AI in the Cloud: Overcoming Challenges and Seizing Opportunities

With the artificial intelligence (AI) market projected to reach $407 billion in 2027, it’s not just innovators and early adopters exploring AI use cases and defining implementation roadmaps. An “early majority” of firms are now in the market for enterprise AI solutions that deliver cost-effective transformation, human capital efficiencies, and implementation time-to-value.

For maximum AI value, however, the right data and cloud infrastructure is required. The cloud provides the necessary computational power, storage, and deployment flexibility to fuel AI models and user adoption. But managing these resources effectively and securely isn’t every organization’s strong suit.

To achieve AI’s full potential, how should companies be thinking about their cloud capabilities?

The Intersection of AI and Cloud Computing

The future of cloud computing will be closely linked to AI, the two developing a symbiotic relationship that all organizations must understand, design, and invest in. Cloud computing is the on-demand availability of computing resources, such as servers, storage, databases, networking, and analytics. Cloud providers offer these services to clients over the internet and charge based on usage.

But an estimated 60% of senior IT decision-makers worry about their organization having the computing power and data center infrastructure needed to support GenAI, while 54% don’t have their data strategy in place to enable real GenAI capabilities. This is where cloud computing offers endless possibilities for firms looking to implement AI-based solutions.  

Outsourcing services to cloud providers grants IT leaders the necessary technology infrastructure and computing power to launch their AI goals. While assessing the large volumes of data required to train AI models, for most organizations, it becomes evident that cloud computing is the most viable path forward to obtaining the needed computing power.

Now and in the future, the ability to capture AI value and its competitive advantages will hinge on the cloud computing models organizations adopt. This investment is critical to delivering on the promise of AI-forward cross-functional process automation, machine learning (ML), predictive analytics, enhanced decision-making, and more.

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Strategic Infrastructure Investments: Finding the Right Balance

There are three general approaches to the cloud: public, private, and hybrid. Leaders must weigh the benefits and potential challenges of each to ensure their AI investments align with their cloud infrastructure of choice.

Benefits of AI and the Public Cloud

1. Public cloud is flexible and scalable. AI requires large computing power, and public cloud can scale up and down to match the organization’s ever-changing data requirements. Similarly, public cloud allows for flexible pricing models that enable companies to optimize their costs by paying extra when workloads increase and avoiding the high upfront costs of private cloud.
2. Public cloud platforms have integrated AI tools. This integration enables companies to capitalize on pre-built/connected “packages,” especially if they’re not interested in creating their own AI solution. AWS, Microsoft, and Google are leaders in this space and continue to release innovative AI solutions integrated into their cloud offerings.
3. Public cloud doesn’t require in-house AI expertise. Unless an organization is already an AI industry leader, a public cloud provider’s AI models are more likely higher quality.
4. Use case: Air Canada leveraged Amazon Connect and Rosie AI to transform their interactive voice response (IVR) systems, which improved customer satisfaction and reduced call volumes 15%.

Challenges of AI and the Public Cloud

1. Security and privacy are major concerns facing every CIO today. In cloud and AI spaces, security is all about access: Who has access? How do they access? Privacy is about how the data is used. Both security and privacy are at the mercy of the cloud provider, as seen in the recent CrowdStrike incident. Third-party risk, then, can have outsized implications that leaders must be attuned to.
2. Building around a specific cloud provider can lead to vendor lock-in. Should a company become too reliant on the cloud provider, they may not be able to switch without significant costs.

Benefits of AI and the Private Cloud

1. Private cloud offers more control. Companies are in the driver’s seat to control their data, infrastructure, security measures, and more, which is especially useful in highly regulated industries like healthcare, financial services, and government.
2. Private cloud enables AI infrastructure to be optimized for specific needs. Public cloud allows for a certain level of customization, but private cloud will always be more customizable.
3. Use Case: Hospitals, financial services firms, and other industries with high volumes of sensitive data may select a private cloud and AI strategy due to more stringent regulations and to maintain greater control of their data. They have the ability to utilize AI to improve client experiences while still securing data, a move that organizations like Somerset Capital Group and others are making. Apple and Microsoft are both expecting upticks in private cloud business as a result. 

Challenges of AI and the Private Cloud

1. Private cloud has a high upfront infrastructural cost. Between the servers, power, cooling, and maintenance, companies can quickly incur costs that may be prohibitive to smaller investments.
2. Private cloud requires expertise. Without the right people with this skill set, designing, building, and operating private cloud and AI tools is challenging.
3. Private cloud has limited scalability. Without the ability to evolve to changing business needs, private cloud can be limiting for high-growth industries. And because AI requires a lot of computing power to run, organizations may be underestimating how quickly they’ll exceed their cloud budgets.

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Benefits of AI and the Hybrid Cloud

1. Hybrid cloud offers the best of both worlds. The company can select the benefits from private and public clouds, giving it ultimate flexibility to optimize security, cost, customization, and scalability.
2. Hybrid cloud minimizes the risk of outages. Because there are two cloud environments, one can support the other in the event of an outage.
3. Use case: A bulge bracket investment bank relies on a hybrid cloud model to blend the benefits of a secure private cloud and incorporate AI capabilities from the public cloud. The bank uses private cloud to manage all internal investment banking and trading data while leveraging AWS and Azure for research inventory and public data sent to clients. This enables the bank to efficiently manage their research via AI to identify trends and comparisons within their data. Further, the AI capabilities are used to communicate with the bank’s wealth management division, which is the primary consumer of the research.

Challenges of AI and the Hybrid Cloud

1. Integration between public and private clouds. Cloud infrastructures can be complicated; in this instance, two may be worse than one.
2. As part of the hybrid environment, the company retains some of the challenges of a private cloud infrastructure, namely the high upfront costs and the required expertise.
3. In some cases, hybrid environments can lead to increased spend as a result of having both a public and private cloud.

Taking the Next Step With AI in the Cloud

As the engine of AI, cloud computing offers immense potential but also introduces significant complexity that requires careful planning. To cut through this complexity, CrossCountry Consulting leverages a proprietary framework to guide the end-to-end decision-making process and achieve optimal performance and cost efficiency.

For a strategic approach to architecture design, workload distribution, and continuous monitoring for maximum AI value, contact CrossCountry Consulting.