Industry leaders from Coder, Scale AI, and Suger reveal why 95% of AI pilots fail—and share the frameworks that actually work to get agents into production.

Topics Include:

• Panel features leaders from Coder, Scale AI, and Suger discussing agentic AI.
• MIT report reveals 95% of AI pilots fail to reach production.
• Challenges are rarely technical—they're organizational, mindset, and people-driven instead.
• Companies lack documented tribal knowledge needed to train agents effectively.
• Many organizations attempt AI where deterministic, rules-based automation would work better.
• "Freestyle agents" concept: Some problems shouldn't be solved by agents at all.
• Regulated industries struggle when asking agents to handle highly differentiated, complex tasks.
• Common mistakes: building one universal agent or separate agents for every use case.
• Post-billing workflows and business-critical operations aren't ready for AI's black box.
• VCs pressure companies to define "AI-native"—but nobody has clear answers yet.
• Scale AI uses five maturity levels; Coder uses three tiers for adoption.
• Success metrics span operational readiness, business impact, and technology performance indicators.
• Production requires data governance, context, A/B testing, and robust fallback mechanisms.
• Even Anthropic uses agents conservatively: research tasks and log triage, no write-access.
• Path to 50% success requires agile frameworks, people change, and proper AI talent.

Participants:

• Ben Potter - VP of Product, Coder
• Raviteja Yelamanchili - Head of Solutions Engineering, Scale AI
• Jon Yoo - CEO, Suger
• Adam Ross - US, Partner Sales Sr. Leader, Amazon Web Services

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