By Neil May
There’s no denying that artificial intelligence (AI) and cloud computing are booming and reshaping how businesses operate around the world. By Q4 2024, the AI market is set to exceed $184 billion, showing a sizeable jump of nearly $50 billion from the year before [1] and expected to skyrocket past $300 billion by 2026 [2]. These numbers reflect the astronomical investments pouring into AI technologies across different industries.
At the same time, the global public cloud computing market is also booming, as enterprises increasingly use cloud services for scalable solutions in networking, storage, and databases. In 2023, businesses spent a whopping $270 billion on cloud infrastructure services – a $45 billion jump from 2022 [3]. As more and more businesses join the race for digital transformation, cloud spending is expected to hit about $675 billion by the end of 2024 [4].
But alongside this growth comes big challenges for businesses, especially in handling data. In 2023, data management emerged as the top hurdle for AI development. Companies worldwide are struggling to manage the sheer volume of data AI requires and generates. Surprisingly, concerns about storage capacity seem relatively low, suggesting the focus is more on using data effectively rather than storing it [5].
With all this growth and spending, managing cloud costs has become critical – and for some businesses – urgent. Many larger organisations are dealing with ‘bill shock’ from unexpected expenses (we’re talking thousands and millions in some cases) and inefficient resource use, so they’re looking increasingly to advanced cloud management platforms and AI-driven analytics. These tools can boost efficiency by providing real-time insights into how cloud resources are used, automating tasks like resource allocation, and pinpointing where costs can be trimmed.
In essence, as AI continues to revolutionise industries and cloud computing becomes indispensable, mastering cloud spending is the key to business growth.
The AI-Cloud Symbiosis: Innovating Together
The relationship between AI and cloud services is inherently symbiotic, driving innovation in the market. When AI is integrated into cloud systems and processes, organisations can revolutionise how they manage operations and add flexibility, agility, and stronger security measures. This integration works across different types of cloud setups – private, public, and hybrid – boosting overall system performance. The real magic happens when AI unlocks advanced capabilities in cloud services. By crunching real-time data, AI transforms how businesses operate, making them more agile and strategic in their approaches. Businesses can gain better scalability, run operations more efficiently, and make smarter, data-driven decisions – all thanks to AI.
One of the biggest advantages of AI in the cloud is how it helps companies scale up smoothly. By using AI-driven solutions, businesses can predict future demands and optimise resource allocation accordingly. This means they can handle increased workloads without massive infrastructure overhauls, which is crucial for staying nimble and competitive.
Scaling AI in cloud computing isn’t without its challenges, though. It requires strategic approaches like getting leadership buy-in, establishing clear ROI metrics, and using responsible AI algorithms. These steps ensure that AI integration not only scales operations but also does so efficiently and with minimal disruption. AI algorithms continuously monitor workload patterns and can make recommendations on adjusting resource allocations accordingly. For instance, during peak periods such as seasonal sales or data-intensive tasks, AI swiftly boosts computing power and storage, and during off-peak times, resources are scaled down to cut costs.
Harnessing AI’s Power for Cloud Efficiency
Real-world examples demonstrate AI’s effectiveness in cloud optimisation. For instance, AI-powered chatbots can handle customer inquiries independently, reducing the need for human intervention in customer service, and helping customers get the answers they need more quickly. Similarly, recommendation systems use AI to analyse user data stored in the cloud, offering personalised suggestions that boost engagement and conversions.
In industrial settings, AI-powered predictive maintenance systems monitor equipment performance in real-time, predicting and preventing potential issues to enable proactive maintenance and reduce downtime.
Furthermore, AI bolsters cloud security by using advanced systems to detect and respond to threats like malware and unauthorised access attempts. This proactive approach safeguards sensitive information stored in cloud environments, making sure data is kept secure and regulations are fully abided to.
How to Optimise AI to Maximise Cloud Spending Efficiency
Here are three recommendations for using AI to make the most of your cloud spending:
1. Make sure you have clear goals and budgets for using AI in your cloud environment and across your business. This helps focus on specific objectives like cutting costs or improving efficiency, while also keeping spending in check. Use a FinOps approach to regularly check and tweak how you’re using cloud resources and who is or is not using them. Monitor usage closely, spot any inefficiencies, and optimize based on AI recommendations.
2. Evaluate your organisation’s readiness against Microsoft’s metrics – including security and existing M365 adoption – to make informed decisions with confidence.
3. Use AI recommendations to pinpoint where you might be using too many resources unnecessarily in your Azure environment. AI can analyse patterns in usage, predict future needs, and suggest changes like resizing or using different types of instances to save money without sacrificing performance.
The Impact of Microsoft Copilot on Cloud Efficiency
Microsoft is one of the top global cloud providers, with 95% of Fortune 500 companies trusting their business on Azure [6]. One of Microsoft’s offerings is Microsoft Copilot, an AI assistant that interprets natural language commands to help users find information, create content, collaborate, and enhance productivity. With 70% of Copilot users stating that they are more productive and 68% stating that it improves , there are many benefits of using Copilot in business. These include its ability to streamline processes by helping with data navigation, analysis and automation. It also offers coding assistance in GitHub, speeding up development and allowing for better resource allocation.
However, the adoption of Microsoft Copilot comes with its own set of challenges. The cost of providing every employee in a large organisation with a Copilot license or let them keep a license can be substantial, especially if the application is not being used to its full potential. The strategic rollout of Copilot can be a complex and time-consuming process, often leading to slow AI adoption due to a lack of precise guidance and tools for strategic planning. Moreover, maintaining unnecessary Copilot licenses can put a strain on cloud capacity. This can lead to increased cloud spending and inefficient use of storage on cloud environments. Therefore, a well-planned adoption strategy is crucial to avoid these pitfalls and ensure that the benefits of AI are harnessed effectively without incurring excessive costs. In some cases, it might even be more cost-effective to remove Copilot altogether, driving significant cost savings and optimizing cloud storage.
This is just one of many considerations when deciding how to most efficiently use AI within the cloud. 
Originally qualifying as a software engineer, Neil May is an experienced business founder, leader, board member, CEO, and committee member. At Surveil, he specialises in channel partner go-to-market strategy, having scaled companies such as Intel distributors and Microsoft Gold Partners. Neil also excels in cloud technology strategy, corporate governance, and digital transformation.