Brace for cloud price hikes and AI failures amid pressure to modernise

IT infrastructure and operations (I&O) leaders must urgently adopt platform-centric models and brace for significant price hikes in traditional cloud services as hyperscalers look to recoup their massive investments in artificial intelligence (AI).

Speaking at the recent Gartner IT Infrastructure, Operations and Cloud Strategies Conference in Sydney, analysts highlighted a key tension facing today’s IT leaders: managing the hype surrounding AI while maintaining core business operations and curbing costs.

“AI agents are at the very peak of hype, but as always, there are new, hyped-up technologies coming all the time,” said Autumn Stanish, director analyst at Gartner. “And as always, we still have to keep the lights on and do our day jobs.”

Paul Delory, research vice-president at Gartner, noted that while foundational practices such as infrastructure automation and DevOps remain critical, the pressure to innovate is mounting. With cost-cutting remaining the top priority for CIOs in 2026, many are banking on AI to deliver those savings.

However, the business demand for AI is currently outpacing I&O readiness. Stanish warned that half of I&O leaders view integrating AI into their current infrastructure as a top challenge, creating a risk that teams could lose relevance. To prevent a repeat of the loss of control experienced during the initial rush to the cloud, Delory argued that I&O must evolve into a value-driving function capable of delivering AI agents, continuous operations and platform-centric models.

Over the coming year, Gartner advised I&O teams to form dedicated AI centres of excellence and build fully automated delivery pipelines with strict cost controls. Delory pointed out that this is highly achievable within 90 days, as the necessary tools are mostly free and open source. Practical applications for AI agents in I&O today include automatically responding to infrastructure changes by updating scripts and playbooks, training AI to function as quality assurance engineers, and deploying compliance agents trained on human-readable policy documents.

Moving to this platform-centric model requires organisational changes. Stanish suggested creating a dedicated platform team – absorbing traditional server and storage engineers – led by a product owner who aligns the technology directly with user needs. This demands new performance metrics, moving the focus away from foundational baselines such as uptime and towards business outcomes such as revenue growth and customer satisfaction.

Beyond operational structures, the conference also highlighted a crisis in technology procurement. Luke Ellery, vice-president analyst at Gartner, cited a 2024 survey which revealed that 79% of buyers regretted their technology purchases, having either failed to meet expectations or settled for lesser solutions.

To address this, Ellery urged organisations to keep senior-level sponsors engaged throughout the buying process to ensure final purchases align with business needs, rather than allowing procurement teams to simply find a cheaper but unsuitable alternative. Buyers should focus on measurable business outcomes rather than detailed feature specifications, adopting agile and lean procurement methods that allow for iterative refinement instead of rigid waterfall processes.

Furthermore, Ellery advised leaders to embrace risk tolerance thoughtfully – using data to understand risks rather than blindly avoiding them – and to build confidence in supplier negotiations through targeted training and market knowledge.

The risks of poor investment are particularly acute in IT support. Gartner predicts that by 2027, half of all AI projects designed for the service desk will be abandoned due to unforeseen costs, risks, or a failure to achieve projected returns on investment.

To avoid becoming part of that statistic, Gartner director analyst Joe Rogus suggested focusing on easily accessible capabilities within existing software, particularly virtual support agents (VSAs) that can deflect incidents away from human staff.

AI can also heavily support human agents through proprietary knowledge discovery using retrieval augmented generation (RAG), converting chat logs into new knowledgebase articles, and using machine learning for endpoint anomaly response. Furthermore, AI can help to categorise and route tickets, as well as automate case summarisation, provided organisations put in the effort to clean up their existing data first.

Rogus also warned against the growing hype around agentic AI, noting that many suppliers are merely slapping the label on basic automation tools. True agentic AI requires giving systems the autonomy to take action on their own, a step that requires high confidence and rigorous data hygiene.

Looking further ahead to the future of cloud computing, Rogus noted that public cloud spending is expected to exceed $1tn by 2027, heavily driven by AI. However, as hyperscalers pour hundreds of billions into AI infrastructure, they are expected to recoup these costs by hiking prices on traditional cloud services.

To prove the ongoing business value of cloud migrations, Rogus pointed to the rise of AI-infused, industry-specific, composable solutions that move away from siloed infrastructure towards a core layer supporting a data fabric and packaged business capabilities.

This carries major implications for IT strategies leading up to 2030. On the sovereignty front, IT leaders must carefully differentiate between data, operational and technological sovereignty, balancing the trade-offs between global hyperscalers and local providers. Multicloud strategies will also require a rethink, with Gartner predicting that most enterprises will eventually perform intensive AI model activity in one cloud while leveraging it with their data in another.

Sustainability will become a bottleneck, as AI-optimised datacentre racks require significantly more power than traditional servers, potentially tripling energy demand by 2030. Meanwhile, security frameworks will need to evolve from static policies to dynamic, real-time approaches, as AI agents effectively serve as digital workers in the network.

Finally, cloud financial management will become non-negotiable. With AI workloads largely running in containers that are currently vastly over-provisioned, Gartner warned that companies failing to optimise their compute environments face paying up to 50% more than their leaner rivals.