AI integration tops cloud priorities for one in three organisations: US 40% vs Europe 28%

AI integration is now the top cloud priority for Operations

Cloud adoption keeps moving fast, and with it come sharper constraints: data privacy, residency, compliance, performance, cost, and security. For Operations, the job is simple to state and hard to execute-deliver efficiency and control without slowing the business.

New research with UpCloud shows a clear shift. Integrating AI into cloud infrastructure and processes has become a core priority, not a side project.

What the 2025 data shows

• 1 in 3 organisations prioritise integrating AI into their cloud stack in the next two years.
• The US leads: 40% call AI integration a priority vs 28% in Europe.
• Leaders (CEOs, CTOs, tech leads) are more likely to prioritise AI: 36% vs 27% for others.
• Right behind AI integration: scalability (32%) and performance (30%).

How teams are enabling AI workloads:

• 56% are training developers on cloud-based AI tools and infrastructure.
• 55% are adopting AI platforms and services from cloud vendors.
• 51% have added AI-specific security and compliance measures.
• Only 11-14% have no plans for these activities-AI is moving into the mainstream.

What this means for Ops leaders

• Capacity planning changes: GPUs, fast storage, and network throughput become daily constraints. Plan queues, quotas, and multi-region burst.
• Scalability and performance: autoscaling for variable AI workloads, right-sizing for inference vs training, and latency budgets by use case.
• Cost control: track cost per training hour, cost per 1K tokens/inference, and GPU utilisation. Enforce budgets and kill switches.
• Security and compliance: apply guardrails for data residency, PII handling, and model/feature governance. Map to GDPR where relevant (EU GDPR).
• Vendor strategy: compare US and European CSPs for data locality, pricing, SLAs, and compliance. Maintain exit plans to avoid lock-in.
• Observability for AI: logs, metrics, traces, model drift monitoring, and dataset lineage. Treat models as production systems, not experiments.

How teams are supporting AI workloads (and how to operationalise it)

• Upskill your builders: standardise training on cloud AI services, MLOps, and security. Define skill paths for Ops, platform, and developer teams. If you need structured learning, see AI courses by job and popular AI certifications.
• Adopt cloud AI platforms with guardrails: use managed services where they cut time-to-value, but document data flows, retention, and model endpoints.
• Apply AI-specific security: content filtering, prompt/input validation, output checks, key management, and isolation for sensitive workloads.

A practical 90-day playbook

• Week 1-2: Prioritise use cases
  • Pick 2-3 high-impact, low-risk workflows (support triage, internal search, document summarisation, anomaly alerts).
  • Set success metrics: cycle time, ticket deflection, CSAT, cost per task.
• Week 2-4: Architecture and data
  • Choose model approach (vendor API vs managed open-source) and region strategy for residency.
  • Define data contracts, PII handling, and retention. Document lineage.
• Week 3-6: Security and compliance
  • Threat model prompts, data exfiltration, and model abuse scenarios.
  • Map controls to your framework (e.g., NIST AI RMF).
• Week 4-8: Platform and observability
  • Set up CI/CD for models/pipelines, feature stores if needed, and tracing for requests and tokens.
  • Track cost per inference, latency, error rates, and drift alerts.
• Week 6-10: Pilot
  • Run a gated rollout with audit logs, human-in-the-loop, and rollback plans.
  • Collect user feedback and compare metrics to baseline.
• Week 9-12: Scale and standardise
  • Codify templates, policies, and playbooks. Bake into your platform.
  • Review costs, SLAs, and capacity. Adjust quotas and routing.

Metrics that keep you honest

• GPU/accelerator utilisation, queue wait time, and failed job rate.
• Time-to-deploy model changes and rollback time.
• Latency p95/p99 and cost per 1K tokens or request.
• Model drift frequency and data quality incidents.
• Compliance audit pass rate and number of policy exceptions.

Risks to watch

• Data leakage through prompts, logs, or third-party endpoints.
• Model drift reducing accuracy without clear alerts and retrain schedules.
• Supply constraints for GPUs affecting timelines and SLAs.
• Vendor lock-in that blocks portability or cost negotiation.
• Shadow AI tools bypassing governance. Close the gap with approved options and fast onboarding.

Methodology note

Findings come from an online survey conducted in May 2025, reaching 300 professionals involved in selecting and purchasing cloud services. Respondents work in organisations with five or more employees across Europe (55%) and the US (45%).

Next steps

• Align AI projects with clear Ops metrics and budgets.
• Build the minimum platform to run AI safely: identity, secrets, observability, and cost guards.
• Upskill your teams to reduce handoffs and blockers-review the latest AI courses to speed onboarding.

AI in the cloud is no longer experimental. With a clear plan, Ops can ship value fast, control risk, and keep costs in check.