The strongest defense isn't a single killer app but a suite of a dozen deeply integrated products serving the same customer. This creates immense stickiness and cross-selling opportunities. AI dramatically reduces the time and effort required to build out such a multi-product surface area.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

The most successful organizations will view AI not as a tool for cost-cutting (doing the same with less) but as an expansionary technology. This mindset focuses on using AI to create new products, enter new markets, and dramatically increase scope, rather than just incremental efficiency gains.

Atlassian's CEO argues that AI tools should not just focus on novel capabilities. They must also improve users' current processes (e.g., AI-assisted writing). This dual approach brings the existing user base along while simultaneously showing them new, transformative ways to work, ensuring broader and faster adoption.

Treat AI initiatives as two separate strategic pillars. Create one roadmap focused on internal efficiency gains and cost reduction (productivity). Maintain a separate roadmap for developing new, revenue-generating customer experiences (growth). This prevents conflating internal tools with external products.

The dominant long-term strategy isn't using AI to do the same work with fewer people (Efficiency AI). Winning companies will leverage AI to create new products, services, and capabilities, massively expanding their output and market presence (Opportunity AI).

An effective AI strategy requires a bifurcated plan. Product leaders must create one roadmap for leveraging AI internally to improve tools and efficiency, and a separate one for external, customer-facing products that drive growth. This dual-track approach is a new strategic imperative.

In the AI era, a narrow, deep product is easily replicated. Choi argues for building breadth across an entire workflow. While a single feature can be "vibe-coded" by an LLM, replicating an interconnected system with multiple integrations and steps creates a much stronger competitive moat.

Leveraging AI requires a dual focus. Leaders must apply AI to solve genuine customer problems, not just for the sake of technology. Simultaneously, they must upskill their teams and re-engineer internal development processes to reduce handoffs and accelerate the entire product cycle.