Cloud AI refers to the integration of artificial intelligence (AI) in a public cloud platform. It enables organisations to leverage enormous computing power and advanced AI processes without depending on costly, inefficient on-premises servers.

Artificial intelligence and machine learning (ML) have become near-essential for the modern organisation that wants to deliver exceptional customer experiences and stay competitive. But building custom models from scratch requires enormous resources, not to mention a great deal of technical expertise.

Combining AI with managed public cloud architectures knocks down these barriers, providing on-demand access to AI infrastructure and applications. This empowers businesses to utilise AI capabilities like natural language processing (NLP), predictive analytics, and computer vision — all without excessive costs and data science expertise.

Cloud AI is a huge draw for businesses. In this comprehensive guide, we’ll explain how it works, why it can benefit your organisation, and how an Australian business is using it to its advantage.

The foundation of cloud AI lies in enormous AI data centres known as hyperscalers. These hyperscale data centres boast vast horizontal server arrays, giving businesses the resources to access, deploy, and maintain AI applications without maintaining on-premises infrastructure.

Beyond that, cloud AI systems consist of several other key components that deliver AI capabilities to businesses. These include:

Cloud-native AI platforms, powered by hyperscalers, offer computing resources and a framework for businesses to build, train, and deploy AI and ML models. These all-in-one solutions ensure everything runs smoothly and securely.

AI platforms offer enormous cloud storage systems, such as data lakes, that provide reliable, secure storage and comprehensive data management features. These AI tools will often help organisations unify, clean, and prepare data to train AI models.

Cloud AI platforms often provide automated model training engines to simplify and expedite the process of building and deploying AI models.

Common AI processes like predictive analytics, speech-to-text, image and video analysis, and language translation are packaged in helpful APIs and software development kits (SDKs), enabling businesses to integrate AI capabilities into their existing operation easily.

Cloud AI providers also offer scalable query engines and trained AI models that provide real-time analytics based on raw data sets.

Cloud AI offers various advantages for businesses looking to overcome barriers to entry in the AI sector. Here are five to consider:

AI Cloud provides access to pre-built APIs and SDKs, offering businesses ready-to-deploy models that they can easily integrate into their operation — no need to build and modernise a costly system from the ground up.

These pre-trained models also improve time to market, reducing what could be months of testing and iterating into a few short weeks. This enables businesses to remain competitive and agile in the face of new trends.

The best part about cloud AI solutions is that they can be scaled up or down depending on a business’s needs and AI workloads. Load balancing capabilities mean cloud resources can be redistributed on demand to accommodate different tasks.

Hyperscalers offer flexible pricing models, allowing businesses to handle everything from small-scale experiments to large-scale deployments without investing in costly hardware — the company only pays for what it uses.

Cloud AI platforms have enormous computing power, facilitating advanced AI processes like predictive analytics, generative AI agents, and computer vision.

Ultimately, all of these benefits add up to superior customer AI experiences by allowing businesses to deploy efficient, scalable solutions that meet consumer needs.

Let’s now discuss some of the potential use cases for cloud AI solutions across different industries. After that, we’ll explore how Woolworths is using AI to optimise every aspect of its service.

Here’s how cloud AI can be applied to support better patient outcomes in the healthcare industry:

• Personalised medicine: AI can analyse patient data and recommend personalised treatment plans, saving professionals time.
• Medical imagery: Cloud AI can act as a second pair of eyes for experienced doctors by diagnosing diseases based on medical imagery.
• Drug research: Cloud AI expedites drug research by analysing biomedical data. It can also optimise clinical trials and predict drug interactions based on patient history. All of this streamlines the development of new treatments.

Cloud AI built on hyperscale infrastructure is big news for retail and e-commerce, allowing businesses to deliver personalised customer experiences across every digital touchpoint.

• AI chatbots: AI agents utilise natural language processing AI and machine learning models to provide valuable, tailored advice in response to customer queries. These AI assistants allow retail businesses to resolve disputes with little to no direct human involvement.
• Customer personalisation: Cloud AI can analyse customer data to provide personalised product recommendations and offers tailored to individual preferences.
• Supply chain optimisation: With the help of predictive analytics powered by cloud AI, businesses can streamline inventory management, predict demand in peak seasons, and optimise inventory levels well in advance. This improves customer satisfaction and reduces long-term costs.

Next, there’s AI in financial services. Here are three key ways businesses in this industry are using cloud AI to their advantage.

• Fraud detection: Cloud AI enables real-time fraud monitoring. It can detect unusual patterns in transactions to flag suspicious activities instantly, supporting rapid human intervention.
• Risk management: AI can analyse vast financial datasets to evaluate risks and suggest investment strategies. This drastically improves portfolio management.
• Algorithmic trading: AI can even perform algorithmic trading, executing strategies automatically based on predefined criteria.

The opportunity to provide personalised learning experiences and automate repetitive admin tasks is an exciting prospect for the education sector.

• Personalised learning: Cloud AI solutions can prepare custom AI content for students based on past performance and progress, helping all students learn at their own pace.
• Predictive analytics: Cloud AI can also predict student performance to identify those at risk of failing proactively. This allows teachers to offer early intervention, improving student outcomes.
• Automated grading: Lastly, AI can automatically perform monotonous tasks like grading, freeing up teacher time to focus on providing individual support to students.

Lastly, there are the manufacturing industry solutions, where predictive maintenance and AI automation have the potential to revolutionise the way businesses operate.

• Predictive maintenance: AI cloud solutions can examine machinery and analyse data to discover issues and defects early. This enables manufacturers to schedule maintenance activities, helping to minimise downtime proactively.
• Quality control: AI-driven systems can analyse products to ensure their quality. If a defect is identified, the solution can flag this immediately, improving overall product quality.
• Product scheduling: Cloud AI can optimise production scheduling by analysing historical data and resource availability. This improves overall operational efficiency.

Australian retail brand Woolworths has consistently impressed with its forward-thinking approach to AI adoption. The company has embraced cloud AI capabilities to enhance operational efficiency, improve customer experiences, and stay one step ahead of a ruthlessly competitive industry. Here is how they’ve used scalable AI to their advantage.

The most exciting component of Woolworth's digital transformation is its best-in-class voicebot and large language models (LLMs). Powered by Google Cloud AI, Woolworths’ AI chatbot is capable of handling calls, cross-checking over 30,000 products, tracking requests and common questions, and processing refunds.

The chatbot can take care of thousands of calls a week, giving human agents time to focus solely on complex queries. Beyond this, the Google Cloud AI infrastructure allows Woolworths to scale as its user base grows flexibly.

Woolworths’ chatbot has been nothing short of a success — after implementing these digital assistants, the brand saw a two-point increase in net promoter score (NPS), showing that businesses can integrate generative AI models to save time without negatively impacting the customer experience.

Woolworths has also partnered with AI-powered intelligence platform TCS Optumera to deploy an AI model capable of recommending space and assortment optimisation for retail products.

The model utilises predictive analytics to suggest the optimal layout for stores and shelves, saving time in the planning process while allowing the brand to deliver better customer experiences across each of its 1,451 local stores.

The best part about Optumera is that it’s ‘always on,’ constantly looking for the next-best opportunity to save space and improve profitability. Naturally, the model also allows Woolworths to test different configurations and choose the best one instantly rather than going through a lengthy process of trial and error.

Security is another area that Woolworths has optimised with the help of the AI cloud. In dozens of stores across NSW, Queensland, and Victoria, the brand is experimenting with self-checkout AI cameras to observe customers scanning items. These models are capable of detecting when items are not scanned correctly, significantly improving loss prevention efforts.

Ultimately, Woolworth's commitment to artificial intelligence allows it to save time and money, deliver better customer experiences, and improve profitability — all without introducing the high degree of risk that would come alongside designing and deploying these models in-house.

Cloud AI is a huge draw for businesses, but it still has its challenges. Here are three you should know about.

AI and security have a bit of a rocky relationship. There’s a concern that AI models could leak sensitive information from the data sets on which they are trained. For that reason, it’s crucial to choose a cloud service provider that makes security and privacy a priority, with strong fault tolerance and disaster recovery protocols.

Enterprise AI solutions can only be trained effectively with structured, clean enterprise data. Businesses need to have a process for standardising data and ensuring its quality, or they risk developing inaccurate, unreliable models.

Depending on a single cloud provider can limit flexibility if the business wants to migrate data, update workflows, or switch vendors at a later date. Businesses should try to opt for a hybrid cloud approach where feasible to limit the risk of vendor lock-in.

Cloud AI has the potential to revolutionise every aspect of a business’s operation. By adopting a public cloud infrastructure for AI development and deployment, organisations can automate workflows, predict patterns through data, and optimise every aspect of the customer experience. This, ultimately, will be the key to remaining competitive in the future.

The first step to getting started with cloud AI is finding the perfect platform for your business needs. Salesforce Data Cloud is our best-in-class cloud AI model. Built on Hyperforce, our public cloud infrastructure, Data Cloud, provides a unified platform for you to collate your business data and deploy cloud AI models and agents into your operation.

Data Cloud will collate all of your siloed data stores, giving you a 360-degree view of the customer. From there, it integrates seamlessly into the Einstein 1 platform, allowing you to deploy AI models that support every aspect of your business workflow — from sales pipeline optimisation and customer service to personalised marketing and more.

Data Cloud and Einstein 1 can help you develop AI models that deliver exceptional customer experiences. Try it for free today to learn how Data Cloud can power your next AI project.