Conviva's CEO warns against "AI washing," where companies apply AI agents to poorly structured data. An agent cannot invent insights that aren't present in the source data. A strong data computation engine is the true prerequisite for effective AI, not a cosmetic front-end.

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.

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."

Instead of solving underlying data quality issues, AI agents amplify and expose them immediately. This makes protecting and managing data at its source a critical prerequisite for maintaining trust and achieving successful AI implementation, as poor data becomes an immediate operational bottleneck.

Unlike humans who debate flawed data in meetings (a "data brawl"), AI agents confidently present a single, incorrect number from bad data. This creates a "silent failure" where the error is persuasive and unnoticed, a phenomenon Salesforce's Gaurav Pathak calls "garbage in, gospel out."

AI won't alert you to underlying data integrity issues like broken contact tracking. Instead, it generates a confident-sounding analysis based on the messy data it's given, creating a significant risk of making strategic decisions based on incorrect information.

AI systems directly reflect the quality and trustworthiness of the underlying data. The danger is that AI presents conclusions with an air of authority, masking a shaky foundation and amplifying distrust when errors inevitably surface. It makes bad data sound confident.

The problem with AI has evolved beyond 'garbage in, garbage out.' Today's systems can rapidly ingest misinformation from public sources and present it as fact, creating a feedback loop. This means bad information is not only used for poor decisions but is actively amplified and distributed faster than ever before.

AI is not a silver bullet for inefficient systems. Companies with poor data hygiene and significant technical debt find that implementing AI makes their bad systems worse, simply scaling the noise and dysfunction rather than solving underlying problems.