Most integrators make a fundamental mistake: they take existing, inefficient business processes and try to speed them up with AI. We call this creating a "petrol horse" — accelerating an old system instead of building a new one.
The flawed approach sounds like this: "Let's teach your accountant to use ChatGPT" or "Let's create an AI copy of your manager." But efficiency and human nature are often contradictory. Job titles were invented artificially simply because, historically, there were no alternatives.
Humans have unresolvable flaws in a business context: poor memory, limited attention spans, and an absolute inability to truly multitask. The probability that an overloaded manager will independently choose the most optimal task to do next is close to zero.
Therefore, AI should not be implemented as a mere assistant. It must be implemented to completely take over the management of memory, attention, and multitasking across the entire organization.
Orchestration and the Pull-System
AI in business must act as an orchestrator and rhythm-setter — much like Henry Ford's assembly line. The employee no longer chooses what task to do. The architecture automatically "pulls" the necessary task and delivers it to either a human or an AI agent.
This entire architecture is built around a single, uncompromising objective function: fulfilling client obligations profitably. Anything that doesn't serve this metric is ruthlessly cut.
Modular Efficiency over Job Titles
We dismantle the outdated role-based model. Instead of relying on "titles," the business is decomposed into micro-modules with strict SLAs (Service Level Agreements for inputs and outputs).
It doesn't matter who executes the module — an AI agent or a human — as long as the module delivers the required result in the right format. This allows you to smoothly and painlessly replace human labor with AI agents wherever it makes economic sense, without breaking the company structure.
A Note on Hardware
Building local hardware (on-premise) is almost always a mistake today. The speed at which models and hardware become obsolete is colossal. It is far cheaper and more effective to use the cloud APIs of the world's best models, utilizing a lightweight architecture, rather than trying to build a static local fortress.