Old Infrastructure - New Intelligence

Your current infrastructure wasn’t built for AI.

But your future business outcomes probably depend on it.

Across every industry, enterprise leaders are being asked to deliver AI-driven insights and automation—on top of legacy systems that weren’t designed for today’s demands. Full-scale rip-and-replace projects are costly, slow, and risky.

That’s why forward-looking organizations are choosing to retrofit instead.

Why Retrofitting Beats Rebuilding

Retrofitting means selectively modernizing—layering AI capabilities into existing systems using cloud-native architecture. Done right, this approach is faster, more cost-effective, and better aligned with real-world business constraints.

With the right design principles in place, enterprises can:

• Add GPU-based acceleration without disrupting core systems

• Modernize storage using AI-optimized object stores

• Deploy pre-trained large language models (LLMs) with services like Amazon Bedrock

• Implement ML Ops overlays that work across hybrid and multicloud environments

The common denominator? Cloud-native infrastructure—modular, API-driven, and designed to bring AI to your data, not the other way around.

Real-World Example: Scaling AI Without Starting Over

I recently came across a relevant business use case - an insurance company wanted to deploy AI to automate claims processing. Their data remained on-prem, with no immediate plans to migrate.

Instead of delaying innovation, they deployed a cloud-native AI layer using AWS tools for document ingestion, NLP, and automation. Data stayed compliant and in place. Workflows improved. Costs dropped. And the time-to-value was measured in weeks—not quarters.

That’s the power of a retrofitted approach.

From Strategy to Execution:

RADIUS and DEVSHOP from Insight

For enterprises unsure where to begin—or how to move forward—Insight provides a structured path:

RADIUS from Insight

A rapid, Insight-led discovery engagement that aligns business goals to cloud capabilities in just days. We identify high-impact opportunities, prioritize use cases, and deliver a clear roadmap that accelerates buy-in, de-risks delivery, and often unlocks AWS funding for MVPs or PoCs.

DEVSHOP from Insight

Once strategy is set, DEVSHOP enables execution. Through embedded co-development sessions, platform enablement, and cloud-native delivery patterns (like GitOps, Infrastructure-as-Code, and CI/CD pipelines), DEVSHOP helps your dev teams build smarter, faster, and more securely on AWS.

Together, RADIUS + DEVSHOP help enterprise organizations move from “we’re interested in AI” to “we’re delivering value from it”—all without needing a complete overhaul.

What To Ask Yourself

If you’re exploring AI adoption within a legacy environment, start with these questions:

• Are we thinking cloud-natively, or trying to force-fit AI into yesterday’s architecture?

• Do we have a plan to enable and support our internal engineering teams?

• What’s the cost of delay vs. the cost of getting started with a retrofit?

Final Thought

AI is a journey—not a lift-and-shift.

The most innovative enterprises aren’t ripping and replacing. They’re extending, modernizing, and scaling what they already have.

Insight’s RADIUS and DEVSHOP offerings are helping clients do just that—bringing business outcomes to the forefront, one use case at a time.

If you’re wondering how to get started with enterprise AI infrastructure, let’s connect.

Want to learn more?

Send me a message to explore a RADIUS assessment.

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