Most current AI tools for sales are general large language models with a thin layer of data on top. The real productivity leap will come from future tools where deep, domain-specific knowledge—like complex enterprise sales methodologies—is embedded from the ground up.

Founders often mistakenly market "AI" as the core offering. Customers don't buy AI; they buy solutions to their long-standing problems (e.g., more leads, better service). Frame your product around the problem it solves, using AI as the powerful new tool in your solution space that makes it possible.

Previously, buyers considered only 2-3 vendors. AI tools now allow them to easily evaluate up to 10, meaning your competitive landscape has expanded. Sales teams must use these same AI tools to research who is being surfaced alongside them and adjust their competitive positioning accordingly.

HubSpot discovered AI search positioned its Service Hub as a 'CRM add-on,' not a standalone leader. This revealed a crucial gap between their internal messaging and market consensus. AI search acts as an unfiltered mirror, exposing critical positioning problems that need to be addressed.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

Feed AI your detailed persona research and data on your top competitors. Then, ask it to identify key persona pain points and values that competitors' positioning fails to address. This process systematically uncovers arbitrage opportunities for differentiated messaging.

AI models are becoming commodities; the real, defensible value lies in proprietary data and user context. The correct strategy is for companies to use LLMs to enhance their existing business and data, rather than selling their valuable context to model providers for pennies on the dollar.

To get meaningful competitive analysis from an AI, first provide your business and product strategy. Then, have the AI define the competitive set. Only after you agree with the landscape should you define specific comparison criteria. This iterative, context-first approach yields much better results than asking for a feature comparison directly.

If a company and its competitor both ask a generic LLM for strategy, they'll get the same answer, erasing any edge. The only way to generate unique, defensible strategies is by building evolving models trained on a company's own private data.