Key Takeaway
The report identifies IT, operations, and facilities management as the leading areas for AI implementation, with 38% of companies adopting AI in these sectors. Cybersecurity, resilience, and software development follow at 30%. Industry-specific applications like claims processing and clinical trials also demonstrate significant efficiency gains through AI. However, effective change management remains a challenge, with only 16% of organizations having proper training strategies for AI adoption. Investing in change management can enhance AI success rates by up to 18%. Jeff Kavanaugh from Infosys emphasizes that organizations must transform their operating models and support employees to succeed with AI.
The Most Successful Use Cases
The report identifies IT, operations, and facilities management as the leading applications of AI, with 38% of respondents implementing AI in these domains.
Following closely are cybersecurity, resilience, and software development, with 30% of companies concentrating on these use cases, which are also among the most likely to achieve success.
Marketing, customer service, and sales are also significant areas for AI investment.
In industry-specific applications, such as claims processing in insurance and clinical trials in life sciences, AI adoption is emerging as a crucial driver of efficiency and accuracy.
However, these applications often necessitate substantial transformation of data and technical architecture.
Change Management Remains a Key Challenge
Despite AI’s increasing influence, companies continue to face challenges with change management and employee readiness.
The report indicates that only 16% of organizations have established effective employee training and change management strategies for AI adoption.
Businesses that invest in these areas can enhance their AI success rates by up to 18%, according to Infosys.
Jeff Kavanaugh, Head of Infosys Knowledge Institute, underscores the significance of organizational change.
“In our largest AI research to date, we have uncovered the drivers of AI business success,” he explains.
“Organizations that move beyond mere experimentation and fundamentally transform their operating model, while also supporting their employees throughout the journey, are most likely to thrive in the era of Enterprise AI.”
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