Artificial Intelligence for Obsolete Business Systems: The Revolution of 2025

Imagine that you have a company that still uses an old accounting system from the 1990s, fully functional but impossible to connect to modern technology. Now imagine being able to make this system communicate with state-of-the-art artificial intelligence without having to throw away 30 years of data and established procedures. This is exactly what is happening in 2025 thanks to intelligent linkage systems.

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While everyone is talking about ChatGPT and the latest innovations in artificial intelligence, the real business revolution is happening behind the scenes. Companies are discovering how to integrate AI into their existing systems without having to completely revolutionize their IT infrastructure.

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Index

What are Intelligent Connection Systems

An intelligent linking system is like a universal translator between the old and new technological worlds. Think of when you travel abroad and use a translation app to communicate-the smart linking system does the same thing, but between your old business software and modern artificial intelligence technologies.

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According to Mira Patel, chief technology officer of Nexus Operations, "The question is no longer 'Can we use artificial intelligence?' but rather 'How do we integrate AI into our daily operations without screwing up the whole system?'"

How They Work in Practice

Imagine these concrete scenarios:

Example 1: The Intelligent WarehouseYour company has a 2008 warehouse management system. The intelligent linkage system "teaches" it AI to predict when it will run out of stock, simply by reading the data that already exists. The warehouse worker continues to work as usual, but now the system automatically tells him when to order new products.

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Example 2: The Accounting AssistantYour2010 billing software is enhanced with AI that automatically recognizes anomalies in invoices. The AI "reads" invoices as an accounting expert would and flags suspicious ones, but all through the software you already know.

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Example 3: Enhanced Customer ServiceYourold telephone switchboard is connected to an AI that analyzes customers' tone of voice and suggests to your operator how best to handle the call, all in real time.

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Digital transformation decisions: Legacy or leap? Find your strategic path forward

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A Strongly Growing Market

The 2025 numbers are impressive: investment in intelligent linkage systems grew 142% in one year, even surpassing investment in new artificial intelligence applications.

Why This Growth?

The explanation is simple: 80 percent of large companies still use "old" computer systems that work perfectly well but cannot communicate with modern technologies. Replacing them would cost millions and months of disruption.

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Numbers that Matter:

5.4 billion: Market value in 2024
34.2 billion: Forecast for 2032
70% of business systems: Will be upgraded with AI by 2028

This means that every day more and more companies are choosing to "green up" their existing systems rather than replace them completely.

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Digital Translators: A New Profession

A new category of experts has emerged: computer systems translators. These are specialized companies that know how to make systems born in different eras talk.

The Three Types of Specialists

1. Language ConvertersCompaniessuch as RetroAI specialize in translating old programming codes (such as COBOL from the 1980s) into modern languages that AI can understand.

Practical example: A public agency's pension system written in COBOL in 1985 is "translated" into modern language, retaining all functions but making it compatible with artificial intelligence.

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2. Communication OrchestratorsCompaniessuch as Harmony Tech develop solutions that coordinate AI processing across multiple business systems, ensuring that all automated decisions are consistent.

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Practical example: In a hospital, the AI that manages appointments automatically communicates with the AI that manages medical supplies and the AI that schedules staff shifts.

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3. Guardians of ComplianceCompaniessuch as GuardRail ensure that all connections to IA comply with industry regulations automatically.

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Practical example: At the bank, whenever the AI makes a decision on a loan, the system automatically verifies that it complies with all privacy and anti-money laundering regulations.

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Concrete Examples of Success

Case Study 1: Manufacturing Industry - Westbrook Industries

The Situation: Westbrook had a 15-year-old warehouse management system that worked well but could not foresee the problems.

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The Solution: They installed an intelligent linkage system that "taught" the AI to read the warehouse data.

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The Result: In 6 months they saved 28 million by predicting supply chain disruptions weeks in advance.

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"The best AI implementation is one that your employees don't even notice," says James Chen, Westbrook's chief information technology officer. "Our warehouse workers use the same system they always have, but now they always know what to order and when."

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Case Study 2: Banking Services - Fidelity Financial

The Situation: A payment processing system from the 2000s that processed thousands of transactions per day but could not automatically identify fraud.

The Solution: Connecting with AI specializing in fraud recognition, without changing the existing system.

Measurable Outcomes:

Operators spend 68% less time searching for information
43% more time in useful conversations with customers
Improved satisfaction of both customers and employees

Sarah Williams, customer experience manager at Fidelity, explains, "Our operators can now spend more time actually helping customers instead of wasting time on manual research."

Case Study 3: Public Administration

The Situation: The U.S. Office of Personnel Management was managing pensions with COBOL systems from the 1980s-functional but impossible to modernize.

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The Solution: Using AI to analyze millions of lines of ancient code and gradually modernize it.

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The Result: Modernization that would normally have taken years reduced to months, with no interruption in pension service.

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Immediate Benefits for Companies

1. Rapid and Measurable Return on Investment.

Companies that connect AI to existing systems are seeing real results:

+18% employee productivity
3 times more likely to exceed earnings expectations
80% less time spent on manual optimizations

2. More Satisfied, Not Replaced Employees.

Contrary to initial fears, linking AI to existing systems has made employees happier with their work. AI handles repetitive and boring tasks, freeing people for more interesting and creative tasks.

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Concrete example: In a call center, AI handles the simple, repetitive questions, while human operators handle the complex cases that require empathy and creative problem-solving.

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3. Security Automatically Strengthened

Modern linkage systems automatically include:

Advanced access controls (who can do what)
Data encryption (protection of information)
Monitoring compliance with regulations
Automatic cyber security reinforcement

4. Flexible Growth

The step-by-step approach allows:

Adding AI functions one at a time
Grow as needed without stopping work
Keep critical systems operational at all times

Major Challenges and How to Solve Them

Challenge 1: "Old Systems Don't Talk to AI."

The Problem: The systems of the 1990s were not designed to communicate with modern artificial intelligence. It's like trying to connect a payphone to the Internet.

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The Practical Solution: You install "smart adapters" that automatically translate messages between the old system and AI, just as an adapter allows you to plug an Italian plug into an American outlet.

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Example: A 1995 billing system is equipped with a "translator" that converts PDF invoices into data that AI can analyze for errors or anomalies.

Challenge 2: "Our Data Are a Disaster."

The Problem: AI needs neat, clean data, but old systems often have information that is scattered, incomplete, or in outdated formats.

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The Practical Solution: You use "data vacuums" that automatically:

They gather information from different systems
They clean and organize them
They transform them into a format that the AI can use

Example: A transportation company had customer data in 5 different systems. The cleaning system unified them, eliminating duplicates and correcting errors, creating a single database for AI.

Challenge 3: "What If They Steal Our Data?"

The Problem: Connecting old (often less secure) systems with new technologies can create vulnerabilities.

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The Practical Solution: "Zero Trust" principles apply-every communication is verified, every access authorized, every piece of data encrypted.

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Example: In a bank, even though AI reads transaction data to detect fraud, every single access is monitored and recorded, and the data is always encrypted.

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How to Start in Your Company

Step 1: Do the Home Inventory

First of all, you need to understand what you have:

Questions to Ask:

What computer systems do we use on a daily basis?
Which are the most important for business?
Where is our data and in what format?
Which processes require the most manual time?

Practical tip: Create a simple map of your systems, as you would do with rooms in your home before a renovation.

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Step 2: Choose the Pilot Project

Characteristics of the Ideal Project:

Not too critical (if it goes wrong, it doesn't stop the company)
With measurable benefits (time or cost savings)
With fairly clean and accessible data
With collaborative users

Perfect example: Automate the reading of vendor invoices. If it goes wrong, you can always go back to the manual method, but if it goes right, you save hours of work.

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Step 3: Choose the Right Partners

Types of Specialists Available:

Systems translators (convert old codes)
Integrators (connect different systems)
Security specialists (protect data)
Industry consultants (they know the specifics of your business)

Step 4: Start Small

The Winning Approach:

Testing a simple process
Measuring results
Error correction
Gradual expansion to other processes

Analogy: It's like learning to ride a bicycle - you start with training wheels, then take them off when you are confident.

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The Future of Enterprise Systems

Systems That Improve On Their Own

The next big step will be self-improving systems that continuously optimize their performance by observing how they are used. Imagine a car that learns your driving habits and automatically adjusts to use less fuel.

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Future example: A customer management system that notices that certain types of complaints recur frequently and automatically suggests improvements to the service.

Specialization by Sector

We are seeing increasing specialization:

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Healthcare: Systems that connect disparate medical equipment for a complete view of the patient

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‍Finance: Solutions that automatically comply with all banking regulations

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‍Production: AI that optimizes production lines and predicts machine failures

Integration with Emerging Technologies

In the near future we shall see:

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Local processing: AI running directly on enterprise devices to reduce waiting time
Virtual Reality: Three-dimensional interfaces for complex systems
Enterprise voice assistants: Controlling systems by voice commands

Conclusions

Intelligent linking systems represent more than just a technical solution: they are a digital evolution strategy that enables companies to enter the age of artificial intelligence without throwing away decades of investment and knowledge.

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Case studies show that companies that choose this path are not just adopting new technologies-they are radically transforming the way they work, one small improvement at a time.

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The message for business leaders is clear: while spectacular demonstrations of AI may make headlines, the real competitive advantage lies in the intelligent and nearly invisible integration of artificial intelligence into existing daily operations.

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The beauty of this approach is that you don't have to become a technology expert to benefit from it. You just have to be prepared to evolve what you already have, like renovating a house while keeping the foundation solid.

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Learn more about how our company can help you integrate artificial intelligence into your existing systems, contact us.

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Questions and Answers

What exactly is a computer systems translator?

A computer systems translator is a specialized solution that acts as an intelligent intermediary between your old software and modern artificial intelligence technologies. It works like an interpreter that allows people of different languages to communicate.

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Practical example: If you have warehouse software from 2005 that records everything in a specific format, the translator "teaches" the AI to read that format and use that data to make predictions or automate processes.

How much does it cost to connect AI to our existing systems?

Costs vary widely depending on complexity, but typically projects cost between 1.3 and 5 million euros for large companies. However, the average return on investment is +18% productivity, with savings significantly exceeding the initial investment over time.

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For small and medium-sized companies, you can start with pilot projects of a few thousand to test the approach.

How long does it take to see the first results?

Pilot projects typically show results in 6-12 weeks, much faster than the months or years needed to completely replace systems. The phased approach allows immediate benefits to be seen while minimizing disruptions.

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Example: A logistics company automated the reading of delivery notes in 2 months, immediately saving 4 hours of manual labor per day.

Is it safe to link our sensitive data to AI?

Yes, if done correctly. Modern connection systems include advanced protections such as automatic encryption, strict access controls and continuous monitoring. Many solutions are certified for highly regulated industries such as banks and hospitals.

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Example: In banks, whenever AI accesses customer data, the access is logged, authorized and the data always remains encrypted, even during processing.

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What old systems can be linked to AI?

Virtually all computer systems can benefit from links with AI, including:

Accounting software from the 1990s
Old generation database
Outdated warehouse management systems
Custom software developed in-house
Industrial and machinery control systems

The important thing is that the system contains usable data, even if it is in an outdated format.

Will AI replace our employees?

Practical experience shows otherwise. Employees become more satisfied because AI handles repetitive and boring tasks, allowing them to focus on more interesting and creative tasks that require human judgment, creativity, and interpersonal relationships.

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Case in point: At Fidelity Financial, employees spend 68% less time on manual research and 43% more time on useful activities with customers.

Can we try a small project first?

Absolutely, it is the most recommended approach. Most successful implementations begin with a noncritical process to test how the integration works before expanding to larger applications.

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Tip: Start with something like automating invoice reading or customer complaint analysis-important but not vital processes.

Who are the main providers of these solutions?

Market leaders include:

RetroAI: Specializing in the translation of legacy systems.
Harmony Tech: Coordination between different systems
GuardRail: Safety and compliance
OpenLegacy: Comprehensive Modernization Platforms
Large cloud providers (Amazon, Microsoft, Google) with specific solutions

How do we prepare for implementation?

Preparatory steps include:

Systems inventory: List all the software you use on a daily basis
Data assessment: Understanding what data you have and where it is
Goal setting: Decide what you want to improve
Team creation: Identify who will be in charge of the project.
Supplier search: Finding specialists for your industry

What happens if the project does not work?

The phased approach minimizes risk. If a pilot project does not work, you can simply go back to the previous method without having compromised critical systems. It's like trying a new recipe: if it doesn't turn out well, you always have the ingredients to make the old one.

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In addition, most serious vendors offer guarantees on results and support throughout the implementation process.

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