Why My AI does’t Work

Given the bold promises about artificial intelligence (AI) revolutionizing industries and automating processes, it’s no surprise that discussions around unmet expectations are growing louder. According to a survey by Capgemini (source), AI has made significant strides in enhancing customer experiences, with companies reporting quick wins, such as cost reductions through chatbots. 
However, despite these advancements, many tools fell short in delivering real value where it matters most—49% of customers reported receiving little to no benefit from their interactions. For business owners looking to invest wisely in AI tools, a clear understanding of their current data processes, workforce capabilities, and technological infrastructure is essential. A thoughtful evaluation of these factors can significantly influence the success and usability of AI automation. 
The potential of AI remains undeniable. PwC estimates that AI could contribute a staggering $15.7 trillion to the global economy by 2030, while the Harvard Business Review projects that machine learning alone could generate up to $2.6 trillion in value for marketing and sales. For businesses, these numbers emphasize the importance of integrating AI automation—but only after carefully considering the key factors that will ensure its success. By aligning AI tools with strategic goals and existing capabilities, companies can maximize their ROI and stay ahead in the competitive landscape. 

Reasons Automation Faces Errors: 

• Poor access to relevant information

In 2022, VentureBeat reported that the average employee spent over 3.5 hours daily searching for the information needed to perform their tasks—an hour longer than the previous year. This inefficiency highlights a clear opportunity for AI-driven automation to revolutionize workflows. However, successful AI automation requires seamless integration with trusted and relevant information sources. Unfortunately, many AI solutions fall short in this area, defaulting to tools like the Google Search API. While Google Search is undeniably powerful, it can overwhelm AI models with irrelevant or misleading data if left unfiltered.

To ensure meaningful results, it’s critical to filter search outputs and prioritize reliable, relevant sources. By doing so, businesses can avoid “data noise” and maximize the effectiveness of their AI automation initiatives. The future of Automation lies in AI systems that not only enhance efficiency but also deliver accurate, actionable insights—making the choice of information sources a cornerstone of any automation project.. 

• Excessive Automation  

Many AI automation projects aim to fully automate processes from start to finish, often overlooking the critical role of human confirmation at key stages as a safeguard against risks. This over-reliance on automation can lead to errors and runtime exceptions over time, especially when factors such as AI failure or hallucination—a common issue with AI models—are not adequately addressed. For instance, while automating data extraction from documents is a straightforward application of AI, the task of validating or dating the extracted information should remain independent of AI. Introducing validation steps alongside AI-driven processes not only mitigates the risk of delivering inaccurate information but also ensures the system remains robust against errors stemming from AI malfunctions. By striking the right balance between automation and human oversight, businesses can enhance the reliability of their AI systems while reducing risks associated with excessive automation. Thoughtful integration of human input at critical stages is essential for building trust and achieving long-term success in AI automation initiatives.

• No AI Result Filtering  

AI has advanced significantly over the past few years and it will continue to advance but One key thing associated with AI is Noise in Results or Hallucination of AI . The AI could hallucinate things that are not part of input or add noise to the current results from previous conversations. This could significantly affect the health of complete AI Automation deliverable and is required to be filtered using various criteria and requirements for the end user.