AI Becomes Core as 96% of Enterprises Integrate, but Data Readiness Lags

AI Takes The Wheel: 96% of Enterprises Embed AI Into Core Operations

AI has moved from pilot to production. A new global report from Cloudera finds that 96% of IT leaders have embedded AI into core processes, up from 88% last year. 70% report significant success from AI initiatives, and only 1% say they have yet to benefit. The study surveyed over 1,500 IT leaders worldwide and shows a clear shift: AI is now part of the operating model.

"AI has shifted from a strategic priority to an urgent mandate," said Cloudera Chief Technology Officer Sergio Gago. He notes that private AI and GPU-powered generative AI behind firewalls are driving adoption, with governance and trust as non-negotiables.

Why this matters for Operations

Operations teams are now responsible for taking AI from proof-of-concept to reliable, auditable, low-latency services. That means SLAs, cost controls, security, and measurable business outcomes-every week, not just at launch.

• Tie models to clear KPIs: cycle time, forecast accuracy, SLA adherence, first-contact resolution, backlog burn-down.
• Standardize deployment, monitoring, and rollback so AI behaves like any core service.

What companies are deploying

• 60% use generative AI
• 53% leverage deep learning
• 50% utilize predictive models
• 67% feel better equipped to diversify AI, including emerging AI agents

The pattern is clear: multiple AI types working together. For Ops, this means mixed workloads (CPU and GPU), different latency profiles, and unified governance for all models-public, private, and agent-based.

Data architecture is hybrid-and it's the bottleneck

Hybrid is now standard. Organizations blend cloud and on-prem systems primarily to improve security (62%), data management (55%), and analytics (54%). This spreads risk and keeps sensitive workloads close to the data.

The pinch point: only 9% say their data is fully AI-ready. Integration and accessibility slow down deployments. Without clean, governed, well-labeled data, model quality and adoption stall.

Your 90-day action plan

• Prioritize 3-5 use cases tied to hard metrics (throughput, quality, cost-per-ticket, on-time delivery).
• Map data sources for each use case; set data contracts (schemas, freshness, lineage, ownership).
• Stand up a private AI path: network isolation, secret management, and role-based access for prompts, outputs, and embeddings.
• Build a POC-to-production pipeline: feature store, model registry, CI/CD, canary releases, and observability (latency, quality, cost).
• Right-size compute: plan CPU vs GPU capacity, queueing, and autoscaling; apply FinOps guardrails per model and per team.
• Quality and safety gates: human evaluation loops, test sets, red-team prompts, bias checks, and content filters.
• Change management: SOPs, runbooks, and quick training for frontline teams who will use or supervise AI output.

Guardrails you can operationalize now

• Adopt an AI risk framework (roles, controls, evidence). The NIST AI RMF is a solid baseline: NIST AI Risk Management Framework.
• Classify data and implement policy enforcement at the service layer (PII, PHI, PCI).
• Set evaluation pipelines: accuracy, toxicity, groundedness, and hallucination rates per use case.
• Runtime monitoring: drift detection, incident alerts, fallbacks, and safe shutdown paths.

How hybrid shows up day-to-day

• Private generative AI for sensitive data, with GPU pools behind the firewall.
• Cloud inference for bursty, low-risk workloads; on-prem for steady and sensitive ones.
• Unified governance across warehouses, lakes, and streaming systems so models see clean, permissioned data.

Scale indicators to track

• Time from idea to first production release
• % of required data that meets AI-readiness standards
• Model uptime and SLO compliance
• Drift or quality incidents per month
• Cost per 1,000 inferences (and per business outcome)
• User adoption and human override rates
• Benefit realized vs. plan (savings, revenue, or risk reduction)

Context

The findings build on last year's study and were presented at EVOLVE25 NYC. The signal for Operations is clear: AI is now part of the core workflow, and the teams who manage data, uptime, and cost will determine ROI.

Skill up your team

If you need fast, practical upskilling for operators and frontline teams implementing AI, explore targeted learning paths here: AI courses by job role.