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You’ll be seated with a curated group of leaders aligned by role and organization stage, then kick off introductions by sharing where your company sits on the AI maturity curve. These are not random tables. This is thoughtful matching designed to maximize relevance, candor, and depth. Expect quick introductions that move past titles and into what you're actually building, testing, and wrestling with.

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We'll set the tone and reframe the question guiding the evening. Not “How fast can we adopt AI?” but “Do our people have the skills, standards, and support to use it well?” This moment aligns the room around shared ambition, shared pressure, and a shared opportunity: to move from improvisation to infrastructure.

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AI proficiency is expected to become standard in most non-technical roles within 24 months, and it's already wired into promotions (67%) and performance ratings (50%). But “AI literacy” is easy to say and nearly impossible to assess. Once rewards are on the line, vague standards stop being a training gap and become a fairness and consistency problem. The organizations that win will be the ones who can name the observable behaviors that matter, like proactively reviewing AI output and handling sensitive data with judgment, and evaluate them the same way every time.

Table Prompts: 

• Where are AI expectations already shaping talent decisions in your organization, formally or informally?
• Which signals do you actually trust when assessing someone's AI skill today, and which are just noise?
• What would a shared, observable standard change about how you hire, review, and grow people?

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Managers are the #1 group organizations expect to build AI capability across their teams, yet only 36% are seen as highly prepared to do it. When systems are weak, people improvise. Managers fill the gaps with informal judgment, standards fragment, and talent decisions turn inconsistent. This isn't about individual managers falling short. It's about asking people to coach and assess a skill no one gave them the definitions, rubrics, or training to evaluate

Table Prompts: 

• What do your managers need most right now: shared definitions, behavior rubrics, role-relevant examples, or coaching guidance?
• Where is manager improvisation already producing inconsistent ratings, promotions, or development calls?
• Who have you seen do manager enablement well, and what made it stick?

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73% of organizations invest in AI upskilling, but no single approach breaks 30% adoption, and there's no consensus on who owns the work: managers (24%), the employer (22%), technology providers (22%), or individuals themselves (20%). Meanwhile, the market has spoken about what it values. Quality control beats coding. The scarcest, premium-commanding skills, like AI safety and governance, output evaluation, and workflow redesign, are exactly the ones piecemeal training doesn't build. You can't buy your way into AI transformation. You have to build it into job architecture, compensation bands, and L&D pathways.

Table Prompts: 

• Which upskilling investments have actually changed behavior at your organization, and which just checked a box?
• If judgment, governance, and quality control are the premium skills, what should your L&D priorities be next year?
• What would it look like to reward the AI behaviors you value, in pay, promotion, and recognition, starting this cycle?

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We close the formal program, but the best conversations often continue here. Compare notes, exchange contact information, and solidify the relationships that will extend well beyond tonight. Leave sharper, better connected, and more strategically armed than when you arrived. Keep marching, my friend. We've got you.