Cognizant's Ollie O'Donoghue on AI's Value Creation Struggle

2026-07-12 · Source: AI Magazine · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Intermediate, short

Summary

Cognizant's Head of Research for the UK, Ollie O'Donoghue, highlights why many AI initiatives fail to deliver consistent business value, despite AI's rapid advancements. While AI can perform work equivalent to US\$4.5 trillion in labor value in the US and 93% of jobs are now exposed, MIT research indicates 95% of projects miss expectations. O'Donoghue explains that organizational adaptation, with its slower planning cycles and governance models, lags behind AI's evolving capabilities, which now include multimodal models and agentic systems. Many programs stall post-pilot because off-the-shelf solutions lack workflow fit or teams are unsure how AI reshapes roles. The rise of agentic AI, automating over 60% of core tasks in some management roles, further complicates accountability. Successful integration requires practical, ongoing skilling tied to specific tasks, ensuring human confidence and trust in AI-informed decisions. Organizations must embed AI into core systems, establish clear ownership, and facilitate role adaptation to achieve lasting performance gains.

Key takeaway

For Directors of AI/ML struggling to scale AI initiatives beyond pilots, recognize that organizational adaptation, not just technical capability, is your primary hurdle. You must integrate AI into core business systems, clearly define accountability for AI-informed decisions, and invest in continuous, task-specific skilling for your teams. This approach ensures AI becomes a dependable tool, driving lasting performance gains rather than remaining a series of isolated experiments.

Key insights

AI value creation struggles because organizational adaptation lags rapid technological capability advancements.

Principles

• Organizations must adapt at AI's speed.
• Integrate AI into core workflows with clear accountability.
• Ongoing, practical skilling builds human-AI trust.

In practice

• Embed AI into core business systems.
• Establish clear ownership for AI-driven outcomes.
• Implement task-specific, continuous AI training.

Topics

• AI Value Creation
• Organizational Adaptation
• AI Governance
• Agentic AI
• Workforce Skilling
• AI Integration

Best for: Executive, Director of AI/ML, VP of Engineering/Data, Consultant

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