AI Adoption Accelerates as Businesses Look to Drive Impact

Infosys Knowledge Institute, the research division of Infosys, has published its most comprehensive study yet on AI effectiveness. 

The Infosys AI Business Value Radar surveyed 3,240 companies worldwide, analysing 132 AI business use cases to assess the current state of AI implementation.

The findings indicate a clear shift: companies are no longer just testing AI but are scaling it to drive real business impact. 

According to the report, 19% of AI use cases fully meet their business objectives, while another 32% demonstrate partial success. 

As AI implementation costs decline, these success rates are expected to increase, particularly for organisations focusing on transformational AI applications.

Industries leading in AI adoption

Some industries are emerging as frontrunners in AI adoption, leveraging the technology to drive efficiency and innovation. 

Professional services, life sciences, high tech, telecommunications and insurance report the highest AI success rates. These sectors, which rely heavily on technical expertise and data-driven decision-making, are achieving substantial benefits from AI integration.

However, not all industries are advancing at the same pace. The financial services sector faces challenges due to regulatory constraints and outdated data infrastructures, slowing its AI progress. 

Meanwhile, industries such as travel and hospitality, manufacturing, retail and the public sector have struggled to implement AI successfully on a consistent basis.

Key AI use cases delivering success

The study identifies IT, operations and facilities management as the most widely adopted AI applications, with 38% of companies implementing AI in these areas. Cybersecurity, resilience, and software development are also key focus areas, with 30% of organisations investing in AI-driven solutions in these fields. 

These use cases not only attract high adoption rates but also deliver some of the strongest success outcomes.

Marketing, customer service and sales continue to be significant areas of AI investment, as companies seek to enhance customer interactions and personalise offerings. 

Additionally, industry-specific AI applications, such as claims processing in insurance and clinical trials in life sciences, are proving essential in improving efficiency and accuracy. 

However, these implementations often require substantial data transformation and upgrades to technical architecture.

Overcoming change management challenges

Despite AI’s expanding role in business, many companies struggle with change management and employee readiness. 

The report finds that only 16% of organisations have successfully implemented training and change management strategies for AI adoption. Infosys suggests that businesses investing in these areas could improve their AI success rates by up to 18%.

Jeff Kavanaugh, Head of Infosys Knowledge Institute, underscores the importance of organisational change in AI success. “In our largest AI research to date, we have uncovered the drivers of AI business success,” he explains. 

“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 study also highlights the growing role of agentic AI—AI systems that take proactive, autonomous actions—as a transformative factor in business models. 

As AI technology continues to evolve, enterprises are expected to integrate agentic AI to transform processes and technical infrastructures.

Satish H.C., EVP and Chief Delivery Officer at Infosys, emphasises the importance of this development, noting: “Enterprise AI is ready to scale. 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.”

Preparing for an AI-driven future

The report outlines five essential steps for organisations looking to maximise AI’s potential. 

These include accelerating agentic AI adoption, investing in AI innovation through a dual AI foundry and AI factory approach, enhancing employee training, adopting a product-centric AI operating model and establishing AI governance frameworks to manage risks.

As enterprise AI moves from concept to execution, businesses that invest in strategic AI deployment, workforce development and governance structures will be best positioned to generate long-term value. 

With AI adoption success rates rising, companies that take decisive action today are set to lead in the AI-driven economy of the future.

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