Platforms Are The Foundation For AI Success

The Platform Strategy

The AI Wave is an opportunity for businesses to grow. However, many organisations find it hard to realise AI’s potential due to the lack of a solid foundation for their initiatives. Without a platform strategy, AI projects often face scalability issues, inefficient resource utilisation, and difficulty integrating with existing systems.

What are Platforms in IT?

But before we start, let’s explain what we mean by “platforms” in IT. The word “platform” has been used in technology for a long time, but people often don’t fully understand what it means. Simply put, a platform in IT is the underlying hardware or software that an application or service runs on. Think of it as the foundation of your digital house.

TechTarget gives a more formal definition:

“In IT, a platform is any hardware or software used to host an application or service. An application platform, for example, consists of hardware, an operating system (OS), and coordinating programs that use the instruction set for a particular processor or microprocessor. In this case, the platform creates a foundation that ensures object code executes successfully.”  

Here’s what’s important about this definition:

• Hosting applications and services: Platforms provide the environment for your applications and services to run correctly. This separation between the application and the platform is important for flexibility and scalability. Imagine building a skyscraper directly on the ground without a foundation – it wouldn’t work!
• Creating an AI foundation: Platforms act as the base, providing essential resources and infrastructure. They’re like the stage in a theatre; the play can change, but the stage stays the same.
• Specialisation for efficiency: Platforms often specialise in supporting specific types of applications or services. This specialisation helps them perform better and use resources effectively. A general-purpose platform trying to do everything would be like a Swiss Army knife with a thousand blades – too complicated and inefficient.

No Platform, No AI Journey

You might wonder – Why we’re talking about platforms when you’re focused on AI? Well, a strong platform strategy is essential for successful AI use. Think of it this way: AI is like a high-performance race car. However, that car won’t perform at its best without a good racetrack and a skilled pit crew. Similarly, your AI initiatives need a platform foundation to succeed.

Here’s why:

• Flexibility in a dynamic landscape: AI technology changes rapidly. A platform-based approach allows you to adapt to new advancements and business needs. You can change components, upgrade systems, and add new tools without disrupting your AI infrastructure.
• Scalability for growth: As your AI initiatives grow, your platform needs to scale to handle increasing data and processing demands. A well-designed platform provides the scalability to accommodate your expanding AI ambitions.
• Efficiency and cost optimisation: Platforms help you use resources efficiently and avoid being locked into a specific vendor. By simplifying underlying infrastructure complexities, platforms let you focus on your main AI goals.

5 Key Components of an AI Platform

Creating an effective AI platform requires careful planning. Here are five key factors to consider:

1. Data management: AI needs data. Your platform should include strong data storage, processing, and governance capabilities to handle the large amounts of data needed for AI.
2. Compute resources: AI workloads often need significant computing power. Your platform should provide access to scalable compute resources, whether on-premises, in the cloud, or in a hybrid approach.
3. Model development and deployment: Your platform should facilitate AI model development, training, and deployment. This includes tools for model building, version control, testing, and monitoring.
4. Integration and interoperability: Your AI platform should work seamlessly with your existing IT systems and data sources and support interoperability with various AI tools and frameworks.
5. Security and compliance: AI systems often handle sensitive data, making security and compliance important. Your platform should have strong security measures to protect data and ensure compliance with data & AI regulations.

4 Platform Strategies for Success

To use the power of platforms in your AI journey, consider these four strategies:

1. Embrace cloud-native platforms: Cloud platforms offer scalability, flexibility, and access to a wide range of AI services. Consider using cloud-native platforms like AWS, Azure, or Google Cloud Platform to accelerate your AI initiatives.
2. Build a hybrid platform strategy: Combine on-premises and cloud-based platforms. This gives you flexibility and control while taking advantage of the cloud’s scalability and cost-effectiveness.
3. Prioritise open-source technologies: Open-source tools and frameworks offer flexibility, cost-effectiveness, and a strong community for support. Use open-source technologies to avoid vendor lock-in and encourage innovation.
4. Invest in platform engineering: Platform engineering focuses on building and maintaining the infrastructure and tools that help developers build and deploy applications efficiently. Invest in platform engineering to create a self-service platform that speeds up AI development.

Platforms: Your AI Compass

As we continue to explore AI in this blog series, platforms will guide us. In the next post, we’ll discuss AI strategy and how it connects to your platform approach.