New research from HCLTech reveals a stark divide in enterprise AI adoption: while nearly every organisation says generative and agentic AI are reshaping how work gets done, only a small fraction are converting that activity into measurable revenue. The report, The Blueprint for AI Leadership, surveyed 500 business and IT leaders and splits them into two camps — AI Leaders and AI Followers — to explain why some organisations are pulling decisively ahead.
A Widening Gap Between Activity and Outcomes
The research found that 90% of organisations say generative and agentic AI are transforming their workflows, yet just 18% report that AI is delivering a significant impact on revenue. HCLTech calls this 18% AI Leaders, with the remaining 82% labelled AI Followers — enterprises adopting AI tools without translating that adoption into meaningful business value.
AI Followers, the report notes, tend to evaluate AI purely through the lens of efficiency and cost savings, which limits how much value they can extract from their investments. AI Leaders, by contrast, are four times more likely to scale agentic and autonomous AI, and 63% more likely to secure senior leadership sponsorship for their initiatives.
"The gap between AI Leaders and AI Followers isn't a single tool, model or platform decision. It's the ability to make value real for the business, build confidence across the workforce and scale responsibly on modern foundations."— PAWAN VADAPALLI, CORPORATE VICE PRESIDENT AND GLOBAL HEAD OF DIGITAL BUSINESS SERVICES, HCLTECH
Follow the Example of the AI Leaders
AI Leaders have woven AI into business strategy, built stronger data foundations and moved further along in workforce transformation. 93% of AI Leaders have structured upskilling programmes in place, compared with just 20% of AI Followers.
Nearly nine in ten AI Leaders run an organisation-wide strategy for retraining and upskilling employees, and 54% actively encourage teams to experiment with AI tools and new ways of working. AI Leaders are also eight times more likely to trust that their data can adequately support generative AI efforts, underlining how central data confidence is to progress.
Sebastian Reiche, Professor of People Management at IESE Business School, points to workforce readiness as a decisive factor: employees are often more willing to adapt to AI than leaders assume, particularly when they are given a say in how tools are integrated into their roles and can see a clear vision for how their work will evolve.