The cloud providers have a plethora of AI services now available to you and Gartner has identified AI Governance as a Top Tech Trend for 2025.  It is an area that encapsulates people, process and technology but what does that mean for cloud platform owners? Here are three interconnected priorities to focus on and help prepare for the increased use of AI.

Enterprise Agile (EA) is an agile project management approach tailored to tackle challenges that enterprise-level organisations may face. EA provides practical guidance on how to embrace new working methods and technologies that are proven to increase agility while helping organisations remain efficient.

Understand your stakeholders

As a platform owner you’re very familiar working with stakeholders such as security and development leaders, but you must now broaden your stakeholder management to include engaging with those responsible for Data governance, AI governance and AI ethics. They are three distinct areas and disciplines for good reason, but are all hugely important when it comes to using your cloud platform for AI purposes. Just as you would incorporate security requirements into your platform, you should do the same for data governance, AI governance, and AI ethics. This includes implementing specific controls, configurations, and actionable observability at both the service and platform levels.

Requirements = code

Ensure all your requirements for data governance, AI Governance and AI ethics are represented in code and easily deployable at scale within the platform.  This may mean that you need to bind together technical configurations with process and integration with other tools in your organisation.

Much AI work right now is human led, but if you subscribe to what many predict the future to be, AI will increasingly take on more responsibility in the Software Delivery Lifecycle (SDLC) and you will need to enable AI at scale and this can’t be done without automation and code. 

Develop platform consumption patterns

Recognise the capabilities and the limits of the technology available to you. This chatbot project leveraged Copilot Studio, which was a beta preview at the time it was being developed. Copilot Studio offers a quick, convenient path to a relatively simple AI chatbot solution. However, working with a new tool that is undergoing rapid development had its challenges, as we needed to adapt to changes.

Some AI projects will require a greater degree of technical control. Depending on your requirements, the choice between using pre-built solutions like Copilot Studio or utilising platforms such as Azure AI Studio, Google Vertex AI Studio, Amazon Bedrock or Amazon SageMaker JumpStart should be carefully evaluated. The decision will be based on the factors that are important to your project, such as speed of development, control and risk.

If you can gather requirements from the right stakeholders, evidence alignment to them in the platform via automation and then empower your users within those boundaries, you’ve enabled the use of AI in your cloud platform in a sustainable and scalable way.

Now, what’s next? 

Learn more about how we can help prepare your cloud infrastructure for the future of AI.