Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.

Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.

Many firms are stuck in "pilot purgatory," launching numerous small, siloed AI tests. While individually successful, these experiments fail to integrate into the broader business system, creating an illusion of progress without delivering strategic, enterprise-level value.

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.

A 'GenAI solves everything' mindset is flawed. High-latency models are unsuitable for real-time operational needs, like optimizing a warehouse worker's scanning path, which requires millisecond responses. The key is to apply the right tool—be it an optimizer, machine learning, or GenAI—to the specific business problem.

Off-the-shelf AI models can only go so far. The true bottleneck for enterprise adoption is "digitizing judgment"—capturing the unique, context-specific expertise of employees within that company. A document's meaning can change entirely from one company to another, requiring internal labeling.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.

A viral satirical tweet about deploying Microsoft Copilot highlights a common failure mode: companies purchase AI tools to signal innovation but neglect the essential change management, training, and use case development, resulting in near-zero actual usage or ROI.

AI tools compound in value as they learn your context. Spreading usage across many platforms creates shallow data profiles everywhere and deep ones nowhere. This limits the quality and personalization of the AI's output, yielding generic results.