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While many AI tools focus on answering questions, the real value comes from instructing systems to perform complex, multi-step tasks. For example, automatically onboarding a new employee requires orchestrating actions across 15 different applications, which is where platforms need to excel.
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In the AI era, enterprises reject the fragmented, best-of-breed SaaS model. They prefer a single AI platform that handles entire workflows across departments. This avoids data silos and streamlines compliance, making end-to-end automation the key value proposition.
According to Okta's CEO, the most valuable application for AI agents in the enterprise will be orchestrating complex processes that span multiple software silos (e.g., Salesforce, SAP, Content Management). This is a task that has historically been difficult to automate with packaged software and required human intervention, representing a massive new opportunity.
Most users only scratch the surface of complex enterprise software. AI agents will bridge this gap by interpreting natural language requests and executing complex tasks on the user's behalf. This transforms the user experience from learning features to simply stating goals, unlocking decades of untapped capabilities.
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.
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 giving AI tools to existing departments like legal or finance yields limited productivity gains. The real unlock is to reimagine and optimize end-to-end, cross-functional processes (e.g., 'onboarding a new supplier'). This requires shifting accountability from departmental silos to process owners who can apply AI holistically.
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.
While AI tools will become simpler, the core skill for leveraging them is the ability to think in systems and workflows. People who can break down a business process into logical, step-by-step instructions for an agent to follow will have a significant advantage in the age of AI automation.
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.
