Early AI adoption focuses on productivity (e.g., writing copy faster). The next stage of maturity is using AI to directly impact revenue. For example, Canva uses AI to create and test 20% more ad variations, leading to more engaging, higher-converting campaigns that drive business results.

Instead of static documents, companies can embed their strategy into an AI agent. This agent assists in planning, identifies cross-departmental conflicts, and can be queried in real-time during decision-making to ensure constant alignment, making strategy a dynamic part of daily operations.

While Canva had been researching AI for years, a specific internal technical breakthrough became the catalyst for the company to "go all in." This single event prompted a rapid re-organization, pulling hundreds of people onto a centralized AI team to commercialize the new capability.

Instead of being a monolithic model, Canva's AI works by orchestrating its entire suite of existing, specialized features like background remover. A single user prompt can trigger multiple tools in sequence to generate a complex, layered design, leveraging years of product development.

Generic AI creates content without context. In contrast, 'Brand-Aware AI' functions like a strategic coach that understands your brand's rules and learns from performance data. It shifts from just generating content to actively recommending improvements based on what resonates.

Canva views its AI as the third evolution of design interfaces. The first was pixel-based (e.g., Photoshop), the second was object-based (classic Canva), and the new era is concept-based, where users describe an idea and the AI generates an editable first draft.

Vercel builds internal AI agents and tools, like an Open Graph image generator, to automate tasks that were previously bottlenecks. This not only increases efficiency but also serves as a critical dogfooding process, allowing them to innovate on their core platform by building the tools their own teams need.

Instead of adopting AI as a simple tooling exercise, identify where decision-making is slow or fragmented. For instance, during planning, AI can synthesize inputs and draft reports. This elevates product teams from low-value "busy work" to high-value strategic debate and tradeoff analysis.

While generating products with AI is popular, a massive unlock lies in applying it to unseen internal processes. AI can optimize workflows, improve content design, and perform analysis. These non-product applications can create significant leverage for design teams within larger organizations.