5 Cloud Strategies For Mission-Critical Data in the Age of AI

5 Cloud Strategies for Mission-Critical Data in the Age of AI

Featuring Dwight Wallace (Silk) and James Norvell (Atos)

1. Managing Cloud Cost and FinOps Optimization

Summary:
Cloud adoption often begins with sticker shock. Many organizations face skyrocketing cloud bills without understanding why. Silk and Atos emphasize embedding FinOps principles from day one, aligning financial and technical stakeholders to manage costs, performance, and governance continuously.

Transcript:

Dwight Wallace: One of the biggest challenges we hear from clients is managing cloud cost. Cloud bills keep rising, and finance teams see overspend without knowing why. Historically, we overprovisioned on-prem—now, in the cloud, you pay for that from day one.

James Norvell: Exactly. Over 70% of organizations struggle with unexpected cloud costs. At Atos, we use a discovery-first approach that integrates FinOps early—before migration even begins. We analyze high-impact workloads and ensure cost governance from the start.

“FinOps isn’t just a post-migration activity—it starts with design and strategy,” says Norvell.

Dwight Wallace: Right. The key is to move into cloud and optimize as you go, not years later. With Silk’s platform, we can control costs and performance from the beginning instead of overprovisioning just to be safe.

James Norvell: And don’t forget licensing—it’s often the biggest cost driver. Reducing unnecessary compute reduces both infrastructure and software license costs.

Dwight Wallace: That’s where Silk helps. By matching performance to application needs, we lower vCPU counts and, in turn, license costs.

Key Takeaway:
FinOps success means governing cost and performance continuously—not as an afterthought.

2. Building a Modernization Roadmap

Summary:
A modernization roadmap isn’t just about moving workloads—it’s about aligning business goals, reducing risk, and preparing data foundations for AI and analytics. Atos and Silk advocate for a discovery-driven roadmap that unifies technical and business perspectives.

Transcript:

Dwight Wallace: Many clients lift and shift, but performance and costs quickly spiral. How should organizations modernize while preparing for AI-driven data growth?

James Norvell: We focus on discovery—aligning workloads to business priorities. What drives revenue? Where’s the risk? Then we design a long-term, AI-ready foundation.

“Modernization is about agility, not just migration,” Norvell explains.

James Norvell: It’s crucial to plan test environments for AI early, not as an afterthought. We design platforms that minimize data sprawl and churn while maintaining high performance.

Dwight Wallace: From Silk’s side, it’s all about risk mitigation. We protect production data so teams can use real data for AI and ML models without impacting operations.

“Once production data is out in AI land, you can’t get it back,” says Wallace.

Key Takeaway:
A strong modernization roadmap protects production systems while laying the groundwork for scalable, AI-ready data operations.

3. Closing the Cloud Performance Gap

Summary:
Many organizations find that cloud performance doesn’t match on-prem expectations. The key is designing for performance parity without overspending. Silk and Atos emphasize optimizing both storage and compute layers from the foundation up.

Transcript:

Dwight Wallace: We often hear that cloud throttles database performance—more CPUs don’t always mean better throughput. How can clients close the gap between on-prem and cloud?

Dwight Wallace: At Silk, we deliver the fastest storage in the cloud. It’s about giving the right performance at the right cost and avoiding unnecessary overprovisioning.

James Norvell: Exactly. Performance risk can cripple things like payment transactions. If you can solve latency and licensing costs together, it’s a game changer.

“Solve performance issues early—don’t let them become an afterthought,” advises Norvell.

James Norvell: During our assessments, we analyze workloads and identify where Silk’s platform can help. Addressing performance at the design phase saves massive cost and downtime later.

Dwight Wallace: Right—building the right foundation is like building a house. You don’t start decorating before the structure is sound.

Key Takeaway:
Cloud performance excellence comes from architecting for balance—aligning compute, network, and storage to business needs.

4. Tackling Copy Data Sprawl

Summary:
Every team wants its own copy of production data—but unmanaged copies multiply costs and risks. Silk and Atos advocate for thin cloning, automation, and lifecycle management to keep environments efficient and secure.

Transcript:

Dwight Wallace: Data sprawl is becoming a major pain point. Each team spins up full copies for dev, test, and analytics, driving up storage costs.

Dwight Wallace: The solution? Platforms that enable instant, zero-cost cloning. You should be able to spin up dev environments instantly, run tests, and shut them down automatically.

“Without data lifecycle management, your storage bills will crush you,” warns Wallace.

James Norvell: Exactly. It’s like having thousands of high-resolution photos you never delete—they just pile up. With the right tech, you only store the bits that change.

James Norvell: Smart tiering is also key—using the right storage class for backups, protection, and active workloads.

Key Takeaway:
Control copy data sprawl with automated lifecycle management and thin cloning to save costs and protect performance.

5. Preparing Infrastructure for AI and Next-Gen Workloads

Summary:
AI workloads consume compute and storage faster than most organizations expect. Atos and Silk emphasize building flexible, scalable infrastructures that can grow and contract with demand—protecting both data integrity and budgets.

Transcript:

Dwight Wallace: AI is here—and it’s devouring compute and storage. Many clients weren’t ready for the cost and complexity.

James Norvell: Right. Gartner predicts that over half of cloud workloads will be AI-related within a few years. That’s why predictive FinOps is essential—anticipating costs before they spiral.

“AI success starts with financial and architectural foresight,” says Norvell.

James Norvell: We help clients design scalable data architectures and use predictive analytics to manage costs while maintaining agility.

Dwight Wallace: And at Silk, we focus on protecting production data from AI agents. By using thin instant copies, we let AI models work on real data safely—without slowing production.

“Build infrastructures that can scale up or down without disruption,” adds Wallace.

Key Takeaway:
AI-ready success demands scalable infrastructure and protective data strategies that prevent corruption and runaway costs.