How to boost enterprise AI success through technology neutrality
The vast majority of enterprise-scale businesses are either using or investigating artificial intelligence (AI). A recent IBM report suggests 42% have already actively deployed AI, while a further 40% are exploring or experimenting with the technology.
This deployment and exploration inevitably takes place in the cloud, where enterprises can take advantage of speed, scalability, security, 24/7 accessibility, and an impressive choice of AI applications. In fact, according to Reuters, AI is fueling another cloud computing boom. The publication recently reported that Amazon, Microsoft, and Alphabet are all enjoying better-than-expected sales at their cloud computing units due to corporate AI spending.
But businesses shouldn’t depend too heavily on these – or any other – single tech providers to support their enterprise AI strategy. Instead, they need diversity, neutrality, and redundancy across their entire cloud infrastructure. This ranges from data centers and carriers to cloud providers and routing services that together create resilient interconnectivity, allowing them to make the most of the AI opportunity.
Here are some of the ways you can ensure technology neutrality and boost AI success.
Take a multi-cloud approach
When your enterprise uses just one cloud service provider for all its AI applications, a single cyber attack or outage can have dire consequences for the business, its partners, and its customers. Taking a multi-cloud approach is the easiest way to reduce the risk of cloud concentration, while still embracing the opportunities that AI brings.
While individual AI applications may need to be located within a single cloud, enterprises can select different applications from different cloud providers. For example they may have an AI-powered customer service chatbot running in AWS, a demand forecasting tool running in Google Cloud, a cybersecurity application running in Microsoft Azure, and a generative AI content creator running in Oracle Cloud.
Even when these AI applications do the majority of their training and processing within a single cloud environment, they’re likely to receive data from, and send data to, other clouds, making a multi-cloud strategy the sensible approach for any business.
Adopt neutral connectivity
Cloud-based AI applications depend on vast volumes of data moving quickly and securely to, from, and between cloud environments. Keeping this data flowing requires diversity and neutrality in your interconnection infrastructure as well as in your cloud providers.
Businesses that want to make the most of AI should have a range of neutral carriers, data centers, and interconnection platforms (such as Internet Exchanges and Cloud Exchanges) that are independent of the cloud service provider, and of one another.
Using neutral infrastructure helps to reduce the risk of outages, as it can work with a number of different providers. It also helps to get AI applications up and running quickly in the event that there is an outage or other form of disruption. Again, this level of diversity dilutes the risk of concentration with any single provider.
Ensure full redundancy
Maintaining the resilience needed to effectively run AI applications and maintain acceptable service levels requires full redundancy to avoid single points of failure. This doesn’t just mean implementing redundant pathways that allow data to take an alternative route when one fails, it also means ensuring full redundancy at access points, data centers, and carriers.
Multiple layers of redundancy are needed in terms of technology, geography, and partners. And businesses need to consider configuration to ensure that, for example, pathways which are otherwise redundant don’t inadvertently cross virtual connections in a data center.
Read our AI guide
Check out our guide, The Secret to AI Success, to learn more about the five elements your business needs to enable fast, simple, stable, and secure cloud connectivity that will help you make the most of AI innovation.