Will legacy systems be a hurdle for AI adoption?

AI is fast penetrating every space of the corporate world – from the financial sector (to facilitate the evaluation of loan requests, for example), the logistics sector (AI can identify the best route for delivery vehicles for quick delivery), to customer support (chatbots), and automobiles. Businesses will depend on AI to make intelligent decisions to drive multi-faceted solutions to major problems in these industries. The underlying ambition behind using AI is the system’s ability to learn, improve and enhance itself. To do this, intelligent AI software requires highly powered systems for analyzing huge amounts of data that goes into helping AI build the intelligence needed to make decisions. This requirement can be massive – one of the popular AI-focused data solutions on the market supports up to 17 gigabytes of data per second. And legacy systems are powerless to keep pace with such demands.

In a recent survey of AI developers by the Evans Data Corporation, 18% of developers cited legacy systems as the top barrier to AI development and implementation.

This is just one of the difficulties that pose a challenge in AI adoption. Lack of technical know-how, high system costs, storage, and computational requirements are all significant factors to consider when implementing AI into the enterprise. However, in a recent survey of AI developers by the Evans Data Corporation, 18% of developers cited legacy systems as the top barrier to AI development and implementation – the highest number amongst all the barriers identified.

The Top 3 legacy issues that pose as hurdles for AI implementation are –

• Capability issues with legacy hardware: Legacy systems in use today were a purpose – built for decade-old use cases. While they served those needs well, the sheer scale of storage and computing power required to drive modern AI systems makes depending on legacy infrastructure infeasible. However, that does not mean you should dump the entire existing system, and transition to an absolutely new infrastructure just like that. With solid planning, the transition can be much smoother and hassle-free.”
• Integration issues with legacy software: Legacy issues aren’t only about hardware. Enterprise software systems have been around for a long time now, and in a large number of enterprises legacy software still, run (quite efficiently) a large number of business processes. Integrating these systems with AI is a major challenge. Planning, analysis, and data management are key to ensuring that legacy systems operate well with AI.
• The talent gap: It goes without saying, too, that the gap not only exists with legacy infrastructure and software but with legacy talent as well. A large number of computer administrators and programmers today cannot work effectively with AI. Finding the right talent to develop, implement and maintain AI systems is not just difficult, but in a market where the supply is several times lesser than the demand, sometimes nearly impossible.

Companies that are considering AI implementation would benefit from working with a seasoned technology partner who can guide them through the transition from legacy systems to modern, AI-driven solutions. Reach out to us if you’d like to know how we can help modernize your enterprise infrastructure and get ready for AI!