As AI is gaining traction, many cloud solutions are enhanced to better support AI use cases. One of the biggest advantages of AI-enhanced clouds is their ability to optimise infrastructure resources to fit the particular AI Inference needs of any business.

Whether a company is working on tasks like financial planning, improved customer support, or boosting employee productivity, AI clouds empower it to tailor its environments for these specific workloads, ensuring the best AI driven accuracy and performance. This capability provides organisations with the opportunity to run multiple AI tasks concurrently, test various AI applications, and continually refine for optimal results.

With the right tools and know-how, AI clouds can also integrate into a company’s existing IT infrastructure effortlessly, making them a convenient option for businesses that want to incorporate AI without requiring a major overhaul of their current systems.

For AI clouds to be truly effective, they must work seamlessly with an organisation’s IT environment. However, outdated systems can present obstacles, as they might not be compatible with the latest AI technologies. To address this, organisations need to focus on bridging the gap between legacy systems and modern AI platforms using specialised tools and careful planning.

The upfront cost of establishing an AI cloud infrastructure can be significant, but the long-term savings and efficiencies are considerable. With effective management, businesses can avoid many of the expenses tied to traditional cloud services, such as hefty data transfer fees. The ability to scale up or down resources on demand further ensures that enterprises only pay for what they use, maximising the return on their investment. AI clouds can also speed up the rollout of AI-based solutions, reducing the time required to bring innovations to market. This optimisation provides companies with an edge over their slower-moving competitors.

AI clouds rely heavily on data, but if the data is biased, the results will also be. Businesses must take care to ensure their AI clouds do not perpetuate biases based on race, gender, socioeconomic factors, or other personal attributes. Methods like bias audits, diverse datasets, and explainable AI techniques can help prevent this from happening. Establishing a clear set of ethical AI guidelines is important in making sure that AI systems align with the organisation’s values and don’t cause unintended harm to users or the broader community.

While creating new large language models is not the focus for most enterprises due to the huge upfront cost of training a new model, many organisations are taking advantage of existing LLMs as the foundation for their modern AI systems. By leveraging these models along with their own proprietary data, businesses can achieve superior results. Many techniques such as fine tuning an existing model, Retrieval Augmented Generative AI (RAG), and AI agents are employed for this purpose. AI clouds are specifically designed to support all these techniques and the unique demands of the various steps of AI workloads, delivering operational efficiencies while also tackling challenges like securing sensitive information and keeping data consistently accessible.

As companies look for ways to maintain a lead over the competition, many are looking to these AI-optimised cloud solutions. Traditional cloud platforms are playing catch up when it comes to handling the inherent properties of AI workloads, AI’s data processing needs and high-performance computing requirements. This is where AI-enhanced clouds can come to the rescue as they are purpose-built to address these workloads and provide the needed resources for AI applications.

One of the key requirements of AI workloads is multi-tenancy with assured SLA for each tenant. Unlike AI model training that requires a huge amount of resources for a single task albeit a very demanding task, most organisations are looking to leverage their investment in AI clouds over multiple AI tasks and multiple users. For example, they usually want to continuously chunk and embed new data to a vector database while serving multiple AI queries for multiple AI inference applications. Each one of these tasks has its own IT resource requirements and a significant performance degradation in any one of them has a direct impact on the overall effectiveness of AI. The multi-tenancy capabilities in AI-enhanced clouds ensure that tasks are isolated by pre-allocating compute and storage resources for each task meaning one tenant’s activity won’t negatively impact another’s performance.

Data security and effective data management are critical for any AI initiative. AI-driven clouds must offer seamless integration with different data sources, automate data workflows, and provide robust data protection to ensure smooth AI operations. With the right tools, businesses can ensure that data is readily accessible without delays, improving overall efficiency.

Given the sensitive nature of much of the data handled by AI applications, such as personal, financial, or proprietary information, robust security measures are a must. AI clouds should incorporate encryption, multi-factor authentication, and continuous monitoring to protect against unauthorised access. With increasing concerns about data breaches and regulatory compliance (such as Europe’s GDPR), implementing strong security protocols is essential.

While AI clouds present an opportunity for businesses to innovate and accelerate digital transformation, they also come with certain obstacles. Legacy systems, data silos, and data integration are just a few of the challenges companies must overcome. Additionally, securing sensitive data and adhering to regulatory frameworks complicates AI deployment. Perhaps, the largest obstacle is ensuring that multi-tenancy is supported and a proper process for leveraging allocation of resources to the various AI tasks is implemented to overcome the inherent inefficiency of traditional clouds.

Addressing these issues through careful planning, robust security protocols, and effective integration strategies allows businesses to capitalise on the immense potential AI-powered clouds offer without falling into common pitfalls.

Unlocking the Full Potential of AI Clouds

With the ability to customise, scale and enhance AI applications, AI-powered clouds provide a transformative opportunity for enterprises. However, to harness these benefits, organisations must tackle the challenges associated with multi-tenancy, security, data management and ethical AI. By adopting a strategic approach and implementing the right systems and protocols, businesses can create AI environments that are not only innovative and powerful but also high performance, cost effective, secure, compliant, and aligned with their ethical principles. 

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.