While offering plenty of advantages, including improved security and scalability, reduced technical debt, and better user experience, infrastructure migration is not just about handling current workloads. On a global scale, it’s about future-proofing businesses for the next wave of technological advancements. Each company follows its own unique path in this direction, which is determined by its initial level of AI maturity.
In this article, we dive into the intricacies of AI-ready infrastructure and explore how organizations at different maturity levels can strategically prepare their technical foundation to not just accommodate AI initiatives but thrive with them.
Impact of infrastructure migration: real-life examples
- Migration to AWS cloud-native infrastructure enabled Netflix to reduce downtime and scale video streaming globally with 99.99% availability.
- After Walmart took the strategic decision to adopt and move to the Microsoft Azure cloud, the company gained a 60% decrease in operational costs and 10 times faster performance speeds.
- By successfully migrating 5 billion notes and 5 billion attachments to GCP in just 70 days, Evernote has achieved greater flexibility, improved speed and reliability, enhanced security, and disaster recovery planning.
Key indicators to measure migration success
McKinsey research shows that over the next three years, 92% of companies plan to increase their AI investments, but only 1% of leaders call their companies “mature”. By mature, they’re referring to a level of AI integration when it is a cornerstone of the workflows and drives substantial business outcomes. At Solvd, we use a high-level maturity model to benchmark your AI journey and understand what pieces you’re missing to become an AI-connected business.
Technical indicators
- System throughput
- Scalability
- System availability
- Deployment time for new models
Business indicators
- ROI of infrastructure migration
- Operational costs
- Time-to-market
- Customer retention rates
AI-specific indicators
- Model training time
- Data access for AI systems
- Capability to work with more complex models
- Number of simultaneously supported models
Your first step: defining AI maturity level
McKinsey research shows that over the next three years, 92% of companies plan to increase their AI investments, but only 1% of leaders call their companies “mature”. By mature, they mean that level of AI integration when it is a cornerstone of the workflows and drives substantial business outcomes. At Solvd, we use a high-level maturity model to benchmark your AI journey and understand what pieces you’re missing to become an AI-connected business.
Solvd’s AI maturity model
| Stage | Level | Characteristics | What it looks like | Typical challenges |
| Awareness | 1 | – Recognition of AI’s potential – Ad-hoc pilots | Leadership recognizes AI’s potential and funds one‑off pilots. | Scattered data, unclear roadmap, limited in‑house skills. |
| Experimentation | 2 | – Multiple PoCs – Small-scale tool evaluations | Several proof‑of‑concepts (PoCs) are deployed across the organization but run in silos. | Lack of integration, ad‑hoc governance, uncertain business value. |
| Expansion | 3 | – Standardized frameworks – Cross-functional deployments | The organization has adopted standard frameworks and established cross-team collaboration on use cases. | Legacy systems struggle under new load; manual processes slow progress. |
| Scaling | 4 | – Production-grade AI – MLOps practices – Automated CI/CD | AI models are successfully operating in production, with automated CI/CD for continuous updates. | Controlling costs, fine‑tuning performance at scale. |
| Optimization | 5 | – Continuous retraining – Self‑healing pipelines | The company has implemented continuous learning loops, maintains self-healing data pipelines, and has fully integrated AI systems with business metrics. | Managing drift, sustaining operations, and ensuring ongoing compliance. |
Self‑assessment questions
- Business outcomes. Do you have clear business goals that you want to achieve? Are you solving existing business problems, trying to achieve your goals, or just experimenting with new technologies?
- Business alignment. Are your AI initiatives tied to clear business objectives, avoiding isolated experiments and driving measurable impact?
- Data strategy. Is your data cataloged, cleaned and accessible for AI workloads?
- Compute readiness. Can you provision GPU/CPU resources with minimal lead time?
- Deployment pipelines. Do you have CI/CD/MLOps in place for model updates?
- Cost controls. Are you monitoring AI workloads and capping runaway costs?
- Governance. Is there an established policy for data privacy, bias mitigation, and compliance?
All these steps are united by one cornerstone—the need to adopt a framework and implement a strategy. It helps ensure efficient operations, minimal downtime, and responsiveness to IT requirements, as well as compliance, ethical, and security concerns. Having a trusted cloud solution partner with in-house AI expertise and the ability to manage the full lifecycle of this core infrastructure is critical to accelerating your journey and capitalizing on this evolution. In our next article, we’ll explore the strategic, not just technical, nature of transitioning to AI infrastructure and determine the best path for your business.
