Current LLMs are intelligent enough for many tasks but fail because they lack access to complete context—emails, Slack messages, past data. The next step is building products that ingest this real-world context, making it available for the model to act upon.

AI's biggest enterprise impact isn't just automation but a complete replatforming of software. It enables a central "context engine" that understands all company data and processes, then generates dynamic user interfaces on demand. This architecture will eventually make many layers of the traditional enterprise software stack obsolete.

Even the most advanced AI is ineffective without business context. The CEO estimates 90% of crucial company knowledge—strategy, rationale, priorities—is undocumented and simply "floats in the air." This lack of structured, accessible context is a bigger barrier to AI adoption than the technology itself.

The primary bottleneck for many users isn't a model's raw intelligence but the user's ability to provide sufficient context. The next paradigm shift will be AIs that can autonomously enter a new environment (like a Slack channel), gather context, and figure out how to be useful, dramatically lowering the barrier to value.

The defensibility of AI-native software will shift from systems of record (what happened) to 'context graphs' that capture the institutional memory of *why* a decision was made. This reasoning, currently lost in human heads or Slack, will become the key competitive advantage for AI agents.

The primary barrier for enterprise AI is the 'context gap.' Models trained on public data have no understanding of your specific business—its metrics, language, or history. The key is building infrastructure to feed this proprietary context to the AI, not waiting for smarter models.

The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.

The primary barrier for useful AI agents is not the underlying model but the complex task of 'data wiring'—connecting to a user's real-world context like emails, local files, and support tickets. Products that solve this difficult integration challenge, where most agents currently fail, will gain a significant competitive advantage.

To get 10x results from AI, stop treating it like Google. Instead, treat it like an A-player new hire by "onboarding" it with your goals, constraints, and values. This deep context allows it to provide nuanced, strategic output instead of generic, one-off answers.