95% of AI pilots fail to deliver meaningful efficiency: TCS CEO
The world of artificial intelligence (AI) has been abuzz with excitement and promise, with many organizations investing heavily in AI pilots to harness its potential. However, according to K Krithivasan, CEO of Tata Consultancy Services (TCS), a staggering 95% of these AI pilots have failed to deliver measurable value. This striking statistic is based on research and highlights the challenges that organizations face in realizing the benefits of AI.
Krithivasan’s comments were made as he looked ahead to 2026, a year that is expected to bring a clearer picture of AI’s impact on businesses and society. He noted that as we move forward, a new form of organizational intelligence is emerging, one that combines the strengths of humans and machines to shape decision-making processes. “We are witnessing…a new form of organisational intelligence, where combinations of humans and machines shape how choices are developed, presented and discussed,” he said.
The failure of AI pilots to deliver meaningful efficiency is a significant concern for organizations that have invested substantial resources in these initiatives. AI has the potential to transform businesses, improving efficiency, reducing costs, and enhancing customer experiences. However, the reality is that many AI projects are not yielding the expected results, and this is causing organizations to re-evaluate their AI strategies.
So, what is going wrong? There are several reasons why AI pilots may not be delivering the expected outcomes. One of the primary challenges is the lack of a clear understanding of the business problems that AI is intended to solve. Many organizations are investing in AI without a clear understanding of how it can be used to drive business value. This can lead to AI projects that are not aligned with business objectives, resulting in limited or no impact.
Another challenge is the quality of the data used to train AI models. AI algorithms are only as good as the data they are trained on, and poor-quality data can lead to biased or inaccurate results. Additionally, many organizations are struggling to integrate AI into their existing systems and processes, which can limit its potential to drive efficiency and innovation.
To overcome these challenges, Krithivasan emphasized the importance of following five core principles when implementing AI. These principles include:
- Define a clear business problem: Organizations must clearly define the business problems they want to solve using AI. This involves identifying specific pain points and opportunities for improvement.
- Ensure data quality: High-quality data is essential for training accurate AI models. Organizations must ensure that their data is accurate, complete, and relevant to the business problem they are trying to solve.
- Develop a robust AI strategy: A robust AI strategy involves aligning AI initiatives with business objectives and integrating AI into existing systems and processes.
- Invest in talent and skills: AI requires specialized skills and expertise. Organizations must invest in talent and skills to ensure that they have the capabilities to develop and implement AI solutions effectively.
- Monitor and evaluate AI performance: Finally, organizations must monitor and evaluate the performance of their AI initiatives to ensure that they are delivering the expected outcomes.
By following these principles, organizations can increase the chances of success for their AI pilots and realize the benefits of AI. As Krithivasan noted, the future of AI is not about replacing humans with machines, but about combining the strengths of both to create a new form of organizational intelligence.
In conclusion, the failure of 95% of AI pilots to deliver meaningful efficiency is a wake-up call for organizations to re-evaluate their AI strategies. By following the five core principles outlined by Krithivasan, organizations can increase the chances of success for their AI initiatives and realize the benefits of AI. As we look ahead to 2026, it is clear that AI will continue to play a critical role in shaping the future of businesses and society. However, to realize its full potential, organizations must be willing to learn from their mistakes and adapt their approaches to AI.
About the Author
