Artificial intelligence inside large organisations is shifting from experimentation to execution. For years, enterprise AI largely meant assistive tools that helped employees draft content, answer questions, or automate isolated tasks. Now, a growing number of major companies are testing AI agents designed to operate directly inside real business workflows.
Several large enterprises across finance, insurance, mobility, and life sciences have begun piloting AI agents that don’t just assist work but actively carry it out within corporate systems. The shift signals a move toward AI playing a more operational role rather than remaining a productivity add-on.
Moving beyond assistive AI
The new wave of enterprise AI focuses on agents that can understand context, interact with multiple systems, and execute tasks under defined constraints. Unlike traditional AI tools that respond to individual prompts, these agents are designed to function within the broader structure of an organisation.
Instead of handling one-off requests, agents are expected to work continuously within workflows, adapting to feedback and following established rules. This change reflects a growing belief among enterprises that real value comes from AI that can operate inside existing processes, not just generate outputs for humans to act on.
A platform designed for AI coworkers
To support this shift, a new enterprise-focused platform has been introduced to help companies build, deploy, and manage AI agents at scale. The platform is positioned around the idea of “AI coworkers” — software agents that have access to shared business context, defined permissions, and structured onboarding similar to human employees.
The goal is to make AI agents aware of how work actually happens inside an organisation. This includes understanding data flows, system dependencies, and operational boundaries. Built-in tools for security, auditing, and performance evaluation aim to give enterprises visibility into what agents are doing and how reliably they are behaving.
Early enterprise adopters
A number of well-known companies are already experimenting with this approach. Early adopters span industries with complex operational and regulatory environments, including financial services, insurance, mobility, and life sciences.
Organisations such as Intuit, Uber, State Farm, Thermo Fisher Scientific, HP, and Oracle are among those publicly linked to early trials. Additional large-scale pilots are reportedly underway at companies in telecommunications, networking, and international banking.
The diversity of these early users suggests that interest in AI agents is not limited to a single vertical. Instead, enterprises across sectors appear to be exploring how agent-based systems might fit into their core operations rather than remaining isolated experiments.
Executive perspectives on the shift
Comments from enterprise leaders point to a shared theme: AI is moving from being helpful to being responsible for execution. Executives have described this transition as a shift from “tools that assist” to “agents that act,” with an emphasis on reducing friction and expanding what teams can accomplish.
From the platform provider’s perspective, the challenge is no longer raw model capability. The harder problem is governance: ensuring agents have the right context, operate within clear boundaries, and can be monitored at scale. Integration and control, not intelligence alone, are becoming the defining factors for enterprise adoption.
Why agents matter for enterprise operations
Most enterprise AI deployments to date have focused on narrow use cases like document summarisation, ticket classification, or content generation. While useful, these applications rarely connect directly to the systems that run business processes.
AI agents are intended to bridge that gap. In theory, an agent can pull information from multiple internal systems, reason about next steps, and take action — whether that means updating records, triggering workflows, or coordinating across tools.
This represents a shift in value creation. Instead of saving time on individual tasks, AI agents aim to take responsibility for parts of the workflow itself, operating under predefined rules and oversight.
Practical constraints on real adoption
Deploying AI agents inside large organisations comes with real-world constraints. Enterprises have strict requirements around data access, compliance, and human oversight. Agents must respect permission structures, log their actions, and remain auditable.
Integrating systems like CRM platforms, ERP tools, data warehouses, and support systems has long been a challenge in enterprise IT. AI agents promise to act as connective tissue between these systems, but whether they can do so reliably will depend on how well companies manage governance, monitoring, and long-term maintenance.
The fact that large, regulated organisations are running serious trials suggests they see enough potential to justify the effort.
What this shift could lead to
If these early experiments prove successful, enterprise AI could evolve rapidly. Rather than generating recommendations for humans to execute, AI agents may begin carrying out work directly, with people overseeing outcomes rather than performing every step.
This shift is also likely to change organisational roles. In addition to AI engineers and data scientists, companies may need specialists focused on agent governance, performance management, and operational oversight.
AI agents are unlikely to replace human teams, but they may become a standard part of how work gets done inside large organisations. These early trials represent a visible step toward that future.
