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Where Enterprise AI Runs: Deliberate workload placement across cloud, on-premises, and edge

This BARC study is based on 329 international respondents. It shows why the cloud is the default environment for AI today, why it is not the right place for every workload, and how data and AI leaders can place workloads deliberately across cloud, on-premises, and edge environments.

What this BARC study answers for data, analytics, and AI leaders.

  • Which criteria decide whether an AI workload belongs in the cloud, in your own data center, or at the edge?
  • Which internal and external drivers are putting cloud-only strategies under pressure, from data sensitivity to customer and contract requirements?
  • What does control really cost? And how do greater complexity and savings on predictable workloads balance each other out?
  • What separates AI leaders from laggards in architecture decisions, and how does hybrid AI architecture become an operating model with clear ownership and decision logic?

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“BARC surveys give me a practical overview of what’s out there in the market and what you don’t normally hear about data, BI and analytics in your day-to-day life”.

Pietro Grammatico, Director Service Management BI, Vorwerk

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Further information

The study is based on a quantitative online survey conducted in June and July 2026. It draws on 329 qualified responses from international decision-makers and subject matter experts across all relevant industries and company sizes.

The report shows the current state of AI deployment models and analyzes the drivers, realized benefits, and challenges of deployments outside the cloud. It examines both dimensions of cost (consumption and control economics), the roles of edge AI and hybrid architectures. Four practical recommendations help organizations classify their workloads, add a control layer to cloud-first AI, build non-cloud capabilities where the case is clear, and run hybrid AI architecture as an operating model.

For 76% of organizations, the importance of deploying AI workloads outside the cloud has grown over the last 12 months. This study gives you the basis to place workloads deliberately, before data sensitivity, regulation, or provider dependency makes the decision for you.

Author(s)

Timm Grosser
Senior Analyst Data & Analytics
Timm Grosser is a Senior Analyst Data & Analytics with a focus on data management.
Florian Bigelmaier
Analyst Data & Analytics
Florian Bigelmaier is an analyst with a focus on data management and data intelligence.

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