AI Teams Cannot Scale in Isolation

Many executive teams ask: How quickly should we expand our AI capabilities? The typical response is to hire more AI/ML engineers, data scientists, or establish a dedicated AI department. While important, this addresses only part of the challenge.

AI does not generate business value simply by increasing headcount. Value arises when models, data, applications, platforms, security, testing, and business processes are integrated. The key question is not just, “How many AI engineers do we need?” but rather, “What organisational structure will turn AI ideas into production business outcomes?”

In many AI initiatives, AI/ML work accounts for only 20–40% of the total effort. The rest of the effort is in data engineering, cloud and platform engineering, automation, software development, QA, security, compliance, monitoring, cost management and business adoption. If an organisation grows AI talent without growing the surrounding capabilities, the bottleneck does not disappear. It only moves somewhere else.

For exploratory work, such as discovery, PoC or model evaluation, the organisation can work with a lighter ratio. A practical planning ratio can be:

1 AI/ML Engineer : 0.5 Platform Engineer : 0.5 Developer : 0.5 QA Engineer : 0.5–1 Data Engineer

This ratio is sufficient when the goal is rapid learning, use case validation, model comparison, data quality testing, and prototyping. At this stage, speed is the priority, and not every component must be production-ready.

However, when moving an AI solution to production, the ratio should change. For enterprise-grade AI, a more balanced ratio is:

1 AI/ML Engineer : 1 Platform Engineer : 1 Developer : 0.75 QA Engineer : 1 Data Engineer

Production AI requires more than just the model. It demands secure environments, automated deployment, observability, data pipelines, integration with existing systems, test automation, performance management, compliance controls, and ongoing ownership after deployment.

The impact of ignoring this ratio can be significant. If the AI/ML team grows from 50 to 150 people, but the platform, data and engineering teams stay almost the same size, the organisation may create a large AI backlog that cannot be delivered. AI teams will produce ideas, prototypes and experiments, but delivery teams will not have enough capacity to industrialise them. The result is delays, rework, higher costs, frustrated teams, and limited business adoption.

Another key consideration for executives is that not every project requires AI. Standard digital initiatives such as cloud migration, platform modernisation, application development, integration, and automation will continue and still require developers, cloud engineers, QA, DevOps, architects, and business analysts. Focusing all growth planning solely on AI may compromise the delivery capacity needed for core business operations.

The AI ratio is more than a team design detail; it is a forecasting tool. It helps leaders determine the necessary supporting capabilities to scale AI effectively, without hindering standard projects or creating delivery bottlenecks.

Successful AI companies are not those with the largest AI departments, but those that understand and build the right operating model. They scale AI in tandem with data, platform, engineering, QA, security, and business transformation.

My takeaways:

Do not scale AI in isolation.
Scale the entire delivery system to realise true business value from AI.

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...

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...

AI ROI without hype: a practical way to measure value using risk adjustment + Azure Copilot example

Most people know what ROI means, but it’s harder to calculate for AI projects. The numbers are less predictable than with traditional platforms because many AI projects never reach stable production. IDC says only about 44% of custom AI apps and 53% of third-party AI apps make it from proof of concept to production. That’s why it’s important to look at ROI through a risk lens, not just cost versus benefit. One useful approach is to use a risk-adjusted formula: AI ROI = (AI Business Value Income / (Initial Investment + Annual Costs)) × Success Probability where, >AI Business Value Income (over N years) Consider a 2 to 3 year period and include both direct and indirect value: Direct: time saved, fewer tickets, higher conversion, lower fraud. Indirect: improved customer or employee experience and quicker decisions. For these, use measurable stand-ins like CSAT, churn, time to resolution, or hours saved, and estimate conservatively. >Initial Investment This covers more than just buil...

Cloud as a Story - Vunvulea Radu

Search This Blog