Preparing Legacy Systems for the AI-Driven Future

Many enterprises still rely on legacy systems that are not designed for artificial intelligence (AI) integration. With the right strategies, however, these systems can be modernized to support intelligent workflows. It is critical for businesses to assess legacy systems so they can extract and prepare data for AI use. This extraction from user interface (UI)-based systems, such as scanned image platforms, is achieved through various methods, including application programming interfaces (APIs), offline exact, transform and load (ETL) processes, embedding AI-driven decision-making tools and using robotic process automation (RPA). The goal is to bridge the gap between legacy technology and modern AI capabilities without requiring a complete system overhaul. 

Application Modernization 

Development schedules and time to market are reduced by up to 50% when AI tools are integrated. With key DevOps contributions, idea phasing, content development and simulation in rapid prototyping, these tandem shifts in development tools and increasingly complex unstructured data underwrite IT uncertainty. Legacy systems often rely on fixed message formats and field limitations, leading to a loss of data capture from these new data landscapes and, subsequently, missed market opportunities. 

Innovation is beneficial, and the tech and AI business is booming, but technical debt is becoming the elephant in the room with AI. JPMorgan Chase invested $17 billion in technology in 2024, yet the question beneath that number is what the $17 billion is for. As innovation drives forward, it leaves a wave of technical debt in its wake. A recent study on smart payment assistants notes that 43% of financial institutions still depend on legacy mainframe systems, and over half have been in operation for more than 15 years. Almost 50% of JPMorgan’s expenditure is allotted to IT maintenance and integration. 

Technical debt is the accumulation of prior solutions that eventually lose market advantage, are phased out, or no longer integrate with contemporary IT infrastructure. This includes infrastructure, code, tools, processes, or databases that become outdated or less accessible as innovation and patches mount up. Enterprises need to accommodate a host of new tools, formats, processes and market players to take advantage of the increase in data and adapt to the shifting functional requirements these opportunities bring.  

Legacy systems are a common issue during AI transitions and the future of the AI economy. As innovation accelerates, it is crucial for an expanded effort at integration and migration to capture innovation. ETL and RPA automation tools help manage these processes but require expertise in integration and implementation, which may hinder smaller enterprises from keeping up with larger players driving the field. To capture value in data economies, IT and business processes are required to interact with and analyze the available data. As complexity, data types, scale and scope of data proliferate, accomplishing this requires advanced expertise and maintenance to mitigate technical debt and risk. 

Advantages of AI Integration  

Time to market and advanced analytics drive advantage in a volatile field with high data volumes, like banking or fintech. With highly fragmented and diverse data systems, some banks handle an average of 75,000 databases. At that scale of data, AI and machine learning (ML) are not just innovative tools; they are requirements. Machine learning models have a significant impact on payment optimization for fraud detection, processing efficiency and predictive modeling. Advanced analytics occur in various environments and frameworks. Natural language processing (NLP) and transformer models provide support and personalization solutions, while a variety of network models and ML forecasting tools offer predictive analytics insight. With tens of thousands of databases and multiple million transactions per second, the scale for a human understanding of the system has been exceeded. Hybrid systems with human-in-the-loop are the way forward. 

The scope of analysis that AI models provide is a significant advantage in preparing a legacy system for AI integration. In a 2024 article about AI’s future, the internet of things (IoT) and synthetic data generation are key areas of concern for feeding the data-hungry AI progress wave. The other side of the AI tech coin is process and management. Data governance will increasingly play a role in supplying access to AI solutions and is a key component of an integration plan. These powerful analytic tools are moot without decision-makers and sponsors understanding the business process inventories, dependencies and the associated utility scores. 

Assessing Legacy Systems for Integration 

What is the process for integration, given the significance and scope of integration expected for modernization? Data governance and master data management (MDM) frameworks assist in organizing and mapping business processes in transparent inventories to establish data ownership, produce records of truth and shore up data integrity. MDM stipulates clear data ownership and role accountability for managing data quality and delivery. This prepares the organization to maintain traceable records to accurately apply AI insights at the right time.  

One of the most daunting barriers to modernization is legacy code and data format integrations. Cataloging data types and formats is a component of good data management practices. Slow rollout approaches are a risk mitigation technique to keep functioning systems online, allow for retraining and prevent adopting volatile technology. Integration with cloud platforms to manage migration is a hybrid approach that enables critical systems to remain online while updating processes incrementally. As the need for integration continues at pace with innovation, the industry can expect to see more tools and reliable frameworks for integration and rollout from service providers and leading tool providers. 

In a period of rapid innovation and uncertainty, legacy integrations will become much more familiar. In this process, good data management and governance practices help retain data integrity and provide professionals with a control structure. In bridging the gap between legacy and AI systems, the integration rollout will become a mainstay of IT. Professionals mitigate the risks of technical debt by following data governance and data integrity practices. Decision makers can rest assured that there will be another round of innovations and applications to integrate again next year. The question is not which technologies to adopt but whether the organization is prepared to adopt an integration, rather than an innovation-based technology strategy.