AI FOR THE ENTERPRISE – A STRUCTURED APPROACH TO MODERNIZING LEGACY SYSTEMS

In an era defined by rapid technological advancement, the imperative for modernizing legacy systems has never been more pressing. Among the myriad of innovations driving this transformation, facial recognition software stands out as a beacon of efficiency and security. By seamlessly integrating this cutting-edge technology into existing infrastructure, organizations can not only streamline operations but also bolster their capabilities in authentication, surveillance, and customer engagement. Facial recognition software, Fitbit, Alexa, and other smart apps guiding the lives of millions of people are all ruled by AI (artificial intelligence). Numerous startups, organizations, and enterprise AI have managed to build incredible AI-powered algorithms and strategies capable of solving challenging business problems. The only pertinent issue with AI is that companies are struggling to make the underlying technology scalable.

The potential of artificial intelligence is huge, and while it may sound complex, if implemented correctly it can succeed at an enterprise AI level. The problem is many executives have yet to acknowledge that AI has its limitations. Without the right data to provide reliability, data privacy, and security, the implementation phase cannot thrive.

AI is more than just an advanced technology tool

For organizations to expand the use of artificial intelligence and reap all the benefits it can offer, first, they must build a unified strategy oriented towards uncovering, framing, and democratizing AI on all levels of enterprise AI. The focus should be on identifying opportunities with added business value, rather than viewing the technology as a limited tool.  Prior to getting started, the following steps to successfully expanding AI apply:

  • Develop a maturity assessment substructure – start with understanding the automation and AI maturity of your organization across multiple sectors and dimensions. Analyze your current strategy, technology, and organizational readiness, and ultimately your data strategy.
  • Process recognition – to benefit from everything AI has to offer, make sure to define, assess, and map out all processes. Leverage value stream mapping and process mining (e.g. Minit, Digital, Celonis) to brainstorm ideas and acknowledge the potential of the technology.
  • Data-based discovery – companies can make the most of their legacy systems to gather insights and work with the data at hand to add improvements. Problem analytics, correlation analyses, social media, and service assurance analytics should also be used to collect as much information as possible on the needs of their customers.
  • Close partnerships to drive innovation – last but not least, organizations should embrace change and get out of their comfort zone. By closing new partnerships with AI experts in the industry, they have the chance to collaborate with numerous high-stake third parties that can help them implement an effective AI strategy. Such collaborations will make their business more scalable via design thinking workshops, structured interviews, and added industry know-how.

Guidelines on embedding across the enterprise AI

There are numerous ways AI can enhance business operations without impacting legacy systems. When embedded correctly, the new services and applications added don’t just increase efficiency, but also improve customer experience. The first step is to use AIOps (artificial intelligence operations) to integrate self-healing capabilities and preventive maintenance into the infrastructure management system. One of the core benefits of AIOps is that it can enable companies to detect and prevent application failure ahead of time. Fixing a problem before it happens eradicates friction, and it can be done successfully via bot factories capable of producing a whole army of action, sensing, and analytical bots.

By combining process bots with conversational AI, virtual agents can significantly boost customer satisfaction. Providing fast replies, reviewing feedback, and asking the right questions at the right time, are just some of the activities that AI can handle really well. For instance, Infosys’s call center platform uses AI-enabled Cortex 2 to analyze call patterns, customer data, and customer history, and prioritize tickets. The end goal is to increase client satisfaction, reduce call handling time, and at the same time boost the company’s ROI in the long term.

Modernization of legacy systems

Because of AI, application development is not as technical as it was 10-15 years ago. Today’s processes are less technical as there is a myriad of no-code/low-code platforms out there that can help engineers build new apps in minutes. There are many benefits of AI, but organizations must accept and acknowledge them first to see results. Transitioning to an AI-enabled world can be done without impacting legacy systems. However, companies must find a way to stay focused on creating an environment that enables them to understand how AI works, including how it applies to their unique business needs.

Closing partnerships with industry experts is equally important, as AI professionals can help with adoption via all kinds of methodologies and techniques for handling bias, data privacy, security, and trust. To move forward in a digital world, enterprise AI must embrace that artificial intelligence is the key to streamlining processes, automating operations, and ultimately scaling by offering superior customer and employee experiences.

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