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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Businesses that understand their data strategy will be best positioned to extract true value from generative AI
The excitement around the emergence of generative AI is wide-ranging and there is no doubt about its potential to disrupt and revolutionize myriad processes and behaviors. In telecommunications (telco) business assurance specifically, generative AI is poised to reshape the operational landscape, as it not only analyzes big data but creates invaluable insights and executes actions based on that information. Generative AI can deliver dynamic solutions in anticipation of issues and challenges, driving proactive decision-making.
Telco businesses are evolving to be ecosystem-based and the more players and links there are, the more opportunities exist for poorly executed operational processes and lost revenue. The complexity of the myriad interactions in those ecosystems is now simply too great for traditional approaches to manage. This leads to revenue leaking away undetected, either through inefficiency or stolen through fraud. Properly integrated generative AI can transform the business assurance model and taking a pragmatic approach to introducing generative AI into the existing business assurance team can help future-proof developing processes.
It’s not difficult to understand the concept of generative AI – the barriers to its success lie in the execution, so it is essential for a business to have its data strategy locked down before looking to leverage generative AI. How and from where data is gathered, where it is aggregated, how its health is assessed, how it is stored and for how long – all this needs to be clear.
We advise our customers to take a steady, structured approach: start with monitoring the data, conducting root cause analysis and then move to the configuration of generative AI. Processing the data is all very well but it’s extracting the usefulness that is key. The ‘squad’ of generative AI agents is essentially a group of sub-programmes – one mining the data, one classifying insights into good and bad buckets, one executing workflows and so on. Understand what you need before moving to the operational stage.
Generative AI agents can enhance operations across telcos’ business assurance teams, taking the burden away from human capital and freeing people up to work on higher value tasks. With telcos’ customer expectations rising relentlessly while budgets are stagnating, implementing generative AI in a tempered manner can deliver impressive results in terms of time saved and return on investment.
However, the true value of generative AI can only be extracted if it is implemented on top of firm business foundations. There is a tendency to dive headfirst into a new technology, especially one with the magnetism of generative AI. But the value of that technology needs to be properly defined.
Businesses need to ask key questions – what will this technology achieve for my organization? What am I missing today that I can achieve with generative AI? The business case – the business problem – must be clearly articulated and that is critical to the success of any generative AI project.
Generative AI is not a panacea and no one can yet claim to be an expert but businesses that truly understand their data strategy and the issues that need to be addressed will be best positioned to extract the true value.
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