When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.

Using AI to generate content without adding human context simply transfers the intellectual effort to the recipient. This creates rework, confusion, and can damage professional relationships, explaining the low ROI seen in many AI initiatives.

Salesforce's AI Chief warns of "jagged intelligence," where LLMs can perform brilliant, complex tasks but fail at simple common-sense ones. This inconsistency is a significant business risk, as a failure in a basic but crucial task (e.g., loan calculation) can have severe consequences.

Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.

The true danger of LLMs in the workplace isn't just sloppy output, but the erosion of deep thinking. The arduous process of writing forces structured, first-principles reasoning. By making it easy to generate plausible text from bullet points, LLMs allow users to bypass this critical thinking process, leading to shallower insights.

While AI can accelerate tasks like writing, the real learning happens during the creative process itself. By outsourcing the 'doing' to AI, we risk losing the ability to think critically and synthesize information. Research shows our brains are physically remapping, reducing our ability to think on our feet.

Research highlights "work slop": AI output that appears polished but lacks human context. This forces coworkers to spend significant time fixing it, effectively offloading cognitive labor and damaging perceptions of the sender's capability and trustworthiness.

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.

Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.