Beneath the surface, sales 'opportunities,' support 'tickets,' and dev 'issues' are all just forms of work management. The core insight is that a single, canonical knowledge graph representing 'work,' 'identity,' and 'parts' can unify these departmental silos, which first-generation SaaS never did.

The audience for marketing content is expanding to include AI agents. Websites, for example, will need to be optimized not just for human users but also for AI crawlers that surface information in answer engines. This requires a fundamental shift in how marketers think about content structure and metadata.

Before implementing AI, organizations must first build a unified data platform. Many companies have multiple, inconsistent "data lakes" and lack basic definitions for concepts like "customer" or "transaction." Without this foundational data consolidation, any attempt to derive insights with AI is doomed to fail due to semantic mismatches.

Generative AI has neutralized content volume as a competitive advantage. In fact, inconsistent messaging across many assets can penalize a brand in AI models. This reverses the old SEO playbook, making it critical to focus on fewer, higher-quality pieces with deep expertise and a consistent narrative across all channels.

Instead of only planning future content, systematically tag every published piece with its topic, performance metrics, and the pain point it addresses. This creates a data-rich, reusable library that allows you to identify and remix your most successful content ideas.

Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.

Optimizing for AI is not a task for a single team. It requires a holistic, coordinated effort across brand, content, lead gen, and ABM teams to ensure all content is consumable by LLMs in a consistent and desirable way, preventing misinterpretation of the brand's narrative.

For AI models to reference your brand, content must be structured in a machine-readable format like JSON. Traditional SEO is insufficient; marketers now need technical skills to ensure content is accessible and prioritized by AI, a fundamental change in growth strategy.

When organizing your content library, add a specific category for the customer 'pain point' each asset addresses. This allows you to analyze performance based on the problems you're solving for your audience, revealing deeper insights than merely tracking topic popularity.