Why Most AI Programs Won’t Survive 2026

Описание: AI experimentation isn’t the problem.
Staying there is.
By 2026, execution will decide which AI programs survive.
Most systems aren’t failing because the models are weak, but because the foundations are wrong.
This episode breaks down what is actually changing in 2026 as AI moves from experimentation into execution. It examines why dashboards, pilots, and isolated tools are no longer enough, and why many AI programs are being quietly shut down when costs rise, risks surface, and value remains unclear. The focus is on how real organizations must design AI systems that hold up under real-world operational pressure.
This isn’t theory. This is how AI systems either earn long-term trust and funding or lose executive support and get cut.
In this episode, you’ll learn:
• Why AI and analytics are converging, and why dashboards alone will not scale in 2026
• How real value comes from AI embedded inside business workflows, not chatbots or copilots
• Why architecture, not models, is the root cause of most AI cost overruns and failures
• What it means to embed governance technically instead of relying on policies and PDFs
• How analytics mesh enables scale without sacrificing trust or control
• Why skills and AI literacy are now a bigger constraint than tools or platforms
This episode is for leaders, project professionals, architects, and teams responsible for delivering AI that actually works. If you are accountable for outcomes, budgets, risk, or long-term adoption, this breakdown clarifies what will matter most as AI becomes a core business system, not an experiment.
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References & Frameworks
• Gartner.com: 2026 Planning Guide for Analytics and Artificial Intelligence
• Analytics mesh operating model
• AI governance and risk management principles
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Winning with AI in 2026 depends on treating it as a governed system, an architectural discipline, and a true business capability.