Run your cloud services as software to stop scaling technical debt

AI has exposed a fundamental rupture in how enterprises run cloud: it can no longer be treated as infrastructure or plumbing. The operating models built around PMO oversight, migration waves, and infrastructure-first governance are breaking under the weight of AI, regulatory pressure, and sustainability requirements. To build AI systems they can trust, CIOs must run cloud services as software that is versioned, automated, and continuously modernized. Enterprises that fail to make this shift will not scale AI, but instead run into technical debt.

A continuously modernized cloud core is key to scaling AI

AI transformation dominates the headlines, but it is continuous cloud modernization (see Exhibit 1) with a multi-cloud core, not episodic migration, that determines whether AI can scale responsibly. Enterprises are expanding hybrid infrastructure, data engineering, cybersecurity, and AI-infused operations because these capabilities form the technical foundation for AI readiness. This shift in investment reflects a deeper truth: AI requires an always-modern cloud core that keeps trust, data, and speed tightly connected. Without that, AI initiatives will stall under reliability gaps, data fragmentation, and governance failures.

Exhibit 1: Cloud-led modernization remains the engine room for enterprise tech spending

Based on inputs from 505 IT and business leaders with Global 2000 enterprises
Source: HFS Research, 2025

Confidence, not cost, defines cloud maturity

The first decade of cloud centered on cost and speed. The new decade demands confidence. Boards expect cloud investments to demonstrate tangible trust, resilience, sustainability, and AI performance outcomes. This shift is driving the rise of total cloud value orchestration, a management model that links cost, carbon, and confidence telemetry into a unified control plane. Supporting disciplines such as FinOps 2.0, GreenOps, and TrustOps are evolving to provide continuous measurement and optimization across financial, ESG, and engineering performance.

Without cloud confidence, AI confidence collapses. Outages become AI failures. Weak governance becomes model drift. Fragmented landing zones become compliance exposure. CIOs can no longer separate cloud strategy from AI strategy because they now share the same operating model.

Enterprises that pursue a confidence-based cloud maturity will measure their environment by the trustworthiness of data and AI governance, the resilience of primary plus multi-cloud architectures, carbon per workload, and the sovereignty of data, models, and policies.

Unlocking AI confidence demands a shift from project to product

Traditional PMO structures and infrastructure-centric governance slow AI more than they support it. They are built for one-time delivery cycles and static control gates that do not match the speed or uncertainty of AI systems. Enterprises need to run cloud as software. This requires cross-functional cloud product teams that own, release, and continually upgrade internal cloud services.

In practice, a cloud services as software operating model includes the following capabilities:

• Productized landing zones that are versioned and upgraded regularly
• Lifecycle-managed modernization packs aligned to business capabilities rather than infrastructure silos
• Containerized workloads and microservices that provide portability and policy enforcement across primary and secondary clouds
• AI-led operations that automate reliability, scaling, and cost per inference.
• TCVO dashboards that provide unified telemetry for spend, sustainability, and AI performance
• Evergreen funding models that reinvest value into continuous modernization

In this model, architecture, data, and compliance become versioned business controls that evolve with every release. Cloud becomes the operating system for AI (see Exhibit 2).

Exhibit 2: From cloud as a service to cloud services as software—the evolution of the enterprise cloud operating model

Source: HFS Research, 2025

Cloud services as software requires an ecosystem engineered for confidence

Enterprises can’t build cloud services as software alone. The operating model depends on a multi-layer ecosystem that behaves more like a coordinated software delivery system than a traditional supply chain. Hyperscalers, neo-cloud providers, sovereign cloud operators, FinOps and TrustOps platforms, AIOps and observability stacks, industry cloud builders, and lifecycle partners each play a role in transforming cloud from a utility into a versioned, governable, AI-ready software platform (Exhibit 3).

Exhibit 3: Enterprise guidelines for selecting and integrating cloud partners in the cloud-services-as-software ecosystem

Source: HFS Research, 2025

Leading enterprises prove that cloud running as software delivers speed, resilience, and trust

The shift from cloud projects to cloud products isn’t a theory. It’s already visible across leading enterprises that treat cloud as a software system for innovation, compliance, and sustainability.

• Capital One transformed IT into a cloud-native product organization, using automated governance, continuous telemetry, and open-source remediation frameworks (such as Cloud Custodian) to run cloud as software. Its operating model demonstrates how cloud-native governance unlocks both velocity and control.
• BP uses cloud-based platforms, digital twins, and operational telemetry to modernize critical operations and optimize energy efficiency. By integrating cloud, real-time data, and sustainability metrics, BP shows how heavy-asset enterprises can run modernization as a software-driven system.
• Pfizer built a cloud-based R&D environment through its AWS PACT initiative, accelerating analytics, compliant data workflows, and AI experimentation. This demonstrates how regulated enterprises can use cloud as a software-defined platform for scientific and operational innovation while maintaining audit integrity.

These examples demonstrate that cloud is no longer just an infrastructure consumption model, but a software-defined operating platform where governance, telemetry, and AI readiness determine enterprise advantage.

The Bottom Line: Scaling AI requires a fundamental shift—cloud must be managed as software or it becomes a factory for technical debt.

Scaling AI demands a reset: cloud must be run as software, not infrastructure. CIOs need to treat it as a continuously evolving product across core, edge, and AI environments—versioned, governed, observable, and always improving. Those who make this shift will start operating a living cloud system built for AI.