The Real Competitive Advantage in AI Is Knowing Your Business

By Guillaume Bacuvier

As AI models are becoming exponentially more capable, AI agents are taking on increasingly complex tasks, and businesses are investing heavily on the promise of greater efficiencies, productivity, and performance. Yet why are so many organizations still struggling to translate these advances into measurable business value?

The answer has less to do with AI than with how well agents understand the businesses they serve. In short, enterprise AI has a context problem, and solving it will be one of the defining competitive advantages over the next 12 to 18 months.

AI Ambition vs. Operational Reality

Research conducted by The Hackett Group in collaboration with ARIS, surveying more than 200 senior executives across the Global 2000, reveals a striking disconnect. While 86% of those surveyed recognize that AI agents need process context to operate effectively, only 22% report that agents have comprehensive, real-time visibility across end-to-end business processes.

Many companies are asking agents to execute work inside organizations without giving the agents the full picture of how the business operates. An agent might analyze information, recommend an action, or complete a task, but does it understand which systems are involved, who can authorize a decision, where exceptions occur, and what happens downstream? Without that knowledge, we risk simply automating complexity.

Intelligence Does Not Mean Understanding

Consider an agent resolving a customer complaint. The agent may have access to transaction histories and company policies, but resolving the complaint might require involving finance, operations, compliance, and customer service. The agent must understand how those activities connect, which rules take precedence, and when a human must intervene.

Data tells AI what exists, but process context tells it how work gets done and how work should get done. Without process context, an agent can perform well in a controlled pilot but fail when confronted with everyday operational reality. And companies are starting to wake up to this reality: At Legal & General Asset Management, more than 2,000 governed process models now provide trusted context for multiple agents running across the business.

Not All Context Is Created Equal

So how can companies provide this in-depth knowledge and understanding to agents, ensuring they act reliably and cost-effectively every time?

The answer lies in a digital twin—a virtual clone of the organization built on the processes unique to that business and continually updated with the execution data that happens around these processes.

Many companies fail to map out how those processes are designed and the triggers and relationship paths that link them, connecting departments, standard operating procedures, policies, regulatory requirements, and all the other detailed information that establishes operational DNA. It is also crucial to capture and describe tacit knowledge of how the company operates, often held by employees who have the experience and judgment to compensate for poorly described processes. At ARIS, we call this the Enterprise Model, a core part of our platform, constructed over more than three decades of working with the biggest businesses in the world.

The importance of this Enterprise Model is supported by Boston Consulting Group’s 10–20–70 framework, which recommends directing just 10% of AI transformation effort to algorithms, 20% to data and technology, and 70% to people, processes, and organizational change. In other words, most of the work required to create value happens outside the model itself.

Greater Autonomy Demands Greater Control

At the same time, as agents become more autonomous, the need for greater operational discipline also increases. Forty-six percent of surveyed organizations place AI accountability solely within IT, the Hackett Group research found. Yet agent decisions can affect financial controls, regulatory obligations, customer relationships, and operational risk.

The false choice is this: Either agents escalate every decision and deliver little value, or they act with too much freedom and create unacceptable risk. The answer is to build governance into the way work is designed, within the processes themselves, giving agents clear knowledge of permissions, escalation paths, and controls from the outset.

Why Context Changes the Economics of AI

Companies putting this approach into practice are already reaping the rewards. Organizations with stronger process context capabilities were five times more likely to report very successful AI outcomes, according to the Hackett Group study.

So the first question should not be how quickly can we deploy agents, but where can AI improve business outcomes? Ensuring end-to-end visibility of how work flows across an organization helps leaders identify bottlenecks, understand dependencies, and establish performance baselines. It also helps them avoid spending money automating tasks that do little to improve the top or bottom line.

The Next Competitive Advantage

Companies will increasingly be able to access intelligent AI models, but what they cannot buy off the shelf is a detailed understanding of how their business operates on a daily basis.

That understanding must be captured, governed, and kept current in the digital twin, giving agents a trusted foundation for execution and enabling business leaders to assess whether AI is actually improving results. At aerospace and defense company Leonardo, a digital twin built on more than 5,000 process models has laid the groundwork for agentic AI.

For CEOs, the priority is clear: Before asking how many AI agents to deploy, ask whether the agents will understand the business well enough to make a difference. The AI race is becoming an execution race, and while intelligence creates possibilities, business context is what turns that potential into performance.

Guillaume Bacuvier is CEO of ARIS.

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