Eshaan Jain, a leader in enterprise AI and CPQ transformation at T-Mobile, asserts that the effectiveness of agentic AI systems hinges more on the quality of existing company data than on the AI models themselves. During a recent virtual discussion, Jain criticized the trend of companies attempting to implement AI agents without ensuring the underlying data is clean and structured. He explained that an AI agent's performance is directly tied to the quality of contract, product, and pricing data, warning that poor data leads to unreliable decisions. Drawing from his experience at Amazon, Jain emphasized the critical role of data preparation, stating that cleaning and labeling contract language is often more crucial than developing the AI model itself. He also addressed future challenges, including the financial and environmental costs associated with large AI models. Jain concluded by urging companies to treat their CPQ and contract data as essential revenue products, advocating for real investment in data quality to ensure successful AI outcomes.