When different departments push their own projects onto the sales team, reps get overloaded. To solve this, enablement leaders must shift the focus of every initiative away from departmental priorities and toward a shared customer outcome. This unified goal minimizes internal friction and clarifies what's truly important.

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.

AI coding agents thrive because developers have broad codebase access and work in a text-based medium. Enterprise knowledge work is stalled by fragmented data access, complex permissions, and multi-modal information (calls, meetings), which are significant hurdles for current AI.

By granting an AI agent read-access to all company data streams—Slack, Notion, Google Docs, email—you can create a centralized oracle. This agent can answer any question about project status or client communication, instantly removing communication friction and breaking down departmental silos.

Instead of siloing roles, encourage engineers to design and designers to code. This cross-functional approach breaks down artificial barriers and helps the entire team think more holistically about the end-to-end user experience, as a real user does not see these internal divisions.

Simply giving AI tools to existing departments like legal or finance yields limited productivity gains. The real unlock is to reimagine and optimize end-to-end, cross-functional processes (e.g., 'onboarding a new supplier'). This requires shifting accountability from departmental silos to process owners who can apply AI holistically.

Companies with an "open by default" information culture, where documents are accessible unless explicitly restricted, have a significant head start in deploying effective AI. This transparency provides a rich, interconnected knowledge base that AI agents can leverage immediately, unlike in siloed organizations where information access is a major bottleneck.

The best products are built when engineering, product, and design have overlapping responsibilities. This intentional blurring of roles and 'stepping on each other's toes in a good way' fosters holistic product thinking and avoids the fragmented execution common in siloed organizations.

With AI, codebases become queryable knowledge bases for everyone, not just engineers. Granting broad, read-only access to systems like GitHub from day one allows new hires in any role (product, design, data) to use AI to get context and onboard dramatically faster.