AI Needs a Strong Foundation – Are Your Business Apps Ready?

Nowadays many companies are so excited about the possibilities of artificial intelligence (AI), but they are still back in this race due to their outdated software . These outdated software were made to store data, when data storage was so expensive , so they were unable to store and retain large amounts of data . As a result, they don’t provide enough data which is necessary for effective AI work . Also, maintenance of these systems is so expensive and also drains money from the AI project. So experts believe that modernizing and integrating these systems is the best way forward
The big problem of legacy software is that it wasn’t built with an AI mindset. Decades ago, when various kinds of systems were introduced , companies had to be selective from all of these various systems to store the data and the companies were very selective regarding this matter because storage was expensive. Today, storage costs have dramatically decreased, and companies now have the ability to collect and process large amounts of data. However,the old software is made with that kind of technology which creates difficulties to integrate with AI,which creates a major roadblock for businesses trying to utilise AI-driven solutions.

Beyond this , maintaining outdated software is also not pocket friendly . As technology evolves, fewer engineers uplifts their career on this and build legacy applications using old programming languages. This means companies have to pay a high amount for hiring these specialized talents to keep these systems running. In many cases some of the company ended up paying a high amount from their expenditure budget for maintenance of their old system. Data from a survey held in 2023 found that companies were spending approximately 13% of their IT budgets to maintain old technology, which ultimately drew down the financial resources for AI projects.
AI success is directly proportional to the effective management and store of data . Most of the AI models lie on high amounts of old data to give perfect predictions and generate valuable information. However, if a company’s software is unable to store data , AI won’t be able to give perfect predictions . Some companies may have a large amount of important data in their old system, but because of outdated data storage software and lack of perfect management practices, they struggle to retrieve and use it in an efficient manner .

Nowadays these problems look like a barrier in their growth and implementing AI for better practices for industries like banking and the financial sector just because they are still dependent upon the old legacy system . These old legacy systems are still in use by them for operating business , which stood like a mountain and not easy to replace them entirely. However, experts believe that complete replacement is not needed. Instead, companies can go for the integration and modification of these old systems by integrating them with advanced AI systems.
A live example of this matter comes from Ensono, a service provider for business. Ensono was working on an AI-powered tool which will predict maintenance needs of the customer. However, they quickly ran into a problem—many of their customers used old systems to collect incident reports,which is in an inconsistent way . Because AI needs well-structured data to work effectively, integrating the old data with the AI model was a major challenge.

Other companies face similar issues . A large enterprise recently told Jeremiah Stone, Chief Technology Officer of SnapLogic, that their data wasn’t useful for AI because of the poor management of the software over the years . This is a common problem to implement AI, and many companies are now spending large amounts of money to modernize their systems. Stone explained outdated software as a “multi-trillion-dollar problem” because businesses worldwide are hustling to bring their technology up to speed with AI advancements. Even after a decade of efforts to modernize IT infrastructure, many companies are still in the middle phase of this transition.

Fixing these issues requires a strategy . Experts suggest companies need to evaluate their IT infrastructure and identify the system which is urgently needed to be upgraded. The reality is that most businesses will end up with a combination of old and new systems, so it’s important to have strong integration strategies to prevent data from fragmented. Without proper integration, AI technology is unable to give predictive analysis.

One way to modernize legacy systems is by utilization of middleware and APIs (Application Programming Interfaces). Instead of completely rewriting old software, which can be costly and time-consuming, companies can use these technologies to arrange legacy systems with newer platforms. This allows them to extract and integrate data into AI models without having to overhaul their entire IT infrastructure. Middleware makes a bridge between old and new software, making it easier for companies to update their systems in a more gradual and cost-effective way.

Use of data lakes is another useful approach . A data lake is a system that allows businesses to store information from different sources in its raw format. This means that AI models can access the data they need without depending upon outdated applications. By creating a data lake, companies can bypass some of the limitations of legacy systems and ensure that AI has access to the best possible data.
However, some experts said that outdated software itself isn’t the biggest challenge—what all happens to it once data is extracted. Robert Cloutier, a lead data and AI engineering manager at Nexapp, believes that the real issue is extraction of data in an efficient manner so it can be used effectively. Many legacy systems contain vast amounts of valuable information, but without proper data engineering knowledge , that information remains difficult to use. AI models need structured and meaningful data to function properly, and companies sometimes struggle with this step.

Cloutier also added that many older systems have been running for decades, collecting a treasure trove of information. While companies may hesitate to extract data from these systems due complexion, the right data engineering expertise can help unlock this hidden value. He warns that a long wait can hamper the adoption of AI, leaving companies behind in the race track.

The good news is that companies do not need to fix everything overnight. A gradual approach which allows the companies to run their old system and beside upgrading the some system . By focusing on the most critical area first the companies can start updating this without delaying the modernization process. In many cases, small improvements—such as integrating APIs, using data lakes, and improving data engineering—can make a big difference.

Ultimately, outdated software is not a barrier for AI adoption. With the right strategy, businesses can modernize their legacy systems in a way that maximizes efficiency and minimizes costs. By improving data storage, integration, and management practices, companies can avail the full potential of AI and stay in the track in the digital race. Instead of seeing legacy systems as a roadblock, organizations should view them as an opportunity to refine and enhance their technology for the future.