’s limitations.
Why voice AI works (and why it doesn’t matter without integration)
LaunchDarkly sells feature flags, which Matt explains through his Christmas lights analogy. Install lights once, then flip switches throughout the year to display different colors without climbing back on the roof. Feature flags let developers deploy code once and control features without new releases. This technical complexity creates specific sales challenges when prospects dismiss it as “just another config tool.”
Matt discovered a voice-enabled AI that lets reps practice these difficult conversations. “You can verbally give an objection to it and it will verbally respond,” he explains. The AI simulates skeptical enterprise buyers who challenge pricing and question architecture decisions. Reps practice the same conversation multiple times, experimenting with different approaches without fear of judgment.
“I play guitar. One of my challenges is that I think about playing with the metronome and all that by myself, and I’m terrible at it. The same thing goes for role plays, where it’s like, I’ll prepare with someone I’m comfortable with, but it’s way different than being on stage.”
Matt Magne, Senior Enablement Manager, Revenue Enablement at LaunchDarkly
The technology works remarkably well for creating safe practice spaces. But here’s where Matt’s MDM experience provides a crucial perspective. Just as companies had customer data spread across dozens of systems twenty years ago, today’s sales organizations have performance insights scattered across their tech stack. The AI might capture that a rep struggles with security objections, but if that insight doesn’t reach the right coach at the right time, deals still get lost.
The elephant problem: Everyone’s touching different parts
Matt uses a vivid metaphor to describe the current state of sales enablement. “We’re all touching the elephant at different spots, and one thinks it’s a tree,” he explains. The manager looks at pipeline metrics, the enablement team checks training completion, and the AI tool logs practice patterns. Each stakeholder has valid data but sees an entirely different picture.
“All these SaaS solutions that we have, I mean, some AEs have like 20, 30 solutions they’re using, and there’s still a challenge of data silos, pulling the data together.”
Matt Magne, Senior Enablement Manager, Revenue Enablement at LaunchDarkly
This fragmentation becomes expensive when reps encounter scenarios they haven’t mastered. Matt notes that reps typically care about training “when their tire goes flat”—meaning they scramble for help only after fumbling a critical customer conversation. By then, the opportunity is usually lost.
The path forward isn’t more sophisticated AI but better connections between existing tools. Matt mentions MCP servers as one potential solution, though he remains realistic: “It’s still an integration game.”
Build for augmentation, not automation
Matt’s experiments revealed a counterintuitive truth about AI in sales training. Perfect AI responses actually create worse outcomes than slightly flawed systems. “They’re spooky, right? They’re so smart, but they’re still really dumb in a lot of ways,” he observes. When AI occasionally misunderstands context, reps develop stronger critical thinking skills to handle real customer curveballs.
“I think I read an article about how hallucinations are a mathematical inevitability... You’re still gonna need a human in the loop.”
Matt Magne, Senior Enablement Manager, Revenue Enablement at LaunchDarkly
This philosophy shapes Matt’s vision for sales enablement. Rather than replacing human coaches, AI should surface insights that help managers provide targeted support. Picture AI noticing struggle patterns and automatically alerting the right coach with specific conversation examples and suggested interventions.
Matt recalls discussions from eight years ago about whether AI would replace sales roles. “We were talking about it then, and they still haven’t,” he notes. The future belongs to teams that use AI to make humans more effective, maintaining what Matt calls “someone gluing everything together, and someone kind of making sure it’s not completely off the rails.”
The real playbook for data leaders
Matt’s experience offers three actionable insights for data science leaders evaluating sales enablement AI:
Start with integration architecture, not AI features. Map where performance data currently lives across your organization. Build connections between systems before adding new AI capabilities. The fanciest role-play tool becomes just another silo without proper integration.
Design for human-AI collaboration. The goal isn’t to automate coaching but to surface insights that help human coaches intervene effectively. Slightly imperfect AI that keeps humans engaged produces better results than flawless automation.
Focus on the “flat tire” moments. Reps need help when they’re stuck in real conversations, not during scheduled training. Build systems that deliver relevant practice and coaching exactly when reps will actually use it.
The uncomfortable truth Matt’s experiments reveal is that most sales organizations aren’t ready for AI transformation because they haven’t solved basic data integration. Until you can connect insights from practice sessions to coaching conversations to deal with outcomes, even the most sophisticated AI just adds complexity without solving core problems.
Connect and learn more
Subscribe to the Data Faces Podcast for more conversations with leaders making AI and analytics work in the real world. Available on YouTube | Spotify | Apple Podcasts
Want to discuss implementing voice-enabled AI for sales enablement without falling into the integration trap? Connect with Matt Magne on LinkedIn at linkedin.com/in/exrocker.
Listen to the full conversation with Matt Magne on the Data Faces Podcast.
Based on insights from Matt Magne, Senior Enablement Manager, Revenue Enablement at LaunchDarkly, featured on the Data Faces Podcast.
Podcast Highlights - Key Takeaways from the Conversation
[0:06] The evolution from MDM to AI-powered enablement
Matt shares his journey from solving master data management problems 20 years ago to tackling the same integration challenges in sales enablement today - proving some problems persist across decades and technologies.
[1:53] LaunchDarkly explained: The Christmas lights analogy
Understanding feature flags through a brilliant analogy - install lights once, flip switches all year without climbing back on the roof. This complexity creates unique sales training challenges.
[6:11] Why traditional sales enablement is broken
The “flat tire principle” - reps only care about training when they’re already stuck in a customer conversation, making traditional front-loaded onboarding ineffective.
[10:15] Voice AI breakthrough: Real conversations, not scripts
“You can verbally give an objection to it and it will verbally respond back” - how voice-enabled AI creates safe practice spaces where reps actually want to train.
[21:23] The 30-tool disaster: Sales tech stack fragmentation
AEs juggle 20-30 different solutions daily, creating data silos that prevent coaches from seeing the full performance picture.
[28:30] Why perfect AI is worse than flawed AI
“They’re spooky smart but still really dumb” - Matt’s counterintuitive discovery that hallucinations and imperfections actually improve rep critical thinking.
[33:18] The holy grail: 60-minute sessions condensed to 7 minutes
Matt’s vision for AI that intelligently summarizes long training sessions into digestible content reps will actually consume.
[37:05] Integration before innovation: The MCP server opportunity
Breaking down data silos matters more than adding new AI tools - why the future requires connecting existing systems first.
[40:59] Augmentation, not automation: Keeping humans in the loop
“The future is still augmentation” - why AI won’t replace human coaches but will make them dramatically more effective.
About David Sweenor
David Sweenor is an AI, generative AI, and product marketing expert. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.
Books
Artificial Intelligence: An Executive Guide to Make AI Work for Your Business
Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies
The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications
The CIO’s Guide to Adopting Generative AI: Five Keys to Success
Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence
The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success
With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.
David holds several patents and consistently delivers insights that bridge technical capabilities with business value.
Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.
