Unified AI infrastructure: 5 key takeaways for IT leaders

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Imagine 10 AI pilots running across your organization. None of them connect. Security flags three for using unapproved cloud services. Finance can’t track costs. Sound familiar?

In 2024, 78% of organizations reported using AI, up from 55% the year before. But while adoption is surging, many still struggle to move from experimentation to enterprise-wide integration. That problem isn’t technical knowledge. It’s that no one planned for what happens when AI stops being an experiment and starts touching every part of the business.

At our recent SHI Summit — Scaling smarter: Infrastructure for the AI era — IT leaders explored what it truly takes to move AI from pilot to production. From infrastructure hurdles and unexpected costs to the organizational friction that comes with scaling, the discussions revealed hard-earned lessons and practical strategies. Here’s what’s working, what’s failing, and what you need to plan for next.

1. AI applications are interconnected — and infrastructure must be too

AI is showing up everywhere: security operations, customer service, marketing, and beyond. But these tools don’t work in isolation. They rely on shared infrastructure to perform, scale, and stay secure.

Take Kira, SHI’s emotionally intelligent Digital AI Ambassador. She’s not just clever algorithms. She depends on a secure, resilient network (think SASE principles), scalable compute, and a software-defined data center (SDDC) that adapts as demand grows. The same backbone that powers real-time video analytics for surveillance also supports Kira’s lifelike customer conversations.

When infrastructure is unified, every new AI initiative builds on what exists. When it’s not, every initiative becomes a custom integration project.

2. The edge is where AI gets real

AI often starts in the cloud, where experimentation is fast and flexible. But mature use cases move closer to where data is created — the edge. Think smart cameras on a factory floor analyzing defects in real time, or AI-powered kiosks delivering personalized recommendations without round-tripping to a data center.

The hardware shift is already here. Devices now ship with neural processing units (NPUs), AI-powered security features, and intelligent automation baked in. That’s great for performance, but it also introduces risk. Six months into a retail deployment, you might discover half your AI-enabled cameras haven’t received security patches because your management system wasn’t designed for edge AI hardware.

This is the edge AI paradox: the benefits are real, but so are the risks. You’re expanding the attack surface, distributing compute across dozens or hundreds of locations, and managing device types your team has never dealt with before. And the scale is growing fast.

According to IoT Analytics, the number of connected IoT devices is expected to reach 21.1 billion globally by the end of 2025, up 14% from the previous year. That number is projected to hit 39 billion by 2030, with AI acting as a key growth driver.

That’s why hybrid architectures — spanning cloud, on-prem, and edge — are essential. They maintain observability and control while optimizing performance. You can’t manage what you can’t see, and you can’t secure what you can’t update.

3. Scaling AI requires a unified strategy

Moving AI from pilot to production isn’t just about adding compute power. What derails scaling? Bandwidth bottlenecks. Disorganized data. No governance. Suddenly finance, security, and legal are fighting.

The good news: this is solvable if you plan for it.

Industry leaders are showing the way. Lenovo is tackling sustainability with liquid cooling and energy-efficient designs. Cisco is embedding security into every layer of the stack. NVIDIA’s AI Enterprise software runs across major cloud and on-prem platforms, making hybrid, elastic solutions feasible.

For IT leaders, the challenge is weaving these pieces into a single, cohesive strategy that bridges traditional workloads with next-gen applications. When strategy leads, scaling becomes intentional, not chaotic.

4. Security and integration are non-negotiable, and most teams aren’t ready

Three questions that catch most enterprises off guard:

1. Who owns AI security when models run in different environments?
2. What happens when your AI vendor gets breached and your training data leaks?
3. Can you explain to auditors how your AI makes decisions?

If you can’t answer these now, you’ll be answering them during an incident.

AI expands your attack surfaces, creates sensitive data flows, and introduces complex compliance requirements most frameworks weren’t designed for. Security must be foundational, not bolted on later. That means zero-trust principles, encrypted data pipelines, and governance frameworks that span cloud, edge, and on-prem environments.

Integration matters just as much. Your AI systems need to talk to each other — and to your existing infrastructure — without creating blind spots or bottlenecks.

The organizations that succeed treat security and integration as core design principles.

5. Focus on business outcomes, not just technology

The organizations that get AI infrastructure right don’t start with “we need AI.” They start with “we’re losing $2M a year to production defects” or “our customer service costs are growing 15% annually.” Then they ask: can AI help? And what infrastructure does that require?

That discipline matters.

Throughout the Summit, one theme kept surfacing: infrastructure must be flexible, scalable, and aligned with specific outcomes. That means making sure data is AI-ready, endpoints are secure, and systems can adapt as needs evolve. But it also means you can measure whether it’s working.

Typical ROI timeline:

• 6–8 months: operational improvements
• 12–18 months: measurable cost reduction
• 24+ months: new revenue

Anyone promising faster is probably oversimplifying.

Whether it’s deploying digital AI ambassadors, enabling real-time analytics at the edge, or automating quality control on the factory floor, the goal is the same: measurable business value. And that requires more than powerful AI models. It requires resilient networks, seamless integration, and a clear roadmap for scaling.

What it really takes to scale AI

AI is redefining how businesses operate, compete, and grow. But transformation doesn’t happen in a vacuum. Mistakes will happen. The question is whether they’re costly surprises or manageable challenges you’ve planned for.

At SHI, we’ve helped organizations imagine, experiment with, and adopt AI infrastructure. We also know what breaks at scale — and how to get your security, finance, and business teams on the same page. That’s usually harder than implementing the technology.

Our AI & Cyber Labs and Next-Gen Device Lab give teams a secure, hands-on environment to validate solutions before deployment. Whether you’re testing edge devices, optimizing hybrid architectures, or evaluating AI-powered security platforms, our labs are designed to accelerate time to value while minimizing risk.

If you’re navigating this shift and want to talk through what scaling AI looks like in your environment, we’re here to help.

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