TL;DR:
The Big Five consultancies are taking different approaches to distributed AI inference in 2025: McKinsey focuses on hybrid deployment models, PwC offers a three-tiered portfolio approach, BCG emphasizes people and processes (70% of their framework), Deloitte champions “split inference” for on-device processing, and KPMG prioritizes governance and security. All recognize that bringing AI computation closer to data sources reduces latency, cuts costs, and improves privacy—transforming healthcare, media, and other industries through real-time decision-making at the edge.
The Shift to Intelligence at the Edge
The strategic deployment of distributed artificial intelligence (AI) inference is rapidly becoming a cornerstone of digital transformation across industries. By 2030, “60-70% of all AI workloads will be real-time inference,” according to McKinsey, creating an “urgent need for low-latency connectivity and compute” infrastructure to support this paradigm shift. 
Distributed AI inference brings processing capability closer to where data is generated—whether at the edge, on devices, or in regional data centers – rather than sending everything to centralized cloud systems. This approach directly addresses the growing challenges of high latency, escalating operational costs, and data privacy compliance risks that come with traditional cloud-based processing.
As organizations navigate this transformation, the Big Five consultancies have developed notably different approaches to guiding enterprise clients. Each focuses on distinct aspects of the distributed inference value chain, from technical architecture to governance frameworks.
Strategic Priorities: People vs. Technology
BCG: The Human Factor in Distributed AI
BCG approaches distributed AI inference through their proprietary 10-20-70 framework, which emphasizes algorithms (10%), technology and data (20%), and people and processes (70%). This people-centric approach recognizes that successful distributed AI inference requires significant organizational transformation beyond the technical infrastructure.
Their implementation methodology revolves around three interconnected value plays: deploy, reshape, and invent (DRI). The “deploy” stage focuses on getting employees using AI technologies quickly, which they consider “a critical first step towards realizing value from GenAI.”
McKinsey: Accelerating Adoption Through Infrastructure
In contrast to BCG’s people focus, McKinsey emphasizes infrastructure and adoption metrics. Their analysis shows AI use increasing from 50% in 2024 to 71% in 2025, while generative AI jumped from 33% to 65% during the same period.
McKinsey frames distributed AI as “distributed AI compute,” emphasizing workloads executed at edge sites instead of centralized data centers. The firm advocates for hybrid deployment models, balancing centralized and distributed AI components for optimal flexibility.
Their research demonstrates the business case: telecommunications companies could see a 6-14% return on invested capital by offering distributed “GPU-as-a-service” for AI inference at the edge.
Architectural Approaches: From Edge to Enterprise
Deloitte: Split Inference for Device-Level Processing
Deloitte has been an early proponent of distributed inference, using the term “split inference” to describe partitioning AI workloads between edge devices and cloud servers. Their 2025 Technology, Media & Telecommunications Predictions report explicitly calls out split inference as a way to “address privacy and security issues” by keeping personal data at the source.
Deloitte forecasts that 30% of smartphones and 50% of PCs will have local AI processing capabilities by the end of 2025. They emphasize that edge and cloud “go hand in hand,” with each use case dictating how much processing stays local versus in the cloud.
PwC: Tiered Portfolio Strategy
PwC advocates a three-tiered portfolio approach to AI implementation that includes distributed inference capabilities:
- Small-scale “ground game” wins for immediate impact
- Mid-level “roofshots” focused on operational enhancements
- High-risk “moonshots” aimed at revolutionary innovations
The firm recognizes that data centers are shifting “closer to local customer demand” as real-time AI needs grow. Their approach is distinctive in its emphasis on energy implications, highlighting power efficiency needs for distributed AI nodes as part of the ROI calculus.
According to PwC’s predictions, competitive advantage will derive less from the specific tools employed and more from how organizations leverage their proprietary data within distributed architectures.
Risk Management and Governance
KPMG: Security-First in Distributed Environments
KPMG approaches distributed AI inference through the lens of data governance and risk. Their 2024 technology survey found that as AI spreads to the edge, data is “being distributed more widely,” heightening privacy concerns and management complexity.
The firm advises on frameworks for deploying AI inference across decentralized architectures safely, with strong guardrails at each step. Their AI Quarterly Pulse Survey indicates that 51% of organizations are exploring AI agents, 37% are piloting them, and 12% have already deployed them – many requiring distributed inference capabilities.
KPMG’s significant investments include a $100M partnership with Google Cloud to accelerate enterprise AI adoption, emphasizing that security, compliance, and cost-effectiveness must guide distributed AI implementations.
Industry Transformation Through Distributed Intelligence
The shift to distributed inference is enabling real-time decision-making across multiple industries:
Healthcare: When Milliseconds Matter
In healthcare, distributed AI enables real-time image processing and remote care. AI models scanning medical images at the bedside can flag critical findings in seconds rather than sending data to a cloud and back. Edge AI for medical imaging has shown diagnostic throughput increases of 5x with 20% reductions in manual review labor.
As Deloitte notes, healthcare organizations leverage distributed inference for applications “where milliseconds matter, and downtime could be life-threatening.” These systems continue analyzing data even during network interruptions, maintaining patient privacy through localized processing.
Media & Entertainment: Personalization at the Edge
Media companies use distributed inference to deliver personalization with minimal latency. Streaming providers employ edge AI for real-time video enhancement and contextual ad insertion during live events, creating richer viewer experiences without the buffering that comes with cloud-based processing.
“Technology, media, and telecom” companies use edge-based recommendation engines to increase streaming engagement through instant, location-tailored content suggestions, according to industry analysis.
Beyond Entertainment and Healthcare
The impact extends to other sectors:
Transportation: Autonomous vehicle systems use edge inference for real-time decision-making, avoiding the fatal delays that could come with cloud processing.
Manufacturing: Industrial systems benefit from distributed inference that operates independently from internet connectivity, maintaining closed environments while leveraging AI for quality control.
Retail: Retailers consolidate compute resources at the “metro edge” to serve multiple stores within proximity, delivering sub-10ms latency for customer applications while reducing infrastructure costs.
Technical Implementation: The Key Decision Points
Organizations implementing distributed AI inference face four critical decisions:
System Architecture
Most effective implementations use hybrid cloud-edge architectures with containerized AI microservices. Split-processing balances workloads between devices and edge servers, with Deloitte noting that “split learning and split inference” techniques optimize both latency and resource utilization.
Edge Infrastructure
Success requires specialized hardware (from GPUs in edge data centers to AI chips in devices), high-speed connectivity, and orchestration platforms. Container management and MLOps pipelines that push model updates to all edge nodes are essential for maintaining this distributed fabric.
Model Optimization
Resource-constrained environments need specialized techniques: quantization, pruning, and distillation create edge-friendly models that maintain performance. Unlike training, inference “must operate continuously, on-demand, and often with ultra-low latency,” necessitating constant optimization.
Security Framework
Distributed inference demands endpoint security, in-transit encryption, and continuous verification of edge nodes. KPMG recommends zero-trust architectures where every node’s activity is verified, with governance policies dictating what data can be processed locally and for how long.
Executive Implementation Roadmap
The Big Five consultancies’ collective guidance suggests a clear implementation path:
Start Small, Scale Fast
Begin with pilot projects in latency-critical areas that demonstrate immediate value. As Deloitte advises, “don’t wait too long to start – companies that wait could risk falling behind,” but pace investments so lessons from early deployments inform subsequent steps.
Cross-Functional Teams
Form “AI strike teams” that bring together data engineers, cloud architects, domain experts, and security officers. KPMG’s research shows that leading AI companies aggressively raise internal AI literacy alongside technology investments.
Budget for the Full Journey
Account for both capital expenses (edge devices, network upgrades) and operational costs (cloud fees, maintenance, personnel). A detailed cost-benefit analysis should quantify expected revenue gains or cost savings against implementation expenses.
Set Realistic Timelines
McKinsey notes that achieving scale in AI is often slower than anticipated. A full enterprise rollout typically requires 1-3 years, with 6-12 months needed for proof of concept. Build in time for integration with legacy systems and model refinement as real-world edge data arrives.
The Path Forward: Distributed Intelligence
The approaches of the Big Five consultancies reveal a fundamental truth about distributed AI inference: it’s not merely a technical adjustment but a strategic realignment of where intelligence lives within organizations and their ecosystems.
McKinsey’s adoption metrics show the acceleration. PwC’s tiered approach balances immediate wins with long-term transformation. BCG reminds us that people and processes represent 70% of successful implementation. Deloitte’s split inference architecture places processing where privacy demands it. KPMG ensures that distribution doesn’t compromise security.
For organizations navigating this landscape, success means aligning distributed inference capabilities with specific business objectives while addressing technical, organizational, and governance considerations in equal measure. The future belongs to enterprises that don’t just deploy AI at the edge, but reimagine their operations around intelligence that’s as distributed as their data and customers.
This article was compiled based on research from multiple industry sources and consultant publications as of April 15, 2025.
