Escrito por: Maisa Publicado: 09/07/2025
Most large enterprises run on software that’s decades old. These legacy systems handle everything from customer transactions to regulatory compliance, the critical stuff that keeps businesses running.
Yet here we are, in an era where AI promises to transform how we work, and there’s a fundamental disconnect: How can we leverage AI when our core systems are hard to maintain, upgrade, or even understand?
Legacy systems are the critical software and infrastructure that most enterprises still rely on for their core operations. They’re outdated and hard to maintain, but they handle everything from payment processing to inventory management.
These systems persist because they’re deeply rooted in how companies operate. Years of business logic, custom workflows, and partner integrations have been built around them. The entire business ecosystem has evolved to fit these platforms.
Changing them carries enormous risk. Beyond the massive costs, there’s the threat of disrupting operations that can’t afford to fail. Lost transactions, compliance violations, recertification requirements, and critical edge cases make replacement a nightmare scenario.
So these systems remain, running the business while technology moves forward around them. The systems that make transformation necessary are the same ones that make it difficult.
If we can’t replace legacy systems, we need to find ways to make them work better. That’s where automation comes in.
RPA (Robotic Process Automation) uses bots that mimic human actions: clicking buttons, filling forms, copying data between screens. Since RPA works at the interface level, it doesn’t care how old the underlying system is. If a human can use it, a bot can automate it.
This has transformed how enterprises handle repetitive, rules-based work. Invoice processing, data entry, report generation. Repetitive actiond that once consumed thousands of hours now run automatically.
But RPA has clear limits. It’s fragile: change a screen layout and the bot breaks. It’s rigid: bots follow scripts exactly, unable to handle exceptions or make decisions. They can’t understand context or adapt to new situations.
What happens when we don’t just want to automate clicks? What if we want systems to understand, decide, or adapt?
What if we want to add intelligence to legacy platforms? This becomes possible when systems expose APIs, opening a path to use Model Context Protocol
When APIs exist, MCP (Model Context Protocol) provides a standardized way for AI agents to interact with these legacy systems. It acts as a bridge, letting AI pull data from old databases, trigger actions, and integrate decades-old infrastructure into intelligent workflows. AI can read from your inventory system, analyze patterns, and make recommendations while working within existing architecture.
But the limits show up fast. AI models can misinterpret data or misuse tools, leading to costly errors. Each interaction requires prompting, and costs add up quickly when dealing with complex workflows. Reliability becomes a constant concern when agents don’t always behave as expected.
These challenges compound. Projects become expensive to design, test, and control. Teams need to build guardrails, monitoring systems, and fallback procedures. That’s why fewer than 6% of companies rely on AI for more than basic tasks like simple Q&A bots.
Today’s AI can reason through complex problems but struggles with reliable execution. It’s smart but unpredictable, which is exactly what enterprises can’t afford when working with critical systems.
What’s needed is computational determinism: the ability to trust that actions will execute precisely, every time. Systems that can integrate with legacy tools whether through APIs or UI, but with the consistency of traditional code.
That’s the idea behind our AI Computer, the KPU. It combines AI’s reasoning capabilities with the reliability of deterministic execution. The KPU executes workflows step-by-step using actual code, not probabilistic responses. It can interact with legacy systems through APIs or at UI level in browser based software.
The result is intelligence plus control. AI that can understand context and make decisions, but executes with the precision enterprises require. End-to-end automation becomes possible, even across legacy tools.