Data Axle's CEO warns that while AI can make good decisions quickly, it also amplifies errors from a weak data foundation, making bad decisions at an unprecedented speed. This makes data quality more critical than ever in the AI era, as poor data leads to flawed outcomes at scale.

Companies believe AI isn't delivering because technology moves too fast, so they invest in training and agile frameworks. The real, invisible problems are structural: ambiguous decision rights, siloed data ownership, and misaligned employee incentives. Solving for 'speed' when the foundation is broken guarantees failure.

Simply using AI to speed up tasks like product discovery is dangerous if the underlying process is flawed. Automating a weak discovery process doesn't yield better insights; it just generates poor results faster and at a greater scale, creating an "efficiency trap."

AI models fail in business applications because they lack the specific context of an organization's operations. Siloed data from sales, marketing, and service leads to disconnected and irrelevant AI-driven actions, making agents seem ineffective despite their power. Unified data provides the necessary 'corporate intelligence'.

Revenue leaders are pressured to show AI ROI, but focusing on the shiniest new AI tool is a mistake. Real gains come from addressing foundational issues like internal data silos and poor data quality before deploying AI, as the technology is only as good as the data it's fed.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Adopting AI acts as a powerful diagnostic tool, exposing an organization's "ugly underbelly." It highlights pre-existing weaknesses in company culture, inter-departmental collaboration, data quality, and the tech stack. Success requires fixing these fundamentals first.

While bad data has always led to bad decisions, AI compounds the problem exponentially. The speed and scale of AI-driven actions mean the consequences of inaccurate data are far more severe and immediate, as it makes bad decisions faster.

According to Salesforce's AI chief, the primary challenge for large companies deploying AI is harmonizing data across siloed departments, like sales and marketing. AI cannot operate effectively without connected, unified data, making data integration the crucial first step before any advanced AI implementation.