5 metrics that show cloud modernization unlocks AI value

Many organisations struggle to get value from AI, even after moving to the cloud. The main obstacle is outdated cloud infrastructure, which impedes the use of AI. Only with a modern cloud foundation can AI deliver real and lasting business value.

But there is one big question that always comes up when people consider investing in modernisation: “How can we show the business value in a simple way, not just with technical terms?”

In this post, I will share five metrics we often use with clients. These are easy for non-technical leaders to understand and clearly show how updating the cloud helps unlock AI’s potential.

1. Customer-Facing Throughput

First, this metric shows how many customer requests, predictions, or transactions the system can handle in a short period. If an AI recommendation service slows down or cannot scale, customers notice the impact right away.

Modernising the cloud increases throughput by allowing systems to scale and process data faster. This results in a better user experience and greater growth opportunities.

2. Service Reliability

Next, AI workloads introduce new errors: model issues, slow searches, missing context, and failing APIs. Error rate shows how often users experience problems. With a modernised cloud, error rates drop. The platform stabilises, making it easier to monitor and fix, thereby building trust in digital services.

3. Infrastructure Efficiency

Another area to consider is that it is common to see cloud costs rise while systems do not deliver more value. Often, CPU or GPU resources are underutilised. Machines run idle or are over-provisioned just in case they are needed.

Modernisation increases efficiency with autoscaling and better data flows. Organisations pay less for equal or better performance, freeing budget for AI.

4. Deployment Frequency

Similarly, AI requires frequent updates as models and behaviours change. Slow release cycles erode AI value. Deployment frequency tracks how quickly new features and model versions are released. Modernisation accelerates updates, increasing business agility and innovation.

5. Cost per Transaction

Finally, this is one of the most important metrics for leaders. It shows how much each customer interaction or prediction costs. If this number is too high, AI cannot grow in a cost-effective way.

Modern cloud improves unit economics by efficiently using resources, optimising pipelines, and running workloads cost-effectively—linking technical gains to profit.

Final Thought

All these metrics highlight an important point: the value of AI does not start with the model, but with the cloud platform. When we modernise applications, data, and operations, AI finally gets the environment it needs to run quickly, reliably, and cost-effectively.

Cloud modernisation is a technical change, but, more importantly, it is a business accelerator for AI.

Why Database Modernization Matters for AI

When companies transition to the cloud, they typically begin with applications and virtual machines, which is often the easier part of the process. The actual complexity arises later when databases are moved. To save time and effort, cloud adoption is more of a cloud migration in an IaaS manner, fulfilling current, but not future needs. Even organisations that are already in the cloud find that their databases, although “migrated,” are not genuinely modernised. This disparity becomes particularly evident when they begin to explore AI technologies. Understanding Modernisation Beyond Migration Database modernisation is distinct from merely relocating an outdated database to Azure. It's about making your data layer ready for future needs, like automation, real-time analytics, and AI capabilities. AI needs high throughput, which can be achieved using native DB cloud capabilities. When your database runs in a traditional setup (even hosted in the cloud), in that case, you will enc...

How to audit an Azure Cosmos DB

In this post, we will talk about how we can audit an Azure Cosmos DB database. Before jumping into the problem let us define the business requirement: As an Administrator I want to be able to audit all changes that were done to specific collection inside my Azure Cosmos DB. The requirement is simple, but can be a little tricky to implement fully. First of all when you are using Azure Cosmos DB or any other storage solution there are 99% odds that you’ll have more than one system that writes data to it. This means that you have or not have control on the systems that are doing any create/update/delete operations. Solution 1: Diagnostic Logs Cosmos DB allows us activate diagnostics logs and stream the output a storage account for achieving to other systems like Event Hub or Log Analytics. This would allow us to have information related to who, when, what, response code and how the access operation to our Cosmos DB was done. Beside this there is a field that specifies what was th...

[Post Event] Azure AI Connect, March 2025

On March 13th, I had the opportunity to speak at Azure AI Connect about modern AI architectures. My session focused on the importance of modernizing cloud systems to efficiently handle the increasing payload generated by AI.

Cloud as a Story - Vunvulea Radu

Search This Blog