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	<title>Revenue and AI governance: who decides, who is accountable, what pays off - Enrico Giubertoni</title>
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	<description>Revenue and AI governance is the system a company's leadership uses to decide which uses of artificial intelligence serve the strategy, who is accountable for them in the organisation chart and which indicators measure their return. Without it, AI spreads through individual initiative and multiplies projects, while revenue stays where it was.</description>
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		<title>AI Governance: Are You Architecting a Strategic Ally or an Internal Saboteur?</title>
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		<description>Separates the AI that works for the strategy from the AI that quietly contradicts it: the starting point of every leadership choice.</description>
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		<title>AI strategic vision: Will You Settle for Writing Prompts, or Will You Redefine Your Industry?</title>
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		<description>Distinguishes tactical use, which cuts costs, from the vision that creates competitive advantage, with three questions to bring to the board.</description>
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		<title>AI is the Body, Culture is the Mind: A Manichean Choice for Business Leaders</title>
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		<description>Explains why, when everyone has the same tools, the advantage comes from the company culture that guides them.</description>
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		<title> The AI Organization Chart and the Future of Work</title>
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		<description>The five roles to redesign in the organisation chart so that AI projects reach production.</description>
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		<title>Escape AI Paralysis: The AI Governance Framework You Need to Act with Confidence</title>
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		<description>A method to measure the company's AI maturity and escape decision paralysis, with the AI Act in the background.</description>
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		<title>The Battle for Trust: Winning the "Integrated Customer" by Overcoming the GenAI Divide</title>
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		<description>Why most generative AI investments do not pay back, and four ways to end up on the right side of the divide.</description>
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		<title>Shadow AI: the internal research nobody commissioned</title>
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		<description>AI used outside company rules, read as free internal research: it shows where people already need tools.</description>
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		<title>Beyond the Hype: A C-Suite Framework for AI Ethics, Bias, and Transparency</title>
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		<description>Hallucinations, bias (a model's systematic distortions) and haste: the three risks that end up in reputation and in the accounts.</description>
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