The new generation of AI automates workflows, acting as "teammates" for employees. This creates entirely new, greenfield markets focused on productivity gains for every individual, representing a TAM potentially 10x larger than the previous SaaS era, which focused on replacing existing systems of record.

Asana CEO Dan Rogers believes the enterprise AI battle won't be won by the smartest model. Instead, value is created by integrating AI into workflows to handle tedious coordination tasks like updates and research. This frees up human employees to focus on strategic work like directing and approving.

The effectiveness of enterprise AI agents is limited not by data access, but by the absence of context for *why* decisions were made. 'Context graphs' aim to solve this by capturing 'decision traces'—exceptions, precedents, and overrides that currently live in Slack threads and employee's heads, creating a true source of truth for automation.

The shift from chatbots to agents represents a jump up the 'use case ladder.' Simple chat focuses on individual generation (drafting emails). In contrast, agents tackle systems-level work like workflow automation and process monitoring, moving AI's value from personal productivity to impacting entire business systems.

Complex platforms have vast functionality that most human users never discover. AI agents can act as perfect power users, programmatically accessing the full suite of features to solve problems. This unlocks the "trapped value" of the software, increasing its utility and solidifying its role as a system of record.

According to AWS's VP of Agentic AI, the primary struggle for enterprises is that critical context is siloed in 'walled gardens' like Outlook, Slack, and other SaaS tools. The most valuable function of AI agents is not just task automation, but their ability to work across these applications to gather and synthesize context, bridging the gaps.

An AI agent that only automates a small, horizontal slice of a business process is "virtually useless." To deliver real business outcomes, the agent must be capable of handling the entire end-to-end workflow, from initial contact to final revenue generation.

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.

The most significant value from AI is not in automating existing tasks, but in performing work that was previously too costly or complex for an organization to attempt. This creates entirely new capabilities, like analyzing every single purchase order for hidden patterns, thereby unlocking new enterprise value.