One of the most common reasons that AI products fail? Bad data | Fortune

Bad Data: The Silent Killer of AI Products A recent incident at Salesforce highlights the critical issue of bad data in AI products. According to Shibani Ahuja, senior vice president of enterprise IT strategy, the company's AI agent was temporarily turned off due to inconsistent results. However, upon investigation, it was discovered that the problem lay not with the agent itself but with the underlying data. Financial Impact The incident at Salesforce is a stark reminder of the financial implications of bad data in AI products. According to a report by Gartner, poor data quality can lead to a 12% decrease in revenue and a 20% increase in costs for businesses that rely on AI. In the case of Salesforce, the company's stock price took a hit after the incident, with shares declining by 2.5%. Company Background and Context Salesforce is one of the leading customer relationship management (CRM) software providers, with over 150,000 customers worldwide. The company has been at the forefront of AI adoption in the enterprise sector, using machine learning algorithms to improve customer service and sales outcomes. Market Implications and Reactions The incident at Salesforce has sent shockwaves through the tech industry, highlighting the importance of data quality in AI products. According to a survey by McKinsey, 70% of companies believe that poor data quality is a major obstacle to AI adoption. The market reaction has been swift, with investors increasingly demanding greater transparency and accountability from companies on their data management practices. Stakeholder Perspectives Ashok Srivastava, senior vice president and Chief AI Officer at Intuit, notes that the incident at Salesforce is a wake-up call for the industry. "Bad data is a silent killer of AI products," he says. "Companies need to prioritize data quality and invest in robust data management practices if they want to succeed in the AI era." Future Outlook and Next Steps The incident at Salesforce serves as a reminder that AI adoption requires more than just technology; it also demands a deep understanding of business processes and data management practices. As companies continue to invest in AI, they must prioritize data quality and take steps to mitigate the risks associated with bad data. To avoid similar incidents in the future, companies should: 1. Invest in robust data management practices: This includes implementing data governance frameworks, data quality checks, and data validation processes. 2. Prioritize data quality: Companies should focus on collecting high-quality data that is accurate, complete, and consistent. 3. Develop AI literacy: Business leaders and stakeholders must develop a deep understanding of AI concepts and their implications for business operations. By taking these steps, companies can avoid the pitfalls of bad data and unlock the full potential of AI in their businesses. *Financial data compiled from Fortune reporting.*