Yet, a critical gap exists between AI ambition and technical readiness. The truth is, most existing technology stacks were not built for the demands of modern artificial intelligence.
Our guide "Modernize For AI, With AI" lays out the steps to achieve true AI readiness utilizing a strategic approach across three interconnected dimensions of data, applications and infrastructure – the essential prerequisites for successful enterprise AI deployment required to build an AI-ready organization.
VML's "Unlocking the Power of Digital Transformation" survey reveals that a surprising 61% of business leaders admit their current infrastructure is inadequate to support their enterprise AI goals. Without fundamental AI modernization driven by the unique requirements of intelligent technologies, digital transformation AI initiatives risk staying stuck in pilot mode. To achieve AI at scale, organizations must prioritize their technical foundation AI.
The truth is that AI isn't just demanding modernization – it's also becoming the most powerful tool for modernization. This creates a powerful, virtuous cycle: you modernize your foundation to enable AI, and then use AI to modernize faster, cheaper, and with less risk. Organizations recognizing this loop early will gain a significant competitive advantage in their legacy systems modernization journey.
Ready to kickstart the engine of your AI-powered future? Download our guide and contact our experts for a bespoke assessment today.
Strategy Director
dti digital - A VML Company
According to VML's survey, 61% of business leaders admit their current infrastructure is not adequate to support their AI ambitions. Our VML whitepaper highlights that existing legacy systems were not built for the unique demands of AI at scale, such as real-time data, modular applications, and unpredictable compute needs. Without a modernized technical foundation, AI initiatives often get stuck in pilot mode because the underlying data, applications, and infrastructure cannot support production-grade AI, leading to a significant gap between AI ambition and technical readiness.
An AI-ready data foundation moves beyond passive storage to active intelligence. It starts with a unified platform, typically a data lakehouse, to eliminate data silos and create a single, governed source of truth. This foundation requires automated pipelines for real-time data delivery, vector databases for advanced AI functions, and a robust model serving infrastructure. Our VML survey found 62% of leaders say data silos significantly hinder transformation, making data modernization – particularly establishing an AI-ready data foundation – the single biggest enabler for successful Enterprise AI.
While traditional application modernization focuses on decomposing monoliths and exposing functionality via APIs, agent-ready architecture introduces an architectural layer specifically for AI. It designs applications to support interactions with autonomous AI agents that can reason and take actions. This means creating APIs that agents can easily discover and call, implementing orchestration layers to coordinate multiple agents, and utilizing emerging protocols like MCP. This shift is crucial for businesses to move beyond basic application updates and build systems truly capable of supporting advanced Enterprise AI and intelligent interactions.
Our VML whitepaper describes a "virtuous cycle" where AI-assisted development becomes a powerful tool for modernization. AI-powered code analysis tools can map dependencies across complex codebases in hours, significantly reducing the risk and cost associated with changing legacy systems. Furthermore, AI-assisted code generation, guided by architectural patterns, can compress development timelines by 40% to 50%. By embedding these tools from day one, organizations can achieve AI Modernization faster, cheaper, and with less risk, transforming the challenge of legacy systems modernization into a competitive advantage.
