AI in Business: A Necessary Deep Transformation

AI in Business: A Necessary Deep Transformation

The integration of artificial intelligence (AI) in businesses is often seen as a mere addition of features to improve efficiency. However, this approach remains largely superficial. To truly transform operations, it is crucial to develop systems that not only automate tasks but also optimize decisions through close collaboration between AI agents and humans.

In most cases, the adoption of AI in business follows a predictable pattern: a repetitive process is identified, a chatbot or co-pilot is integrated, and the time saved is measured. While this may seem like progress, it does not constitute a deep transformation. Two years after the rise of AI, it is clear that these tools have improved the efficiency of certain individual tasks but have not fundamentally changed the way work is organized.

Leaders must now ask the crucial question: what does an organization truly designed for AI look like, and are we building this new approach or simply adding layers to an already outdated model?

The Trap of Fragmented Initiatives

Under pressure to demonstrate rapid advancements, many companies have multiplied AI initiatives without creating a coherent architecture. This has led to a disparate set of tools that do not share the same context or information and do not reinforce each other. The result: a lack of sustainable value.

The problem does not lie in the technology itself, but in the foundations on which it is built. An AI grafted onto existing systems, often referred to as "legacy," and disconnected from corporate policies, approval processes, and business logic, may seem promising during demonstrations, but it often fails to deliver in production.

Without a unified operational context, AI cannot reason effectively about the organization's activities. It can only work from data that is similar to it.

From Record Systems to Outcome Systems

Historically, enterprise software has been built on the "system of record" model, designed to record, store, and retrieve information. This model has served businesses well, but it is now outdated.

We are now witnessing the emergence of a new paradigm: "systems of outcomes." Rather than simply retaining data, enterprise applications must now be proactive:

• Coordinate activities across multiple functions
• Detect problems before they arise
• Simulate different scenarios in real-time
• Continuously advance processes, even in the absence of collaborators

This is not just about improving a chatbot's memory, but rethinking the architecture to include teams of specialized AI agents, each with a specific role to achieve a common goal. For example, in a negotiation with suppliers, one agent could prepare requests for quotes, another could compare offers, and a third could formulate recommendations. All work towards a defined objective, such as reducing costs or shortening procurement timelines.

These agents do not merely execute tasks; they reason based on the outcomes to be achieved.

Rethinking the Role of Human Oversight

Agentic AI raises crucial organizational questions. What level of autonomy are companies willing to grant to AI, and for what types of decisions?

The answer is not simple. Some processes will still require human validation, especially when risks are high or human interactions are essential. Others may be largely autonomous, with human intervention only in exceptional cases. As trust in AI systems grows, the scope of autonomy may expand.

In practice, this changes the nature of work. Take the example of a chief nurse managing hundreds of staff. Currently, a significant portion of their time is spent managing schedules and regulatory constraints. An AI system could analyze these parameters simultaneously, propose optimal scenarios, and allow the chief nurse to validate the final decision.

Human expertise is not replaced, but the cognitive load is alleviated.

The True Competitive Advantage

In the race for AI adoption, speed may seem to be the primary indicator of success. However, the companies that will succeed are not those that deploy AI the fastest, but those that build the right foundations.

A sustainable competitive advantage relies on the richness of the context available to AI. An AI that understands not only general patterns but also internal policies, approval processes, risk thresholds, and the business logic of the company. This context transforms a generic recommendation into a relevant and directly actionable one.

This is what distinguishes companies that have merely adopted AI from those that have profoundly transformed their operations. All companies will evolve in this direction, but the question is when they will commit to it and what lead they will leave for those who started earlier.