A report by digital services and consultancy firm Infosys Knowledge Institute has found that agentic AI is emerging as a priority for operating model transformation. Many organisations are now transitioning from experimentation to scaled deployment, thanks to decreasing costs and increasing success rates.
The report, titled Infosys AI Business Value Radar, surveyed 3,240 companies worldwide across 132 different AI business use cases and highlights a significant shift in AI deployment.
The research reveals that 19% of AI use cases deliver on all their business objectives, while another 32% show promise by partially meeting their objectives. The research also indicated that organisations prioritising core, transformational AI use cases are more likely to achieve business objectives.
As AI costs decline in the future, the research data indicates these transformational use cases will rapidly begin to deliver more effective business outcomes. Well-designed change management strategies combined with robust employee training efforts enhance AI deployment success rates by up to 18 percentage points.
These findings underline the growing potential of enterprise AI to deliver business value across industries when supported by bold action focused on reshaping business processes, employee training and data architecture to ensure success.
“Enterprise AI is ready to scale,” said Infosys Chief Delivery Officer Satish H C. “With effective use of data architecture, operating models, and employee readiness, businesses can accelerate their adoption of AI to achieve measurable success.”
“Our research indicates that agentic AI is critical to operating model transformation,” he added. “We expect this to develop significantly over the coming year to become the driving force of enterprise transformation as it reshapes business processes, operating models, and technical architectures.”
Key findings and recommendations from the Infosys AI Business Value Radar report include;
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White-collar and technically focused industries, such as professional services, life sciences, high tech, telecommunications, and insurance, tend to achieve more success from AI;
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Financial services are the only white-collar industry that ranks just below average on performance, likely due to regulatory and data modernisation challenges
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AI is not benefiting all industries equally. Travel and hospitality, manufacturing, retail, and the public sector struggle to achieve consistent success;
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IT, operations, and facilities is the most pursued AI use case category, with 38% of respondents implementing it. This is followed by cybersecurity, resilience, and software development, with 30% pursuing these categories. Use cases in these categories are 10%-15% more likely to succeed;
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The next most pursued are marketing, customer service, and sales.
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Industry-specific applications, such as claims processing in insurance and clinical trials in life sciences, tend to improve core business operations. These use cases typically require transformation of data and technical architecture;
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Only 16% of companies have implemented effective change management and employee training for AI; and
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Companies that have taken initial steps to address AI can nearly double their likelihood of success with AI deployments by fully investing in workforce AI readiness.
“In our largest AI research to date, we have uncovered the drivers of AI business success,” said Head of Infosys Knowledge Institute Jeff Kavanaugh. “Organisations that go beyond experimentation and fundamentally change their operating model, as well as support their employees through the journey, are most likely to thrive in the era of Enterprise AI.”
The report recommends these five critical steps to become AI-first and generate business value from AI deployments:
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Accelerate agentic AI as a route to operating model transformation;
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Speed up innovation by simultaneous experimenting through an AI foundry and AI factory model;
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Prepare employees by investing in training to achieve an 18-percentage point uplift in use case success;
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Adopt a product-centric mindset to support AI operating models; and
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Create an AI governance task force to reduce risk and improve accountability.
You can read the full report here.
