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."

Don't assume AI output is inherently correct. An expert at Databricks shared that running the same prompt across the top five AI providers yields distinctly different answers. This proves human oversight is crucial to question, validate, and contextualize AI-generated responses before acting on them.

Unlike traditional B2B markets where only ~5% of customers are buying at any time, the AI boom has pushed nearly 100% of companies to seek solutions at once. This temporary gold rush warps perception of market size, creating a risk of over-investment similar to the COVID-era software bubble.

People overestimate AI's 'out-of-the-box' capability. Successful AI products require extensive work on data pipelines, context tuning, and continuous model training based on output. It's not a plug-and-play solution that magically produces correct responses.

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.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

Advanced AI tools like "deep research" models can produce vast amounts of information, like 30-page reports, in minutes. This creates a new productivity paradox: the AI's output capacity far exceeds a human's finite ability to verify sources, apply critical thought, and transform the raw output into authentic, usable insights.

AI's biggest risk is not incompetence, but its tendency to fill context gaps with general industry knowledge. This can seem insightful but leads to hallucinations. AI Stewards must provide specific business data and knowledge to constrain the AI and ensure relevant, accurate output.