Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

There are two product philosophies: user-centric, purpose-built tools (like Asana) and system-centric, universal tools (like Notion). Purpose-built apps are easier to start with but inevitably add features and concepts, becoming bloated. Universal apps, built on a few core concepts, are harder to learn but scale infinitely without breaking their core model.

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.

Modern AI models are powerful but lack context about an individual's specific work, which is fragmented across apps like Slack, Google Docs, and Salesforce. Dropbox Dash aims to solve this by acting as a universal context layer and search engine, connecting AI to all of a user's information to answer specific, personal work-related questions.

The core problem for many small and mid-market businesses isn't a lack of software, but an excess of it, using 7 to 25 different apps. This creates massive data fragmentation. The crucial first step isn't buying more tools, but unifying existing data into a single customer profile to enable smarter, automated marketing.

Vector search excels at semantic meaning but fails on precise keywords like product SKUs. Effective enterprise search requires a hybrid system combining the strengths of lexical search (e.g., BM25) for keywords and vector search for concepts to serve all user needs accurately.

Before diving into SQL, analysts can use enterprise AI search (like Notion AI) to query internal documents, PRDs, and Slack messages. This rapidly generates context and hypotheses about metric changes, replacing hours of manual digging and leading to better, faster analysis.

The company's key innovation is Humane One, an AI operating system for enterprises. It replaces the fragmented, icon-based world of separate apps for HR, finance, etc. with a unified system. The biggest implementation challenge is not the technology, but shifting the organization's culture and mindset.

For tools like Harvey AI, the primary technical challenge is connecting all necessary context for a lawyer's task—emails, private documents, case law—before even considering model customization. The data plumbing is paramount and precedes personalization.