Success Story: AI Provisioning with Microsoft Azure and Container8

Transforming AI Service Delivery in the Digital Age

Discover how a corporate enterprise transformed their AI service delivery model, dramatically reducing provisioning times from weeks to mere hours by leveraging Microsoft Azure and the Container8 Internal Developer Platform. This significant transformation not only streamlined IT processes but also boosted productivity and satisfaction among both AI service users and engineers.


Results at a Glance


Agility in deploying AI services is crucial for maintaining competitive advantage. Container8, powered by Microsoft Azure and Jira Service Management, has been pivotal in this strategic shift, simplifying the AI deployment workflows and empowering developers (and other AI users) to focus on innovation rather than operational logistics.

This platform has allowed developers to kickstart their projects with ease, providing essential tools and real-world examples to streamline the process. This case study will delve into how Container8 has redefined the customer’s experience, addressing their unique challenges, deploying strategic solutions, and achieving impactful outcomes, ultimately underscoring the role of advanced technology in enhancing productivity and fostering innovation.

About the Customer

The IT department at our customer is at the forefront of the company’s digital transformation efforts. As a key player in transitioning from traditional manufacturing to a tech-driven enterprise, the department leads the shift towards “Technology as a Service.” This involves an array of services from AI as a Service (AIaaS) to more traditional SaaS and PaaS offerings, underpinning the company’s innovation in smart home technologies.

The department provides a comprehensive tech platform for developing, deploying, and managing AI-driven applications and services across the organization, playing a crucial role in digitalization and enabling customizable features for smart appliances.

Initial Situation

Before the implementation of the new system, setting up and deploying AI services was a cumbersome and slow process. Developers and project managers had to navigate through a complex, manual procedure that began with extensive forms filled out to request AI service provisioning. This process involved multiple departments and was fraught with potential for delays and errors, with each AI service request taking weeks to fulfill.


The primary challenges in IT operations included:

Communication Gaps: Significant barriers between development and operations teams hindered effective communication and slowed down project delivery.

Delayed AI Service Deployment: The manual processes involved in setting up AI services led to prolonged deployment times and frequent project overruns.

Complex Approval Processes: Obtaining the necessary approvals for new AI services was a protracted affair, involving multiple layers of bureaucracy.

Data Model Mismatch: Existing models only leverage public data and don’t work for the company’s data.

Un-trainable Model: Public AIs can’t be customized to full extend and produce only general output.

Limited Model Future: It’s not clear how the roadmap of public AIs looks like. Therefore, changes may affect the usage negatively.

Chat Only Interface: For use cases other than content creation, analysis, etc. public AIs don’t work as desired. Technical users also need programmatic access, not just a chat interface.

These challenges created bottlenecks in productivity, delaying AI project deliveries and causing widespread inefficiencies across the enterprise.

Approach and Solution

To address these issues, the company adopted a streamlined approach by implementing Microsoft Azure integrated with the Container8 Internal Developer Platform. This solution automated the AI provisioning process, which was previously manual and time-consuming.

The integration directly into Jira Service Management facilitated efficient handling of AI provisioning requests. This automation significantly cut down the time required to set up AI services, eliminating many of the manual steps and reducing the reliance on extensive documentation.

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The self-service for AI has drastically changed how we approach our work at BSH, allowing us to start developing new open use cases very fast, and with a high degree of flexibility and customization of AI.

Cloud Engineer at our Customer


The implementation of this Internal Developer Platform led to substantial improvements:

Reduced Provisioning Time

The time to provision AI services was dramatically reduced from weeks to under two hours.

Enhanced AI Transformation

This efficiency boost accelerated the company’s AI adoption and transformation initiatives.

Increased User Satisfaction

The streamlined process improved satisfaction levels among internal users of AI services.

Innovation Acceleration

By minimizing time-consuming administrative tasks, the system enabled quicker innovation cycles within the company.

Reduced Workload for Engineers

The automation relieved engineers from repetitive setup tasks, enhancing job satisfaction and productivity.

Overall, the introduction of Microsoft Azure and Container8 revolutionized AI service provisioning within the company, marking a significant leap forward in their digital transformation journey.

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