A Lawyer’s Take On Why AI Investments Keep Disappointing

2026-06-11 · Source: The AI Journal · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Legal & Regulatory · Depth: Intermediate, short

Summary

UK businesses have invested between £22 and £30 billion in generative AI since its recent surge, yet only 5% report meaningful returns, according to MIT's State of AI in Business 2025 research. This article argues that the issue lies with management practices, not the technology itself, which is performing as designed. Organizations often respond to disappointing results by acquiring more tools or waiting for new models, rather than addressing underlying operational deficiencies. The research highlights that 75% of professionals plan AI investment, but only 31% have a formal strategy, with a clear roadmap making companies up to 3.5 times more likely to achieve measurable returns. The core problem is a failure to treat AI as an operational capability, focusing on output over measurable business outcomes, and lacking clear accountability and governance.

Key takeaway

For leaders overseeing AI initiatives or considering new investments, recognize that disappointing returns are likely due to organizational and strategic gaps, not technology shortcomings. Prioritize developing a formal AI strategy with clear accountability for tools and deployments, treating AI as a core operational capability. Focus on measuring tangible business outcomes rather than just output, and provide vetted AI tools with explicit usage guidelines to prevent shadow IT risks. Your success hinges on robust governance and operational discipline, not just the next model release.

Key insights

AI investment failures stem from poor organizational management and strategy, not inherent technology limitations.

Principles

• Treat AI as an operational capability, not an experiment
• Focus on measurable business outcomes, not just visible output
• Assign clear accountability for AI tools and deployments

Method

Integrate AI into defined workflows, assign specific owners, establish metrics tied to business objectives, and review AI operations like any other part of the business.

In practice

• Vet AI tools and provide clear usage guidance to employees
• Establish a single line of AI governance accountability
• Define specific roles and responsibilities for AI management

Topics

• AI Governance
• AI Strategy
• Enterprise AI
• Business Outcomes
• Shadow IT
• AI Investment

Best for: AI Product Manager, CTO, Executive, Director of AI/ML, VP of Engineering/Data, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI Journal.