In AI acquisitions, a startup's underlying technology is less important than its "workflow proximity." Atlassian's AI head advises buyers to assess how deeply a tool is integrated into a user's fundamental daily tasks. A tool central to a core workflow is far more valuable and defensible than a specialized, peripheral one.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

As AI agents take over task execution, the primary role of human knowledge workers evolves. Instead of being the "doers," humans become the "architects" who design, model, and orchestrate the workflows that both human and AI teammates follow. This places a premium on systems thinking and process design skills.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

While current AI tools focus on individual productivity (e.g., coding faster), the real breakthrough will come from systems that improve organizational productivity. The next wave of AI will focus on how large teams of humans and AI agents coordinate on complex projects, a fundamentally different challenge than simply making one person faster.

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

Early AI adoption by PMs is often a 'single-player' activity. The next step is a 'multiplayer' experience where the entire team operates from a shared AI knowledge base, which breaks down silos by automatically signaling dependencies and overlapping work.

Contrary to the belief that AI architecture is only for senior staff, Atlassian finds that "AI native" junior employees are often more effective. They are unburdened by old workflows and naturally think in terms of AI-powered systems. Senior staff can struggle with the required behavioral change, making junior hires a key vector for innovation.

Even powerful AI tools don't produce a final, polished product. This "last mile" problem creates an opportunity for humans who master AI tools and then refine, integrate, and complete the work. These "finisher" roles are indispensable as there is no single AI solution to rule them all.