Speed has always been one of the most important measures of business success. The need for speed has increased in recent years, as time-to-market, streamlined production, seamless logistics and 24-hour customer service are all expected as the foundation for companies wanting to compete. We’ve reached the limit of what hard work and elbow grease can achieve in these areas, and that’s where artificial intelligence (AI) is taking the baton and running with it.
AI models are no longer a fringe technology. They are now commercially available and companies can train them to fit their own specific use cases. This new-found accessibility is in no small part due to the cloud environment and the agility, flexibility and scalability that comes with it. The marriage of AI and the cloud has paved the way for AI-as-a-Service (AIaaS) solutions – just as the majority of companies are now taking advantage of Software-as-a-Service (SaaS) to avoid long-standing supplier lock-ins and dependency on legacy platforms, AIaaS brings the benefits of AI modeling, natural language processing (NLP), and large language models (LLMs) to the masses.
These benefits are hard to ignore. According to a report from Accenture, AI adoption has the potential to increase business profitability by an average of 38% by 2035. However, if companies want to reap the benefits of this now highly accessible technology, they will have to overcome one of its most problematic issues. common bottlenecks – connectivity. Not only are the speed and robustness of connectivity critical to leveraging AI, advanced use cases also require an expertly configured multi-cloud environment that ensures security and performance.
CEO at DE-CIX, a leading provider of premium interconnection services.
Putting the “P” in KPI
All companies use key performance indicators to measure their success, from first call resolution (FCR) to the time it takes to bring a product to market. The scope and ambition of these KPIs are of course limited by what the business can realistically achieve, but AI is changing that. Take Amazon, for example, which uses AI to create customized product recommendations, optimize pricing strategies on a regional basis, and detect fraudulent reviews and transactions. All of these functions are directly linked to KPIs such as sales volume, repeat customer requests and risk mitigation – all made possible through the use of AI. One of Facebook’s most important KPIs in recent years has been moderating the content on its platform. The company has also deployed AI and machine learning to flag and remove inappropriate content, allowing it to increase and meet its KPI targets to maintain trust in its brand.
The automotive industry is another good example. Automakers will have numerous KPIs around driver and passenger safety, which can now be amplified with the help of AI. Onboard infotainment systems can use real-time sensors to detect things like driver fatigue – something that wouldn’t have been possible without the addition of artificial intelligence.
As tools like OpenAI’s ChatGPT and Google’s PaLM become more sophisticated and available, the opportunity for businesses of all sizes to optimize their KPIs and achieve higher levels of performance is now within reach – but only if they avoid the connectivity bottleneck.
Connectivity is the foundation of cloud-based AI
Adding more traffic to an already busy road will only lead to more problems, especially when that traffic has to move very quickly to be useful. Adopting AIaaS with simple public internet connectivity is like putting race cars on a busy city road. It is technically possible, but does not produce the desired result. If companies are going to invest in the race car (AIaaS), they need to make sure there is a dedicated highway they can use. Interconnection technology can provide that highway.
The speed of data transfer (the race car) to and from the cloud is critical, and the unpredictable routes of the public internet can slow traffic and, perhaps worse, risk sensitive data being exposed. Interconnection achieves optimal performance by using direct connections from the company’s IT infrastructure to cloud services that bypass the public Internet. Enterprises can still use the urban road (public cloud) for non-critical traffic, and use the dedicated highway provided by interconnection technology for AI processing. This setup avoids the unreliability of the public Internet and provides near real-time insights and lightning-fast processing. Implementing a cloud routing service on an interconnection platform also enables direct cloud-to-cloud communications, eliminating the need for data to travel back to the corporate infrastructure. This service improves application performance across systems and ensures seamless interoperability between clouds – perfect for multi-cloud setups.
So the race has begun. According to IBM, 35% of companies worldwide have already implemented some form of AI and the AIaaS market will grow at almost 40% per year until 2030. To be part of this upward curve, companies must be in the cloud. – but more than that, they need to have the connectivity infrastructure in place to thrive in the cloud.
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