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5 metrics that show cloud modernization unlocks AI value

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 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 The main obstacle is outdated cloud infrastructure, which impedes the use of AI.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.

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