AI’s biggest problem isn’t intelligence. It’s implementation

Fast Company Tech

by Mark Sullivan

February 19, 2026

AI-Generated Deep Dive Summary

The AI revolution is not just about intelligence—it’s about implementation. While major AI companies promise transformative changes across industries, the reality of widespread adoption remains elusive due to uneven integration, organizational culture, and workforce adaptation. A 2025 MIT study revealed that despite $30 billion to $40 billion spent on generative AI annually, 95% of large companies saw no measurable profit impact. However, more recent research from the Wharton School shows promise: three-quarters of enterprise leaders report positive returns, with 88% planning to increase AI investments. Yet, the true challenge lies in how organizations adapt their people and processes to harness AI effectively. Early adopters with clear strategies, skilled teams, and supportive cultures are making progress, while others struggle to prove ROI. AI’s impact also varies by industry and application. For example, software developers benefit from coding efficiency gains, while retailers see improvements in customer service through smarter chatbots. However, initial deployment often requires significant human effort to correct or train AI tools, limiting productivity gains. Moreover, AI-forward organizations report increased ambition among workers, leading to longer hours rather than reduced workloads. This highlights the complex interplay between technology and human behavior. A new benchmark, the Remote Labor Index (RLI), sheds light on AI’s real-world performance by testing agents on tasks like game development and product design. Despite rapid advancements in AI models, current tools struggle to complete even basic projects. For instance, Anthropic’s top-performing model completed only 3.5% of RLI tasks. This underscores the gap between AI hype and practical application, challenging bullish

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Originally published on Fast Company Tech on 2/19/2026