If global enterprises currently agree on one thing, it’s that innovating with artificial intelligence (AI) is critical to long-term success. From product design and production through to customer use, AI supports innovation, efficiency, time to market, customer support, and personalization.
The secret to AI success
Why enterprise AI innovation depends on mastering cloud connectivity
“Despite potential challenges and risks, organizations are confident that adopting AI will continue to be crucial for future proofing their business operations and remaining ahead of the competition.”
An IDC report indicates the worldwide AI software market will grow from $64 billion in 2022 to almost $251 billion in 2027.
The above figure doesn’t even include generative AI (GenAI) platforms and applications, which are expected to generate revenues exceeding $55 billion in 2027.
GenAI is already being embedded into business operations. A Forrester report suggests 71% of enterprises are experimenting with real use cases. It predicts those that actively harness GenAI will realize outsized growth and outpace their competition.
“The mass adoption of generative AI has transformed customer and employee interactions and expectations. As a result, genAI has catapulted AI initiatives from ‘nice-to-haves’ to the basis for competitive roadmaps.”
So where is the majority of this AI experimentation taking place? The answer, of course, is in the cloud.
Read on to find out how to ensure your cloud connectivity is fit for the AI age.
Section 1
AI works best in the cloud
Harnessing the benefits of any type of AI requires speed, security, and access to a choice of applications wherever a business operates. So the majority of AI setups will benefit from being hosted in the cloud. This boosts AI with cloud-native agility, scalability, and the convenience of being available from anywhere 24/7.
A common way for enterprises to deploy AI in the cloud is through commercially available, cloud-based Artificial Intelligence as a Service (AIaaS). This allows businesses to experiment with AI in a low risk way, and without large up-front investment. It enables them to train AIs for their own use cases, developing customized models and apps to solve their organization’s specific problems.
GROWTH OF THE GLOBAL AIAAS MARKET
By 2028 | By 2029 | By 2030 |
| $55 Billion | $76 Billion | $98 Billion |
| CARR 42.6% | CAGR 40.3% | CARG 38.2% |
| Marketsandmarkets | Maximize Market Research | MarketDigits |
There are various estimates around the size and expansion rate of the global AIaaS market, but the bottom line is its growth is rapid and outpacing the AI market as a whole.
AI in the cloud is far more convenient than locally-hosted models, and makes infrastructure planning far simpler. But getting the connectivity right is critical. Making the most of AI in the cloud means making sure your cloud connectivity is robust, resilient, and lightning fast, with short, direct data pathways and quick reaction times.
Section 2
Mastering cloud connectivity
Multi-cloud scenarios
AI functions effectively in the cloud, but not everything can happen within a single cloud environment. Even if most AI training and processing for a particular use case occurs within one environment, other applications that feed data into the AI model – or receive data from it – are likely to be housed in other clouds.
This makes it vital for businesses to embrace multi-cloud or hybrid cloud scenarios, with direct and private high-performance cloud-to-cloud connectivity. Businesses that avoid multi-cloud strategies because they perceive them to be too complex risk missing out on AI innovation potential. They also risk cloud concentration, but that’s another story.
Data sources and recipients
In addition to considering cloud-to-cloud connectivity as part of a multi-cloud strategy, businesses also have to consider how they connect sources and recipients to cloud environments.
Data comes from multiple different sources to be analyzed in an AI model, including ERP or CRM systems, IoT devices, customer products, and databases. And it goes to multiple recipients such as internal teams, end-customers, consultants, and decision makers, when the results come out. These sources and recipients aren’t necessarily in the cloud.
Enterprises need a holistic network environment that brings together all data sources and recipients. They need to update cloud strategies to make them fit for the AI age, considering how they connect to the cloud, how they connect between clouds, and how they connect to the end user.
Section 3
Cloud AI in action
A real-world illustration
Imagine a distributor of construction machinery has implemented an AI chatbot, and a contractor is using it to order a new part for an excavator.
The application for the AI chatbot operates in one cloud environment. But to access the distributor's CRM data to determine what excavator model the customer has, and what repair and maintenance plan they are on, it will need to communicate with another cloud environment.
It will then need to check stock and order the part, and ensure local engineer availability, possibly in two additional cloud environments, all in a matter of seconds, before going back to the customer with an install date.
Other examples where AI needs to work across multiple clouds, data sources, and recipients include:
AI embedded into automotive technology to detect the state of the driver using sensors and take real-time mitigating action. This requires fast and reliable connectivity from the vehicle to the cloud and back, and often between clouds to draw on additional contextual data.
AI included into ecommerce apps to increase sales by making intelligent recommendations. In addition to a fast, robust connection from the app to the cloud, cloud-to-cloud connectivity is needed to access purchase and preference data and inform instant recommendations.
AI used by financial services companies to improve consulting. GenAI large language models (LLMs) specializing in tax or wealth management need access to countless sources of real-time data, hosted in a variety of cloud environments, to give the best possible advice.
AI accelerating the recruitment process. By screening applications, matching candidates to the right vacancies, arranging appointments, analyzing interview footage, and onboarding new employees, AI can speed up recruitment. This requires access to numerous cloud-based data sources and communication with multiple recipients.
Section 4
Five elements of AI-ready cloud connectivity
There are five key elements your business needs to enable fast, simple, stable, and secure connectivity that makes AI work effectively in the cloud:
Technology neutrality
Enterprises need a range of neutral carriers and data center providers, with infrastructure that is independent of cloud service providers, to ensure maximum resilience. They also need to use multiple cloud providers to avoid over-reliance on one, and full redundancy – at access points, data centers, and carriers – avoiding single points of failure.
Colocation data centers
Most smaller and private data centers don’t have the power density for AI servers. Businesses need to be in hyperscalers, which are massive, centralized, highly-efficient, and custom-built computing facilities. In a hyperscale colocation data center, it’s possible to privately route data between cloud environments within the data center campus.
A Cloud Exchange
Enterprise IT infrastructure should be connected to a Cloud Exchange on an interconnection platform. This ensures direct, secure pathways to cloud environments at the highest possible speeds, and reduces dependency on the unpredictable public Internet for AI workloads.
Find out how Industrial conglomerate Borusan improved network performance by 51% and decreased latency almost 60% using a Cloud Exchange instead of connecting via the public Internet.
A cloud routing service
A cloud routing service implemented directly on the interconnection platform can minimize the data pathways between cloud environments. A cloud routing service enables cloud-to-cloud communication without data having to travel back to the enterprise IT infrastructure, and ensures interoperability between clouds.
End-user connectivity
Finally businesses will need flexible connectivity interconnecting all company locations and remote workers, as well as customer-facing apps and other end-user digital products, all unified with cloud connectivity.
Ready to learn more?
Discover more about optimizing and future-proofing your business network infrastructure to cope with the surge in AI applications and an ongoing explosion of data, in our Beyond the Internet ebook.
