In the contemporary discourse surrounding AI integration within organizations, it has become common to describe AI agents as analogous to new employees. This metaphor, while accessible, and beyond its slightly dystopian nature, fails to capture the nuanced and transformative nature of AI's role in modern enterprises, particularly with regard to operational flexibility and cost management.
Human employees possess specific skill sets, earn regular compensation, and contribute to organizational objectives through relatively stable roles. Hiring decisions are deliberate, and capacity is inherently constrained, as skill development is gradual and resource-intensive. Transposing this employment model directly onto AI creates a conceptual mismatch.
AI introduces a fundamentally different dynamic. Unlike human labor, AI capabilities can be rapidly reconfigured and scaled. The production landscape evolves continuously: a process automated today exposes new inefficiencies tomorrow. This phenomenon mirrors a well-established management practice, wherein leaders routinely identify and address the organization’s slowest workflows to incrementally accelerate overall performance. AI amplifies this effect dramatically, rendering static workforce paradigms insufficient.
Consequently, the notion of purchasing "digital workers" (AI agents) warrants critical examination. What exactly is being procured? How should utilization and costs be managed in this fluid environment?
Consider the example of an AI agent designed to automate customer onboarding. Initially, this agent delivers substantial value by alleviating bottlenecks in client acquisition. However, once onboarding is optimized, the challenge shifts to customer retention and support. Overcommitting financial resources to the now-resolved onboarding phase introduces inefficiencies. Furthermore, if discrete software solutions are responsible for automating each workflow in isolation, reallocating the budget dynamically becomes complex and restrictive. Agility requires that organizations retain the capacity to shift computational investments responsively.
Thus, conceptualizing AI as modular software components powered by shared computational infrastructure—rather than as independent, quasi-human agents—is far more strategic. A compelling analogy can be drawn from modern agriculture and its historical evolution.
Before tractors and PTOs transformed farming, agriculture relied on large numbers of workers and relatively simple tools to perform labor-intensive tasks. This model was limiting, as scaling productivity required adding more human labor. The introduction of tractors fundamentally changed this. By providing centralized power through the Power Take-Off (PTO), tractors enabled the development of increasingly sophisticated machinery. These advanced machines could perform highly specialized tasks with great efficiency, automating processes that once required many workers, all that by “just” plugging them to the PTO.
Software is now undergoing a similar transformation. What we once regarded as 'simple tools'—independent software solutions with limited automation—are evolving. The introduction of central AI models, like large language models (LLMs) acting as the PTO, allows for the creation of modern software machinery that brings unprecedented levels of automation and intelligence. Crucially, just as agricultural implements do not need to replicate the tractor's engine, software tools do not need to embed their own AI engines. Doing so would only lead to unnecessary expense and increased maintenance complexity. Instead, they should tap into the shared computational power of centralized AI, ensuring efficiency, cost-effectiveness, and flexibility.
This architecture offers significant advantages: it decouples intelligence from individual software products, facilitates flexible allocation of resources, and simplifies procurement strategies. Embedding bespoke AI into each application would, conversely, lead to fragmentation and inefficiency.
Although this new metaphor may lack the immediate allure of utopian narratives featuring autonomous digital employees, it offers pragmatic benefits. Modern software must function as precision tools, orchestrated intelligently and fueled by a shared reservoir of AI power. Strategic control over this computational substrate ensures that organizations can direct AI resources to areas of highest impact as priorities shift.
Here we see a machine burning bad seeds with laser lights.
At Uxopian Software, we have embraced this paradigm. Our recent internal demonstration of a comparison feature designed to seamlessly integrate with existing AI engines exemplifies our commitment to this modular and scalable approach. All our AI capabilities will be thought to be “plugged” into the customer's source of intelligence choice.
To conclude, it is critical to summarize the key takeaways of this paradigm shift:
By adopting this modular and centralized approach, organizations can maintain flexibility, reduce costs, and ensure they are prepared to thrive in an environment where AI and software continue to evolve at a rapid pace.
💬 Author’s personal note: I have always been impressed by the inventiveness my father has had building by himself new machines that would leverage the PTO, to do all sorts of stuff you would not believe. Let’s look for the creative guys of the LLMs area :)