Despite heavy investment, 95% of generative AI (GenAI) pilots are failing1 and, at the same time, more than 50% of businesses report moderate to severe technical debt that’s expected to reach 75% by 2026 as AI intensifies pressure on already strained systems2. These failures aren’t about AI being too complex. Instead, they stem from the same root issue: AI adoption and cloud modernization have been treated as separate initiatives rather than as parts of a single, unified transformation. When infrastructure evolves on one track and AI on another, neither can deliver full value.
AI Isn’t Creating Chaos; It’s Exposing It
As businesses test AI, they will often surface issues that have been years in the making: sprawling applications across multiple environments, legacy VMware estates disrupted by ecosystem changes, data scattered across systems that break retrieval-augmented generation (RAG) accuracy, shadow AI introducing governance and privacy exposure, and unpredictable cloud costs driven by mismatched workloads.
Each of these challenges is manageable on its own. Together, and especially when AI is integrated on top, they compound. AI requires clean data, predictable performance, and strong governance, and neglecting these needs will expose weaknesses that traditional infrastructure obscures. This is the truth about why so many pilots fail to make it into production. The barrier isn’t the AI itself; it’s the foundation underneath it.
Two Roadmaps: One Outcome Problem
Many organizations run two disconnected agendas: cloud modernization on one track, AI experimentation on another. Different owners and timelines create the conditions for the AI technical debt trap. Strategically, the relationship is clear: AI is a force multiplier that depends on, and accelerates, infrastructure modernization. When the two move independently, the results are, unfortunately, predictable: AI without access to the correct data, infrastructure that can’t absorb new workloads, governance gaps from shadow AI, duplicated platforms that inflate costs, and an absence of KPIs that prevent clear ROI.
The bottom line: AI’s success depends on the strength of the underlying infrastructure.
The Unified Roadmap: Stabilize, Optimize, Modernize
Expedient’s Intelligent Infrastructure strategy addresses the split-roadmap issue by merging both into one continuum:
1. Stabilize: Fix what’s fragile
- VMware disruption risk (contract cliffs, licensing changes)
- Hyperscale cost variability
- Shadow AI and unmanaged model use
- Expedient Bridge Program
- Cloud rebalancing assessments
- Expedient Private Cloud + Disaster Recovery as a Service from Expedient
- Secure AI Gateway for safe, governed AI access
2. Optimize: Build an AI-ready core that’s consolidated, visible, and right-sized:
- Aligning workloads with their optimal environment
- Integrating data sources for cleaner AI retrieval
- Establishing consistent governance and cost controls
- Right-sized Expedient Private Cloud for predictable performance
- Managed Public Cloud for variable workloads
- Data integration and enterprise observability within Expedient AI CTRL Platform
3. Modernize: Plug AI directly into the business
- Agentic workflows
- Process automation
- Contextual, data-grounded chat interfaces
- Domain solutions for HR, finance, sales, and service
- Expedient AI CTRL Platform
- Secure AI Gateway as the AI "on-ramp" with multi-model access
- Integrated pipelines to bring proprietary data into AI safely
Why a Unified Approach Outperforms Every Piecemeal Strategy
When companies adopt a unified roadmap that combines cloud and AI, they achieve results beyond what isolated projects can offer:
- Complete visibility over infrastructure, data, and AI use by eliminating blind spots, governance issues, and uncontrolled model deployment
- Reduced total ownership costs by placing workloads where they are most economical, not just where legacy choices dictated
- More precise AI outcomes by integrating and governing rich, private data effectively
- Security and compliance aligned with business risks by removing shadow AI to enforce policies and safeguard data
- Quicker realization of value by designing AI and infrastructure to work together, not compete
This showcases Expedient’s Intelligent Infrastructure approach: AI depends on properly built infrastructure, and infrastructure must remain current to meet AI’s needs.
The Most Important Lesson: You Don’t Fix AI at the AI Layer
When the underlying environment is fragmented, fragile, or governed inconsistently, no AI model—not ChatGPT, Claude AI, or even an enterprise LLM—will deliver the results you expect. Remember that AI is an outcome of modernization, not the next step after modernization. The same strategy unlocks both.
Take the First Step: Explore Your Intelligent Infrastructure Roadmap
If you are experiencing stalled pilots, fragmented data, unpredictable cloud costs, or mounting pressure to “show AI progress,” it’s time to step back and realign to a single strategy. Expedient can help you map a unified path across:
- Cloud rebalancing
- Data integration
- AI governance
- Modernized private cloud
- Agentic automation
All under a single, outcome-driven Intelligent Infrastructure roadmap.
Let’s connect the dots. Schedule an Intelligent Infrastructure discovery session with Expedient and explore Expedient AI CTRL Platform today.
Sources
- MIT, The GenAI Divide: State of AI in Business 2025, Jul 2025
- Forrester, Predictions 2025: Technology & Security, Oct 2024
