AI Won’t Fix Your Business Until Your Legacy Systems Stop Fighting It

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Application modernization has always been an important process for technology managers. Taking existing applications and bringing them up to date is an important part of the process of managing legacy systems. Modernizing systems can be less disruptive than entirely replacing them with something new.

This subject is becoming critically important as more and more companies try to explore how generative AI can help them to gain productivity internally and offer more streamlined pathways for customer service. AI is a tool that can be applied across any organization – to find trends, patterns, and even just to enhance efficiency.

But the data inside most companies is not well ordered. It’s scattered across many different systems in different formats – it’s hard to get marketing to communicate with sales let alone share the same data. So how do we introduce AI systems that need to explore and use all this data?

The first option is to radically restructure how data is used in your business. You can create a data layer that is then accessed by every system in the company. This creates the opportunity to cut across all systems and all company departments – there is just one version of the truth.

The problem is that this is usually difficult to achieve. Often, your business processes will depend on a legacy system and that system is not easy to replace. it’s also dangerous to replace or upgrade multiple systems at that same time – the big bang approach could be a transformative success – or a complete disaster.

MIT research from 2025 shows exactly why this is important. They found that in most industries across the USA there is no measurable structural change in how companies are operating even when they are using AI. There are some personal gains at an individual level, but nothing that has an effect on the P&L of the business. In fact, this is so pronounced the MIT research suggested that 95% of companies that had invested in AI were not seeing any business impact – pilots were not scaling up to the enterprise level.

The report said: “The dominant barrier to crossing the GenAI Divide is not integration or budget, it is organizational design. Our data shows that companies succeed when they decentralize implementation authority but retain accountability.”

The MIT research shows that all these difficult to change processes and disorganized data are the biggest barriers to taking an AI idea, testing it with a pilot, then scaling up across the entire enterprise.

This is where modernization can be an effective approach. Take each application in turn and modernize it to work in a web browser, or work on the cloud, or migrate the system to a new modular architecture. Bring each application up to date in a way that supports greater connectivity to other applications and data sharing.

Although it is not as dramatic as a big bang where the entire architecture of the business is changed, it is more realistic. Very few companies can afford the risk of a widespread change to all business processes simultaneously – in most cases it’s just not feasible to even consider.

This is why application modernization needs to move from the CIO’s office right up to the boardroom. AI will never deliver enterprise-wide transformation if it is being forced to navigate broken processes, disconnected data in silos, and old legacy systems that were never designed to communicate with each other.

The winners of the future will not be the companies that keep launching pilot projects or those who just talk about AI on their social media channels. They will be the companies that quietly, but systematically, modernize the foundations of the business, one critical application at a time. This will allow AI to move beyond individual productivity hacks and start changing the way the entire business performs.

Your CEO may not even be aware that application modernization is possible, but they need to be. The future of your business may depend on it.

For more information on application modernization and examples of various approaches and projects managed by the IBA Group, please click here.