HCLTech Study: How Businesses Drive Revenue Impact with AI

2026-07-25 · Source: AI Magazine · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Fundamental Awareness, quick

Summary

An HCLTech study, "The Blueprint for AI Leadership," surveyed 500 business and IT leaders, revealing that while 90% acknowledge generative and agentic AI are transforming workflows, only 18% report a significant revenue impact. This 18% are termed "AI Leaders," who are four times more likely to scale agentic AI and 63% more likely to secure senior leadership sponsorship than "AI Followers." AI Leaders integrate AI into core business strategy, establish data readiness, and prioritize workforce transformation, with 93% implementing structured upskilling programs compared to 20% of followers. They also possess organization-wide retraining strategies and encourage experimentation. Critically, AI Leaders are eight times more likely to trust their data for generative AI efforts, emphasizing that achieving revenue impact requires a holistic shift across leadership, culture, and foundational infrastructure.

Key takeaway

For Directors of AI/ML evaluating your organization's AI strategy, recognize that merely adopting AI tools is insufficient for revenue impact. Your focus must shift from narrow efficiency gains to holistic integration across core business strategy, data readiness, and workforce transformation. Implement structured upskilling programs and ensure your data infrastructure supports generative AI efforts to move beyond operational pilots and achieve tangible business value.

Key insights

Only 18% of firms achieve significant AI revenue impact by integrating AI holistically across strategy, data, and workforce.

Principles

• AI Leaders prioritize holistic integration.
• Data readiness is crucial for GenAI.
• Workforce upskilling drives AI adoption.

Method

Achieving AI revenue impact requires rethinking business workflows, embedding AI into everyday decisions, and coordinating shifts across leadership, culture, and foundational infrastructure.

In practice

• Implement structured upskilling programs.
• Encourage team experimentation with AI.
• Ensure robust data infrastructure.

Topics

• AI Leadership
• Generative AI
• Agentic AI
• Workforce Transformation
• Data Readiness
• Revenue Impact

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

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