Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.

AI's biggest enterprise impact isn't just automation but a complete replatforming of software. It enables a central "context engine" that understands all company data and processes, then generates dynamic user interfaces on demand. This architecture will eventually make many layers of the traditional enterprise software stack obsolete.

AI is fundamentally changing SaaS interaction. Instead of users clicking buttons to take action, AI will perform the tasks. The UI will then transform into a surface where users primarily review AI-driven outcomes, get insights, and make corrections, often interacting via conversational language.

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.

Cognizant frames AI adoption across three maturing vectors: 1) Hyper-productivity for automating tasks, 2) Industrializing AI by embedding it in core workflows, and 3) Re-identifying the Enterprise, where AI agents become collaborative partners for complex, cross-functional work.

AI isn't a technology to be applied to existing processes. It's a foundational layer, like an operating system, that fundamentally reshapes how businesses create value, make decisions, and operate. This perspective forces a complete rethink of strategy, not just an upgrade.

The Goldman Sachs CEO differentiates between two types of AI adoption. Giving employees AI tools to make them more productive is relatively easy. The much harder, yet more impactful, challenge is fundamentally re-engineering long-standing, complex processes like customer onboarding from the ground up.

Simply adding an AI layer on top of a traditional SaaS stack will fail. A true AI-native architecture requires an "AI data layer" sitting next to the "AI application layer," both controlled by ML engineers who need to constantly tune data ingestion and processing without dependencies on the core tech team.

The true power of AI is unlocked by adopting an "AI First" approach. This means completely redesigning workflows with AI at the core, rather than simply using AI to accelerate existing processes. This shifts employees' roles from performing tasks to managing the AI agents that do the work.