Skip to main content

What a company needs to be able to deliver Cloud AI Native solutions

By

 Cloud AI-Native delivery means turning AI from a basic demonstration into a scalable platform. This requires modern cloud infrastructure, up-to-date & well-organized data, engineering practices suitable for operating AI at scale, and processes to ensure AI is used safely and responsibly.

So, what does a company actually need to do to make this work?

Build platforms, not just projects

A company must design and build reusable foundations. Reliable frameworks have to support products and teams, rather than creating isolated projects.
This means the company must be able to create reference architectures, standard templates, clear approaches, and clear processes for how teams work. Security, cost control, and operational monitoring must be built into the platform design at the start, not added later.

Modernise applications, not just move them

A company must migrate from lift-and-shift systems to cloud-native ones. This calls for skills in refactoring, containerisation, breaking monoliths, externalising state, and using managed services.
It also means reducing technical debt in a structured way. Many AI initiatives fail because the application layer cannot scale, change quickly, or integrate cleanly with AI workloads.

Modernise data for GenAI

A company must build a trusted data foundation. GenAI does not work with fragmented, outdated, or poorly managed data. The company must know lakehouse patterns, streaming ingestion, metadata, data lineage, and governance.
It must also handle unstructured data optimally. Most enterprise value sits in documents, tickets, and knowledge bases. If the company cannot prepare this content for retrieval, embeddings, and search, then GenAI will stay a demo.

Engineer AI workloads like production systems

The company must be able to run inference and AI workflows as real services. This includes autoscaling, latency management, resilience, versioning, rollback, and proper release processes.
This covers the full lifecycle: training, deployment, evaluation, monitoring, prompt management, and improvement. A company focused only on data science will struggle. AI-native delivery needs a cloud engineering discipline.

Put governance and safety at the centre

The company must be able to deliver AI with trust. This includes security, privacy, compliance, and responsible AI. The company must know how to control access to data, control what agents can do, and keep audit trails.
This is critical because agents can do more than simply generate; they can also take actions. Without guardrails, the risk becomes too high, and the client will stop the program.

Operate with a Build Run Evolve model

You must be able to operate AI solutions over time. AI Native is less stable than classic applications. Models drift, data changes, prompts evolve and the platform must keep up.
The company must have strong DevOps, MLOps, observability, and incident response practices. The company must also be able to create feedback loops from user behaviour and operational signals into the next release cycle.

Manage cost and ROI through FinOps for AI

A company must be able to explain AI costs in business terms. In 2026, clients will ask for cost per interaction, cost per transaction, and ROI. Tokens, GPUs, vector search, and data pipelines can quickly create high spend.
So the company must be able to build cost guardrails, model routing strategies, monitor usage, and track unit economics. Without this, AI adoption will stall when finance teams get involved.

Microsoft tools, frameworks, and resources that map to the 7 strengths

  1. Build platforms, not only projects: Microsoft Cloud Adoption Framework (CAF) and Azure Landing Zones
  2. Modernise applications, not only migrate them: Azure Migrate and Azure App Service Migration Assistant
  3. Modernise data for GenAI: Microsoft Fabric and Microsoft Purview
  4. Engineer AI workloads like production systems: Azure Machine Learning managed endpoints and Azure OpenAI Service
  5. Put governance and safety at the centre: Microsoft Responsible AI Standard and Azure AI Content Safety
  6. Operate with a Build Run Evolve model: Azure Monitor and Application Insights
  7. Manage cost and ROI through FinOps for AI: Azure Cost Management and the FinOps Foundation “FinOps for AI” guidance

Final thought

Delivering Cloud AI-Native requires modernizing cloud and data, engineering AI workloads, managing risk, and securing constant evolution. It is not a single skill but an integrated delivery system.

Comments

Post a Comment

Popular posts from this blog

Why Database Modernization Matters for AI

By
  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...
Post a Comment
Read more

How to audit an Azure Cosmos DB

By
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 a Comment
Read more

[Post Event] Azure AI Connect, March 2025

By
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.
Post a Comment
Read more