Companies poured $109B into AI in 2024, yet productivity stats haven't moved. Is this an infrastructure build-out or a sophisticated bubble?
A CTO couldn't tell his board what AI tools the company was using. Most organizations can't answer basic questions about their AI spending either.
$2.3M spent, and nobody used the platform. Three strategic questions — skipped by most leaders — separate AI success from expensive experimentation.
Most companies burn millions on AI that solves the wrong problems. The difference between AI strategy and the AI graveyard is focus.
Start with business problems, not AI solutions. Here's how to separate real AI opportunities from expensive distractions — and when to wait.
McKinsey's latest AI survey shows what separates experimentation from real value: workflow redesign, CEO oversight, and meaningful KPIs.
The orgs winning with AI aren't the ones with the biggest budgets — they're the ones connecting AI directly to business outcomes. Build your flywheel.
Everyone's impressed by what AI can produce. But flashy outputs aren't business outcomes — and confusing the two is an expensive trap.
AI investment isn't optional — it's a strategic necessity. Here's how to pick the right AI projects, align them with your goals, and manage risk.
The 'brochure costs' of AI are only 30% of the real investment. Data prep, compliance, change management, and opportunity costs make up the other 70%.
67% of orgs are doubling GenAI investment, but 68% have moved less than 30% of experiments to production. Here's what's blocking scale.
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