CIOs предупреждают: диверсификация облачных провайдеров без единой архитектуры ведет к потере контроля над данными в эпоху ИИ

How CIOs can avoid integration hell as they leverage multi-cloud AI The road to vendor diversification is paved with promise — unless it falls victim to the multi-cloud AI trap. There can be benefits to diversifying cloud vendors to support an AI strategy, but if CIOs and CTOs do not maintain control, they risk making data ungovernable. As AI workloads extend across an increasingly diverse technology ecosystem, sensitive data and operational context move along with them, said Brian Gruttadauria, hybrid cloud CTO at Hewlett Packard Enterprise. "The real cost of vendor sprawl isn't just added complexity; it's the fragmentation of data, context, governance and control at the very moment AI depends on them most," he said. Vendor diversification within a multi-cloud strategy requires distributing workloads among numerous cloud providers, such as AWS, Microsoft Azure and Google Cloud. Diversification is primarily used to minimize vendor lock-in, enhance resilience and redundancy and optimize costs and performance. Unfortunately, when combined with AI, vendor diversification can suddenly turn into a living, breathing hellscape. Signs of misdiagnosing the problem Too many organizations treat multi-cloud AI as an architecture challenge when it's actually an organizational and financial one, said Jesse Dean, CIO at cybersecurity management firm TDI Security. "Driven by a fear of vendor lock-in and enabled by lax governance, companies scatter data and models across multiple clouds," he said. That can dilute engineering talent and drive up long-term costs. Dean said IT leaders need to take a hard look in the mirror to address foundational flaws. "Prioritizing a unified data standardization strategy and committing to a primary-cloud data footprint are key to reducing technical complexity and spend," he advised. The risk isn't that organizations rely on multiple clouds, since many enterprises have valid reasons for doing so, said Ha Hoang, CIO at backup and recovery software firm Commvault. "The risk is allowing each cloud to become its own AI ecosystem with different models, data pipelines, governance policies and developer tools," she warned. Like many IT leaders, Hoang said she believes AI will generate the most value when it can securely access trusted enterprise data and operate consistently across the entire business. If data is fragmented, with security policies differing in each cloud, organizations can end up with disconnected agents, duplicated investments and inconsistent business outcomes. "Multi-cloud should be a deliberate architecture decision — not the accidental result of independent technology choices," she said. The clearest sign of excessive vendor diversification is when data and workloads are no longer consistently visible or governable across environments, Gruttadauria said. "If IT can't see and control an asset, regardless of where it sits, in the cloud — on-prem or edge — that's a sign the architecture has outgrown its governance," he warned. Finding a data fabric escape route The answer to falling victim to the multi-cloud AI trap is a unified data fabric that spans cloud, on-premises and the edge, creating a single operational namespace instead of a collection of disconnected data silos, said Yuri Gubin, CTO at software engineering and IT consulting firm DataArt. "When data, workloads and AI share a common foundation, they remain visible, governed, and portable regardless of where they run." That lets organizations embrace multi-vendor innovation without inheriting the burden of multi-vendor complexity. Start with governance rather than technology, Hoang advised. "Define a small number of approved AI platforms, establish common security and identity standards, and treat enterprise data as a shared asset rather than as something owned by individual clouds or business units." She said IT leaders should also design for portability wherever it makes business sense. "That doesn't mean every workload has to move freely across clouds, but it does mean avoiding unnecessary lock-in for core AI capabilities." That will allow for flexibility that can create a competitive advantage. Keep business objectives in sight The best prevention is creating an enterprise AI operating model before AI adoption scales, Hoang said. "Every new AI platform should meet common standards for security, data access, observability, governance and cost management," she said. It is also important to ensure that every AI investment is tied to a measurable business objective. The goal isn't to support every model or every cloud provider; it's to deliver business outcomes with the least operational complexity.