Companies worldwide recognize artificial intelligence (AI) as a transformative force, poised to significantly boost productivity and revolutionize industries. Yet, a new study from Infosys Knowledge Institute (IKI), the research arm of Infosys (NSE, BSE, NYSE: INFY), reveals a stark reality: while businesses anticipate AI-driven productivity gains of 10-40%, only 2% of organizations are fully prepared to integrate AI into their operations on a comprehensive scale.

Infosys’ Enterprise AI Readiness Report, which surveyed over 1,500 respondents across Australia, New Zealand, France, Germany, the United Kingdom, and the United States, highlights a major readiness gap. Despite an overwhelming consensus on AI’s potential to reshape business models and create new economic opportunities, fundamental readiness in five key areas—strategy, governance, talent, data, and technology—is still largely lacking. These gaps hinder the effective adoption of AI and, consequently, the realization of its full benefits.

Executives see AI as the next industrial revolution, one that has the potential to transform industries, reshape traditional business models, and propel the new economy forward. However, the report reveals a glaring gap: enterprises predict productivity gains averaging 15%, with some forecasting as much as 40%, but only 2% of companies are adequately equipped across all readiness dimensions, limiting their ability to harness AI’s transformative power.

Technology and Data Remain Major Barriers

Infosys’ research reveals that technology readiness is one of the most significant hurdles for companies looking to adopt AI. Only 9% of surveyed companies possess the requisite AI technologies—such as machine learning frameworks, prebuilt algorithms, and scalable computing infrastructure—needed to successfully implement enterprise-wide AI solutions. The scarcity of advanced technological capabilities highlights the urgent need for investments in cutting-edge AI infrastructure. Additionally, data challenges continue to plague enterprises, as only 10% of organizations report easy access to high-quality data for AI projects. The quality, health, and accessibility of data are critical factors that can make or break the success of AI initiatives, yet these challenges continue to be a major roadblock to effective adoption.

For enterprises to bridge these readiness gaps, Infosys recommends focusing on five critical areas: developing a clear AI strategy, establishing responsible governance, upskilling talent, preparing data infrastructure, and fostering a culture of innovation. A well-defined AI strategy must align with business objectives, ensure ethical considerations, and outline clear technology investments. Talent development, ethical AI practices, and the nurturing of a supportive culture are all essential for future competitiveness in the AI-driven landscape.

Steps to Close AI Readiness Gaps

Developing a comprehensive AI strategy is crucial for organizations seeking to leverage the full power of AI. However, only 23% of respondents reported having a ready AI strategy in place. A robust AI strategy that is well-aligned with business goals can not only enhance growth but also uncover use cases that drive value, such as optimizing operations, enhancing customer experiences, and creating new revenue streams. Effective AI governance is also key to mitigating risks associated with AI, such as bias, misuse, and security threats. Despite this, only 10% of organizations possess well-defined governance structures that can adequately manage these risks. Infosys’ Responsible AI Office, part of Infosys Topaz, exemplifies how centralized governance can add enterprise value while reducing risks, ensuring that AI is implemented responsibly and effectively.

Another critical factor in closing the readiness gap is upskilling the workforce. Despite the promise of AI to transform industries, only 21% of respondents indicated that their employees have the knowledge required to adopt AI tools, and just 12% of organizations currently offer sufficient training programs for their employees. Closing the skills gap is paramount, and forward-looking companies are beginning to create AI skill pathways that aim to equip employees with the necessary skills and knowledge to work alongside AI systems effectively. Building internal AI expertise is key to driving successful AI adoption, fostering collaboration between humans and machines, and ensuring workforce readiness for the AI-driven future.

In addition to talent development, preparing data infrastructure for AI is essential. Data serves as the fuel for AI models, yet many enterprises struggle with data quality and accessibility. A significant portion of companies—30%—rate their data quality as poor, which hinders the effective use of AI technologies. Improving data accuracy, standardization, storage, and accessibility is fundamental for effective AI implementation. Organizations must invest in robust data management solutions that ensure high-quality, well-governed data is available for AI initiatives.

Finally, cultivating a culture of innovation is critical for fostering AI adoption. Investing in foundational technologies, such as machine learning and automation, is necessary to enhance customer experiences, reduce operational errors, and boost compliance. However, only 9% of enterprises are fully technology-ready, indicating that significant work remains to be done to foster an innovation culture and ensure that organizations are prepared to invest in foundational AI technologies. Companies that create an environment that encourages experimentation, continuous learning, and the adoption of new technologies will be better positioned to leverage AI and create sustainable competitive advantages.

Expert Opinions Highlight the Path Forward

Mohammed Rafee Tarafdar, Chief Technology Officer of Infosys, emphasized the importance of establishing a robust, scalable foundation for enterprise-wide AI readiness. He noted that building data readiness, establishing responsible AI guardrails, and driving an AI talent transformation strategy are crucial to accelerating AI development and democratizing AI capabilities. “This must be complemented by an AI foundry and factory model for scaling AI initiatives across the enterprise,” Tarafdar stated. According to him, these foundational elements are vital to creating an environment where AI can thrive and deliver on its promise of transforming business processes, improving decision-making, and fostering innovation.

Jeff Kavanaugh, Head of Infosys Knowledge Institute, added that while AI promises up to 40% productivity gains, the gap between potential and readiness remains both a challenge and an opportunity for enterprises worldwide. He highlighted that companies that act now—by building clear AI strategies, establishing strong governance frameworks, and upskilling their talent—will not only lead the next wave of innovation but also fundamentally reshape their industries. “AI is not a distant goal; it is the prerequisite foundation for future competitiveness. The time to invest in AI readiness is now,” Kavanaugh explained. He further elaborated that the companies investing in AI readiness today are setting themselves up to be industry leaders, as AI continues to become the critical enabler of business transformation in the coming years.

The report from Infosys ultimately underscores the urgent need for organizations to take a proactive approach to AI adoption. The disparity between perceived potential and actual readiness represents both a challenge and an immense opportunity. Companies that take the necessary steps now to prepare their technology, talent, and data infrastructures, as well as establish strong governance and foster innovation, are those that will be able to fully realize the productivity gains promised by AI. In this transformative era, the organizations that recognize AI as the key to unlocking efficiency, innovation, and future success will be the ones to thrive.

  AI Readiness Report, Australia, Enterprise AI, France, Germany, Infosys, Infosys Knowledge Institute, Jeff Kavanaugh, Mohammed Rafee Tarafdar, New Zealand, United Kingdom