AI innovation is nonstop. Your cloud foundation should be too.

Stabilizers (~60% of companies) are mostly stalled in their cloud journeys: cloud strategies aren’t aligned to business goals so initial efforts halt, draining trust. Legacy systems, partial automation and weak observability slow releases and turn every change into a risk. Budgets favor keeping the lights on, not moving the business forward.

The opportunity is practical: refocus cloud as a cash-and-capacity unlock. Modernize a few visible systems, make value measurable in real time, cut incidents and cost and rebuild momentum step-by-step.

Actions to take

• Tie business value to your cloud posture: Translate immediate business needs and long-term growth objectives to clear modernization targets, and establish governance to anchor cloud decision-making to business value.
  
  
• Design a modern enterprise architecture foundation: Choose AI-ready compute, data, security and platform services, set up secure landing zones and organize data so models can always find the right signals fast.
  
  
• Modernize across the continuum: Build modern capabilities across on-premises, hybrid and multi-cloud environments; adopt Agile and DevOps and establish a modern data foundation.
  
  
• Go all-in on full-stack FinOps: Make cloud spend transparent across multi/hybrid estates, with real-time visibility tying every deployment to measurable business value.
  
  
• Boost observability and security: Build systems with real-time metrics that create a feedback loop for improving AI readiness. Secure data, applications and AI workloads across the cloud landscape, establishing end-to-end visibility and clear access management.

Optimizers (about a third of companies) have completed core migrations and built stable cloud estates, but they’re designed for continuity, not innovation. Automation is shallow, AI use cases support work but don’t transform it and value tracking is fuzzy, leaving finance and tech misaligned. Data challenges including security and compliance, sprawl and integration limit AI from scaling.

The goal for Optimizers is to break incrementalism: tie cost, performance and intelligence to outcomes, rebuild one revenue-critical journey end-to-end, and turn a solid foundation into a repeatable engine for innovation.

Actions to take

• Embed intelligence on a composable AI platform: Assemble solutions using reusable data products, pipelines, guardrails and templates and unify data on a governed cloud layer with business context.
  
  
• Use cloud and AI capabilities to accelerate modernization: Modernize applications incrementally with APIs and event-driven architectures and accelerate the process with AI tools. Introduce AI FinOps to manage the value of AI workloads as they scale.
  
  
• Drive human-led autonomous operations: Deploy AI-enabled predictive observability and intelligent automation to proactively manage system health.
  
  
• Make security automatic and AI safe from the start: Embed security and responsible AI governance using Zero Trust principles and approved cloud architectures.
  
  
• Build AI-powered teams, not rotating crews: Establish dedicated, cross-functional AI teams to sustainably build and manage AI systems. Equip them with AI assistants and invest in upskilling.

Innovators (8% of companies) are moving fast from local AI use cases to enterprise-wide reinvention. They’ve mastered pilots, cloud-native patterns and AI experimentation—now they need to redesign core processes and business models with AI in the workflow. Therein lies the challenge: full data and AI integration is still elusive, and automation has not yet reached its peak.

The opportunity now is to hardwire AI into core workflows, unify data flows and target board-level outcomes—new revenue, margin lift and market share—turning hard-won cloud progress into a compounding AI advantage.