Speed is critical in today’s business environment. Simplified manufacturing and logistical procedures, quick turnaround times for round-the-clock customer support, short time to market, and unmatched creativity and ideas for digital product and data-driven business model development are all necessities of modern businesses. To perform all of this, artificial intelligence (AI) models, whose number and capability are growing rapidly, are becoming increasingly important.
Businesses may train AIs for their own unique use cases using commercial AI-as-a-service solutions that operate in the cloud. Indeed, almost all artificial intelligence setups will gain from being in the cloud, as cloud-native agility, scalability, and the ease of always being accessible from any location will amplify the use of AI. Consequently, businesses need to adapt their cloud strategy to stay up with the ever evolving technological environment. The integration of cloud computing is increasingly crucial for business success, in addition to the fact that the growing usage of AI necessitates an enabling multi-cloud environment.
The combination of AI and the cloud is significantly speeding up business processes. It has been shown that adopting cloud computing may result in lower operating expenses, faster revenue growth, and improved crisis resilience. It is possible to cut a company’s time to market by up to 50% using a cloud and digital transformation plan. According to a research done by Accenture, the application of AI may raise profitability rates by an average of 38% by 2035.
Using AI to Meet Key Performance Indicators
An increasing number of company key performance indicators (KPIs) are depending on AI assistance, from first call resolution (FCR) to product innovations based on consumer behavior. AI helps speed to market, efficiency, innovation, customization, and customer support across the product lifecycle, from design to manufacturing and personalization. To provide one example: AI is currently being incorporated into automobile entertainment systems, allowing for enhanced customization based on preferences of the driver or passenger as well as the identification of the driver’s condition (e.g., fatigue) and the implementation of suitable corrective measures. Almost real-time data from the car’s sensors is required for the AI model to do this.
Therefore, robust, reliable, and lightning-fast communication is required between the digital product and the cloud. Similarly, telemedical diagnostics applications for e-health are crucial. The use of AI in payment and e-commerce applications to boost sales via smarter suggestion generation and chatbots for post-purchase assistance are also covered. The economic potential of artificial intelligence (AI) is growing, especially with the upcoming releases of OpenAI’s ChatGPT and Google’s PaLM in 2023, among others.
There are several benefits of using cloud-hosted AI versus locally hosted models. It is more convenient, cloud-native, quicker (given proper connectivity), and simpler to prepare the infrastructure for. All businesses should now embrace multi-cloud due to the introduction of AI-supported services and applications. While a significant portion of AI processing and training will take place in a single cloud, several additional applications that provide data to the AI model or receive data from it could be stored in other clouds or as cloud-based applications. Businesses that avoid the presumed complexity of multi-cloud management run the risk of losing out on opportunities for innovation.
Poor Connectivity Design Slows Everything Down
Our fast-paced business environment depends on quick decision-making processes and clear communication to maintain agility and flexibility, which are critical for staying ahead of the competition in the race to the customer. Brief data paths result in quicker response times, and the same is true for cloud-based data and applications. As such, your ability to perform anything else will be influenced by how you connect to your clouds and AI applications.
Cloud computing allows for lightning-fast data processing. Nevertheless, this might cause delays when the cloud depends on obtaining data from other sources, such as the business’s infrastructure or other clouds. Since data transfer speeds to and from the cloud are crucial, it’s not only about the data, services, and applications that are housed there.
Data can only move at the speed of light, which presents a challenge. Though it could seem quick, the process of transferring data to the cloud is slowed down if lengthy and uncertain diversions are taken over the public Internet. Not only that, but it may also expose the data. Through the shortening and security of data paths, direct links from the firm infrastructure to cloud services enhance data transfer. The organization may access clouds directly and at the fastest speeds feasible by connecting its IT infrastructure to a Cloud Exchange on an interconnection platform. Because AI tasks are immune to the whims of the general population, they are able to provide analytical insights in near real time, timely suggestions or warnings, and a variety of creative forms of assistance.
But it’s not just about optimizing the connectivity to the clouds – this is just the first step. Secondly, the pathway between clouds can be reduced dramatically by using a cloud routing service implemented directly on the interconnection platform. A cloud routing service makes it possible for clouds to talk directly to one another (cloud-to-cloud communication), so that data does not need to first travel back to the company infrastructure. Such a service also ensures interoperability between clouds, making an AI-enabling multi-cloud scenario more manageable and increasing the performance of applications across all systems.
Finally, by also directly connecting to end user access networks over an interconnection platform (also known as ‘peering’), the performance of customer-facing applications and customer service chatbots, as well as personalization and customization systems for example, can also be given a performance boost. In this way, you can make your data work for you – fast, flawlessly, and securely.
It Isn’t Just THE Cloud – Data Needs a Safe Home in Many Clouds
Releasing the focus from AI for a second, business continuity is the last KPI that has to be considered. The cloud provides more resilience than storing data on-premises, as many businesses have discovered. However, AI shouldn’t be required to comprehend that a corporation risking cloud concentration does so by choosing a single cloud. Having a single point of failure in the case of an outage might result from having an exclusive partnership with one cloud provider. As a result, a multi-cloud approach is a crucial component of every business continuity and disaster recovery plan, not merely a useful addition. Correct setup of a multi-cloud environment ensures that business data and apps are always accessible, even in the event of regional problems. A key component in this case is redundant connectivity via a distributed interconnection platform to cloud onramps from geographically dispersed data center sites. Furthermore, in the case of an outage, a smooth and automatic transfer to the backup cloud may be arranged with the help of a cloud routing service. This guarantees the business’s continued existence.
Getting Ahead of the Competition with AI from the Cloud
Thus, the race has begun. 35 percent of companies are already using AI assistance. From now until 2030, the market for AI as a service is anticipated to expand at a rate of about 40% annually, with a projected valuation of nearly two trillion US dollars at that point. Being in the cloud is a must if you want to participate in this fascinating trend. But the trick is not simply to be in clouds. Instead, it is about getting the most out of your clouds by making sure that your cloud connectivity is robust, resilient, and lightning fast.