AI projects stalled? Five ways to scale for real business value

Artificial intelligence (AI) is no longer just a shiny new toy for enterprises. For IT leaders in hypergrowth companies, the focus has shifted to demonstrating real value. Many organizations have moved beyond pilots and demos, with AI models now being actively deployed into production.

But here’s the hard truth: Most AI projects fail to deliver tangible ROI because they can't scale effectively.

The gap between a successful pilot and an enterprise-wide solution is often a chasm of data silos, security bottlenecks, and infrastructure limitations. You need a strategy that turns AI potential into business performance.

Scaling AI, whether to cut costs or drive innovation, requires a new approach to your data foundation. Here are five ways to move from “AI-curious” to “AI-driven.”

If your data is fragmented, your AI is flying blind.

Data silos hinder AI scalability by trapping critical information in disconnected systems, limiting model accuracy, causing bias, and slowing innovation.

How do fragmented data environments impact AI performance?

Don't let data wrangling slow you down. When data scientists spend weeks hunting for data, productivity plummets, and AI models train on incomplete information, leading to misleading results.

To scale AI effectively, you need an intelligent data infrastructure. This creates a unified, logical view of your data, no matter where it's stored. By breaking down silos, you give your AI models the high-quality data they need to perform, transforming data from a headache into your most valuable asset.

Security cannot be an afterthought. In the rush to adopt GenAI and Large Language Models (LLMs), many organizations inadvertently expose themselves to new risks.

The nightmare scenario? Your proprietary code, customer PII, or internal strategy documents leaking into a public model. Or, conversely, malicious actors are poisoning your training data to manipulate your AI's behavior.

Is it possible to innovate with AI without compromising security?

Yes, by adopting a “secure-by-design” approach. Treat your AI data with the same governance as your financial records. Start with robust access controls and encryption at the storage layer. Then, focus on model governance: manage who can prompt the model and keep inference data off the public internet via private endpoints. By applying a Zero Trust architecture to your AI infrastructure, you verify every request. This gives your team the freedom to innovate boldly within strong, reliable guardrails.

In the world of AI, latency is the enemy. Sophisticated algorithms are useless if your infrastructure can’t feed data to GPUs fast enough. Idle GPUs waste money and destroy ROI due to storage bottlenecks. Why is storage performance critical for AI workloads?

AI training and inference demand massive throughput. Traditional storage often chokes under this pressure, turning hours of work into days.

To scale, you need high-performance flash storage that keeps up with your GPUs. But speed is only half the story; you also need data mobility. Moving data seamlessly from the edge to the cloud removes friction from your pipeline. By optimizing for performance, you accelerate time-to-insight and stay ahead of the competition.

The AI landscape changes rapidly. A rigid infrastructure is a liability.

Today, you might be using Llama 3 on premises. Tomorrow, you might need to burst to the cloud to access H100 GPUs for a specific training run. Next year? Who knows. Locking yourself into a single cloud provider or a specific hardware configuration limits your options and drives up costs.

How does hybrid cloud infrastructure support AI scalability?

A flexible, hybrid multicloud approach lets you place data and workloads where they make the most sense—based on cost, performance, or compliance. You could keep sensitive data on premises for security while using cloud compute for model training, or use different clouds for different stages of the AI lifecycle.

True flexibility comes from a consistent data plane across these environments. When data management is the same everywhere—on premises, in AWS, Azure, or Google Cloud—your teams can pivot quickly without retraining staff or refactoring apps. That agility is essential for staying competitive.

Finally, we must talk about cost.

AI is resource-intensive, and unmanaged cloud costs can quickly escalate. Startups and enterprises often overspend by treating AI like an unlimited resource. To drive value, AI must be approached with careful optimization.

What are the best practices for managing AI costs?

Efficiency is about maximizing resources. Start with smart data tiering—move cold data to lower-cost storage to cut costs without losing access. Use technologies such as deduplication and compression to reduce storage requirements. On the compute side, gain visibility into usage to avoid over-provisioning and costly idle instances. Rightsize your infrastructure to ensure every dollar spent on AI drives results.

Move fast, but build strong

Scaling AI isn't magic. It's an engineering challenge that requires a solid foundation. By breaking down silos, embedding security, accelerating performance, embracing flexibility, and optimizing costs, you build an environment where AI can actually work. You move from interesting experiments to mission-critical applications that drive growth.

The technology is ready. The question is, is your data infrastructure ready to support it?

Explore these five essential strategies to scale AI effectively. Download our guide now.