Like just about everything else in the IT business, cloud computing is seeing a rejuvenation of software and support in 2025, driven by the fast-growing deployment of generative artificial intelligence (AI) (generative artificial intelligence (genAI). Businesses are witnessing notable improvements in performance as their own applications and cloud providers integrate new bots and AI-based infrastructure to smooth inefficiencies and aid developers in building new services.

Across the industry, the integration of genAI is no longer a far-off concept but a present-day reality, pushing the boundaries of what's possible within the cloud ecosystem. This article identifies key trends shaping this evolution, highlighting the opportunities – and challenges – that lie ahead for organizations.

Here are some market numbers that illustrate this trend: According to Nutanix's Seventh Annual Enterprise Cloud Index – which last fall used U.K. researcher Vanson Bourne's survey of 1,500 IT and DevOps/platform engineering decision-makers around the world – more than 80% of organizations already have implemented a genAl strategy of some sort. Moreover, only two percent of companies admit that they have not started planning their genAl strategies.

That said, implementation targets are all over the board. Most organizations believe genAl solutions will help improve their teams' levels of productivity, automation, and efficiency, and there's nothing out there to disprove that contention.

Meanwhile, as real-world genAl use cases currently gravitate toward customer support and experience solutions using bots, most organizations say they are planning to apply genAl solutions to cybersecurity and data protection workloads in the near future.

Smarter infrastructure, leaner operations

One of genAI's most significant impacts is on the foundational layers of cloud infrastructure and management. Intelligent automation is central to most strategies, with AI algorithms now embedded in cloud management tools. These sophisticated systems are optimizing resource allocation in real time, accurately predicting demand fluctuations and automating routine operational tasks. The results? IT managers are seeing tangible gains in efficiency and significant reductions in operational expenditure, allowing businesses to allocate resources more strategically.

Furthermore, the security posture of cloud environments is being fortified by AI and machine learning (ML). Advanced threat detection systems, capable of identifying and responding to anomalies instantaneously, are becoming the norm. Predictive security analytics are proactively identifying potential vulnerabilities, while the adoption of zero-trust architectures and the increasing relevance of post-quantum cryptography underscore the commitment to robust protection in an evolving threat landscape. Industry leaders like Microsoft, with its Defender for Cloud, are already demonstrating the power of AI in mitigating security risks proactively.

The rise of intelligent cloud services

Beyond infrastructure, genAI is fueling a new generation of cloud-based services. Cloud providers are democratizing access to cutting-edge AI capabilities by offering a growing portfolio of pre-trained genAI models and intuitive APIs. This empowers businesses, regardless of their in-house AI expertise, to leverage these tools for a diverse range of applications. From generating compelling marketing content and crafting hyper-personalized customer experiences to accelerating data analysis and uncovering hidden insights, the possibilities are vast.

The emergence of sophisticated AI agents and assistants is also changing business operations. These intelligent entities can manage complex workflows, automating intricate business processes, and providing always-on customer and employee support across multiple channels. Imagine customer service interactions handled with consistent accuracy and personalized attention, freeing up human agents to focus on more complex issues. Bots are doing this every day and not fouling up as often as many naysayers thought they would.

Even fundamental tools such as enterprise search are being revolutionized. AI-enhanced search systems now allow users to navigate vast repositories of internal data using natural language queries, and image, audio, and video inputs, leading to more intuitive and efficient information retrieval.

Challenging conventional norms for data security and privacy

A whopping 95 percent of respondents in the Vanson Bourne study agreed that genAI is changing their organization’s priorities, with security and privacy becoming a primary concern. More than 90 percent of enterprises say data privacy is a priority for them when implementing genAI solutions. However, a staggering 95% of respondents still believe their organization could be doing more to secure its genAI models and applications. Security and privacy will remain a major challenge for organizations as they seek to justify the use of emerging genAI-based solutions and ensure that they adhere to traditional security norms, and new requirements for data governance, privacy, and visibility.

Data transformed: from raw to insight with AI

The convergence of genAI and cloud services is also starting a data management and analytics revolution. AI-powered analytics tools are enabling real-time processing of massive datasets inside large language model (LLMs), resulting in powerful predictive capabilities and automating analytical workflows. This translates directly into more informed and agile decision-making across organizations, analysts say.

Genai is also facilitating synthetic data creation. This approach helps overcome hurdles related to data privacy regulations, data bias, and limited data availability, all of which are crucial for training good AI models. In addition, the increasing adoption of scalable, high-performance, and cost-effective cloud-native data warehouses – such as Databricks and Snowflake – provides the foundational infrastructure for advanced analytics and the delivery of real-time business intelligence.

Architecting for agility and scale

The underlying architectures supporting these advancements are also evolving. Hybrid and multicloud strategies are gaining traction as businesses seek greater flexibility, vendor diversification, cost optimization, and enhanced resilience. Simultaneously, sophisticated tools for orchestrating, monitoring, and securing workloads across these heterogeneous environments are becoming increasingly sophisticated.

The integration of edge and cloud computing is also proving to be a critical enabler, particularly for applications requiring low latency and real-time inferencing. AI workloads can now dynamically shift between the edge for immediate processing and the cloud for computationally intensive model training. Furthermore, the continued rise of serverless computing empowers developers to focus solely on code development, accelerating deployment cycles, ensuring inherent scalability, and optimizing cost efficiency by eliminating the need for infrastructure management.

Collaborative ecosystem fuels innovation

Major cloud providers do not solely drive the advancement of genAI in the cloud. Open-source projects are playing a vital role in democratizing access to genAI technologies, fostering its usual ecosystem of innovation and actively working to address biases in AI development. Moreover, the “bring-your-own-AI” trend – which is another version of shadow IT – is gaining momentum, allowing organizations to integrate their custom-built or preferred AI models into existing cloud platforms, enabling greater customization and alignment with specific business objectives.

New problems that need to be solved A transcendent era for intelligent cloud computing

• While integrating genAI into cloud services presents a wealth of opportunities, it also brings forth new power challenges. The immense computational demands of genAI are driving up the need for high-performance infrastructure, particularly GPUs and TPUs from providers such as Nvidia, AMD, Intel, and Groq, and consequently, increasing energy consumption within data centers.
• New security measures are required in the face of increasingly sophisticated AI-powered cyberattacks.
• A persistent talent gap in cloud computing and AI/ML expertise poses a significant hurdle for many organizations.
• Finally, ensuring the responsible and ethical deployment of genAI, addressing inherent biases, and navigating the broader societal implications of these technologies are critical considerations that demand careful attention.

This year is pivotal in the evolution of cloud services, with genAI acting as a catalyst for development, only three years after its commercial introduction in October 2022. This convergence is reshaping every facet of the cloud ecosystem, from the underlying infrastructure and security protocols to the delivery of intelligent services and tailored industry solutions.