All About AI Management Platforms (AI-MPs)

Building a GenAI eco-system using platforms to accelerate delivery and improve efficiency

7/6/20243 min read

In the rapidly evolving landscape of artificial intelligence, organizations are increasingly leveraging Generative AI (GenAI) to drive innovation, improve efficiency, and gain competitive advantages. A significant decision they face is whether to adopt an AI Management Platform (AI-MP) or opt for a self-built solutions. An AI-MP is a no-code platform that allows organizations to build, train and manage their AI agents in a way that is centred around their needs. There are very few of these platforms but most offer tools to build, train, and manage GenAI agents in an LLM (large language model) agnostic manner. The archetype in this emerging industry is the UK scale-up funded by the UK National Technology Officer at Microsoft – Great Wave AI

Here's a look at the pros and cons of using AI-MPs for effective AI management.

Pros of AI Management Platforms (AI-MPs)

1. User-Centric Approach: AI-MPs provide a highly intuitive interface designed for users with varying levels of technical expertise. This democratizes AI management, enabling non-developers to engage in the AI management process without needing deep coding skills. It also provides a single “pane of glass” through which organisations can monitor and manage their AI.

2. No-Code Tools: One of the most compelling features of AI-MPs is the no-code environment. This allows organizations to quickly develop, deploy, and iterate on AI solutions without investing heavily in specialized programming resources. It accelerates time-to-market and enhances agility in AI management.

3. LLM Agnostic: AI-MPs are designed to be LLM agnostic, meaning they can integrate with various language models regardless of the provider. This flexibility ensures that organizations can leverage the best models available and switch providers if necessary without significant retooling, optimizing their AI management strategies.

4. Centralized Management: AI-MPs offer a centralized platform for managing all AI agents, providing clear visibility into their performance and usage. This holistic view helps in monitoring, troubleshooting, and optimizing AI deployments effectively, enhancing overall AI management efficiency.

5. Cost Efficiency: By using AI-MPs, organizations can significantly reduce the costs and risk associated with developing and maintaining custom AI infrastructure. The platform handles much of the heavy lifting, allowing internal teams to focus on strategic AI management initiatives rather than technical details

Cons of AI Management Platforms (AI-MPs)

1. Limited Customization: While no-code platforms are powerful, they may not offer the same level of customization that a self-built AI management solution can provide. Organizations with very specific or complex requirements might find AI-MPs limiting in terms of capabilities and flexibility

2. Vendor Lock-In: Relying on a third-party AI-MP can lead to vendor lock-in, where the organization becomes dependent on the platform provider for updates, support, and continued development. This dependency can be a risk if the provider's direction or service levels change, impacting AI management continuity.

3. Performance Constraints: For highly specialized or performance-intensive applications, AI-MPs might not match the efficiency and optimization achievable through a custom-built AI management solution tailored to specific needs.

4. Data Security Concerns: Using a third-party platform often involves sharing sensitive data with the provider. This raises potential security and privacy concerns, especially in industries with strict regulatory requirements, affecting AI management practices.

5. Integration Challenges: Although AI-MPs aim to be versatile, integrating them seamlessly with existing enterprise systems and workflows can sometimes pose challenges, requiring additional effort and customization in AI management processes.

Conclusion

AI Management Platforms offer a compelling proposition for organizations looking to harness the power of generative AI without the heavy lift of building and maintaining a custom AI management solution. They provide user-friendly, no-code tools that accelerate deployment and are flexible enough to work with various language models. However, they also come with trade-offs in terms of customization, potential vendor lock-in, and security considerations. Organizations must weigh these factors carefully to decide the best approach for their AI management strategy.