Implementing AI is no longer the biggest challenge, but scaling it effectively is
Aveekshith Bushan, Vice President and GM, Asia Pacific and Japan, Aerospike
A hyperconnected world is now the norm. Since the Digital India initiative was introduced, the number of internet users in India has grown from 305 million in 2015 to 1.24 billion in 2023, according to a 2024 Statista report. The outcome is a digitally savvy population that readily accepts new technologies.
As digital literacy has expanded, artificial intelligence (AI) has moved from a futuristic concept to being an essential part of modern business-consumer interactions. These AI-driven consumer services are revolutionizing industries, enabling companies to provide personalized, real-time experiences so they can stay competitive. The challenge now is not just how to implement AI, but also how to scale it to meet the growing volumes of data, while maintaining infrastructure and cost constraints - especially when serving millions of users.
Data Overload and Processing Challenges
Data overload occurs when the volume of information surpasses the ability to process it effectively. Recently, X (formerly Twitter) faced a significant data overload issue, with users globally reporting problems such as delays in post loading and search functionality disruptions. This situation highlighted the platform's struggles to manage real-time data processing effectively, leading to user dissatisfaction.
With advanced algorithms and machine learning techniques, AI holds immense potential for making sense of data, identifying patterns, and extracting valuable insights. But it is also a significant contributor to the data overload problem as it generates even more data at an unprecedented scale. In the above example, the data overload issue on X is partially exacerbated by content generated by AI bots, which increasingly mimic real users to exploit the platform’s engagement-driven algorithms. X’s infrastructure struggled to discern and filter real interactions from bot activity due to the sheer volume of transactions. The inability to distinguish authentic from inauthentic activities results in slower response times for genuine users, increased costs for maintaining and scaling inefficient systems, and user dissatisfaction.