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AI's enterprise role is twofold. It will be embedded as a feature within systems like Salesforce to optimize specific tasks. Concurrently, it will operate as a top-level abstraction layer, pulling data from multiple systems (Salesforce, Workday, email) to generate novel, cross-functional insights.
Work will bifurcate into two modes: delegating tasks to asynchronous agents (e.g., in Slack) and performing core work inside AI-native environments like Codex. These platforms will become the primary operating system where you run other apps, rather than AI being just a feature within apps.
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.
Salesforce is navigating the AI transition by championing a hybrid model of "apps and agents." This strategy positions its traditional software ("apps" for humans) as the foundation, which is now extended and made more powerful by AI ("agents"). This narrative preserves the value of their core offerings while embracing AI's productivity gains.
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.
SAP’s CTO views AI not as a feature but a fundamental architectural shift akin to the cloud transition. It requires re-engineering software at three levels: creating dynamic 'Generative UIs', automating 'Business Processes' with agents, and building a unified 'Data Layer' to power intelligence.
Becoming an "agentic enterprise" requires a foundational shift to an AI-first, conversational way of working. It involves augmenting every employee's workflow with AI assistance for faster decisions, all built upon a foundation of trusted, accessible data that powers the entire system.
The race in enterprise AI isn't just about agent capabilities, but about owning the central dashboard where employees direct agents across all applications (Salesforce, Jira, etc.). Companies like OpenAI and Microsoft are vying to become this primary interface, controlling the customer relationship and relegating other apps to the background.
Legacy systems like CRMs will lose their central role. A new, dynamic 'agent layer' will sit above them, interpreting user intent and executing tasks across multiple tools. This layer, which collapses the distance between intent and action, will become the primary place where work gets done.
The next major leap for AI is its ability to connect disparate apps and data sources (email, calendar, location) to take autonomous actions. This will move AI from a Q&A tool to a proactive agent that seamlessly manages complex workflows.
The proliferation of SaaS tools forces thousands of employees to act as manual "human glue," moving data and connecting workflows between systems. The key value of AI agents is creating an intelligent layer to automate this mundane, connective work, freeing up employees for higher-value tasks.