The Organizations Capturing AI’s Value Are Doing Something Different

In this edition of “Office Optional, Larry English” talks about why enterprise AI is the next competitive advantage, explaining how organizations can move beyond individual AI assistants to build coordinated systems of AI agents that transform how work gets done across the business.

According to a 2026 study, 20% of companies are capturing 74% of AI’s economic value. The difference isn’t access to better models or bigger budgets. Most organizations have access to the same technology.

What’s separating leaders from everyone else is how they use AI. Most companies are deploying AI to help employees work faster through copilots, chatbots and assistants. The leaders are redesigning how work gets done by scaling AI across the enterprise.

Instead of treating AI as a productivity tool, they’re building multi-agent systems that analyze information, coordinate activities and execute business processes across functions. The conversation is shifting from individual productivity to organizational capability, and that’s where the greatest long-term value will be created.

The Evolution From Personal AI To Enterprise AI

Most organizations follow a predictable path in their scaling AI journey. The starting point is personal assistance. Employees use AI to generate content, answer questions, summarize documents or assist with routine tasks. Employees remain the primary producers of work, with AI serving as a tool that accelerates execution. Productivity gains are real but modest at this stage, typically falling in the 10-25% range.

The next level of value comes from redesigning business processes. This is where agentic AI comes in. Rather than simply responding to prompts, agents take action. They can gather information, initiate workflows and complete multi-step processes. Employees are now reviewers, not just producers. Employees spend less time executing tasks and more time directing work, applying judgment and providing oversight. At this level, productivity gains jump into the 25-50% range.

The real leaps in productivity happen when organizations learn to run multi-agent systems: coordinated teams of AI doing long-running, non-linear work. Humans shift from reviewing work to directing it while coordinated agents handle much of the execution. Productivity gains can range from 1x to 5x, depending on the use case.

At the final level of AI maturity, agents operate largely autonomously within well-defined guardrails. This is the enterprise AI tier, with long-running agents, multi-agent system orchestration, governance, observability and integration with core business systems. Humans remain accountable while much of the execution becomes automated. At this stage, productivity gains of 10x or greater become possible.

One key insight in this framework is that in scaling AI, you never leave the lower levels behind. Employees will still use AI chat every day. The goal is to build the capability to operate at all five levels simultaneously and to know which level the work in front of you calls for.

How To Scale AI Across The Enterprise

Moving from personal AI to enterprise AI is significantly more challenging than most organizations expect.

Just because an AI agent works well on an employee’s desktop doesn’t mean it will work when deploying across the enterprise. Enterprise environments are messy, with incomplete data, varying inputs and lots of exceptions. Small errors can cascade into larger operational issues. A chatbot hallucinating a paragraph is inconvenient. An enterprise agent hallucinating an action inside a system of record becomes a business risk.

The complexity increases when organizations begin deploying teams of agents, or multi-agent systems, working together across workflows. At that point, success depends on strong governance, security, orchestration, observability and operational management.

The organizations succeeding at operating enterprise AI tend to focus on a few foundational processes.

1. Build auditability into every workflow.

When a human makes a decision, you generally know who made it and why. Enterprise AI requires the same level of accountability. Leaders need visibility into which actions were performed by humans, which were performed by agents and how decisions moved across a chain of interacting agents. Without an audit trail, governance becomes difficult and accountability becomes unclear.

2. Measure outcomes across workflows, not individual agents.

Most organizations track AI usage at the individual level. But when agents are working together across a workflow, the cost and value of the entire process is what matters. Leaders need visibility into where AI is creating results and where costs are outpacing benefits. The goal is operational visibility, not perfect ROI calculations.

3. Embed governance into the platform.

Policies and training can help employees use AI responsibly. But when agents are operating across workflows and business systems, governance must be built into the platform itself.

That may include restricting access to sensitive data, enforcing approval requirements for certain actions or applying controls around regulated information. The specific guardrails will vary by industry, but they should reflect the organization’s policies, risk appetite and compliance requirements.

Technology can enforce governance, but it cannot define it. Effective governance starts with clear organizational policies and oversight, then translates those decisions into technical controls.

4. Treat agents like high-value identities.

One of the most overlooked aspects of cybersecurity and enterprise AI is identity management. Employees should not be able to access information through an agent that they could not access directly. Agents require the same disciplined approach to permissions, monitoring and oversight that organizations apply to other sensitive digital identities.

The most effective model is to treat agents similarly to service accounts: Assign only the permissions necessary for their role, continuously monitor activity and maintain clear ownership and accountability.

5. Invest in infrastructure, not just agents.

The future of enterprise AI is teams of specialized agents working together. That requires real infrastructure, including workflow management, monitoring, logging, cost controls, security controls and integration with the systems your business already runs on.

Some organizations will prefer an integrated platform. Others will assemble the pieces and connect them. Either approach can work depending on the organization’s culture, technical maturity and existing investments.

What doesn’t work is treating agents as standalone tools and expecting them to deliver enterprise-scale results.

A Real-World Example Of Enterprise AI

Modernizing legacy systems has traditionally been expensive, time-consuming work requiring large teams of developers, architects and business analysts. AI is condensing that work.

For example, at my company, Centric Consulting, teams are using AI-assistant development and specialized agents to accelerate code analysis, documentation, testing and migration activities. We’ve been able to reduce the standard modernization timeline by as much as 80%.

That gain doesn’t come from developers working faster, but rather from orchestrating multiple AI capabilities across the modernization process. Coordinated agents analyze code, identify dependencies, generate documentation, create test cases and recommend modernization approaches while human experts make key decisions.

The Next Phase Of Enterprise AI

Successful pilots and successful enterprise deployments are very different things. Pilots succeed because experts guide them. Scale happens when organizations build systems that allow thousands of employees to achieve consistent results.

That’s why the next phase of AI will be less about models and more about systems. The companies that pull ahead won’t simply make employees more productive. They’ll redesign how work gets done across the enterprise. The result won’t just be higher productivity. It will be organizations that use enterprise AI to learn faster, adapt faster and execute faster than competitors.

This article was originally published on Forbes.com.