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 improve efficiency and drive business growth. However, according to TCS CEO K Krithivasan, the reality is far from rosy. Citing research, Krithivasan claimed that a staggering 95% of enterprise AI pilots have failed to deliver measurable value. This stark revelation has significant implications for businesses and organizations looking to leverage AI to drive success.
In a recent statement, Krithivasan noted, “As we look ahead to 2026, a clearer picture of AI’s impact is emerging.” He added, “We are witnessing…a new form of organisational intelligence, where combinations of humans and machines shape how choices are developed, presented and discussed.” This statement highlights the evolving nature of AI and its potential to transform the way organizations operate and make decisions.
The failure of AI pilots to deliver meaningful efficiency is a concern that has been echoed by many experts in the field. Despite the significant investment in AI, many organizations struggle to achieve tangible results. There are several reasons that contribute to this failure, including the lack of a clear strategy, inadequate data quality, and insufficient expertise.
Krithivasan’s statement highlights the need for organizations to rethink their approach to AI and focus on developing a more comprehensive strategy that takes into account the complexities of AI implementation. He emphasized the importance of combining human intelligence with machine intelligence to create a new form of organizational intelligence.
So, what can organizations do to avoid the pitfalls of AI pilots and ensure that they deliver meaningful efficiency? Here are a few key takeaways:
- Develop a clear strategy: Before embarking on an AI pilot, it is essential to have a clear understanding of what you want to achieve. Define your goals, identify the problems you want to solve, and develop a roadmap for implementation.
- Ensure data quality: AI is only as good as the data it is trained on. Ensure that your data is accurate, complete, and relevant to the problem you are trying to solve.
- Build a skilled team: AI requires a unique set of skills, including data science, machine learning, and programming. Ensure that your team has the necessary expertise to develop and implement AI solutions.
- Focus on human-machine collaboration: AI is not a replacement for human intelligence, but rather a tool that can augment and enhance human capabilities. Focus on developing solutions that combine the strengths of humans and machines.
- Measure and evaluate: Finally, it is essential to measure and evaluate the effectiveness of your AI pilot. Establish clear metrics and benchmarks to assess the success of your pilot and make adjustments as needed.
In conclusion, the failure of AI pilots to deliver meaningful efficiency is a concern that organizations cannot ignore. As Krithivasan noted, the future of AI is emerging, and it is essential to develop a new form of organizational intelligence that combines the strengths of humans and machines. By following the principles outlined above, organizations can ensure that their AI pilots deliver tangible results and drive business success.
As we look ahead to 2026, it is clear that AI will continue to play a significant role in shaping the future of business. However, it is essential to approach AI with a clear understanding of its limitations and potential. By doing so, organizations can unlock the full potential of AI and drive meaningful efficiency and growth.
About the Author
