The Road to Successful Gen AI Programs: Overcoming Hurdles and Building a Platform for Innovation

The growth in the generative AI era has been marked by progress, setbacks, and challenges for companies looking to harness the power of this technology. As organizations navigate the complexities of gen AI, they often encounter two major hurdles that can impede their development efforts:

• Failure to innovate: Many teams struggle with process constraints, lack of focus, and cycles of rework that hinder innovation. A significant amount of time is spent on compliance requirements and re-creating experiments, rather than solving valuable problems. This leads to duplicated work, one-off solutions, and a lack of real value being unlocked.
• Failure to scale: Enterprises often face risk concerns and cost overruns when trying to move from prototype to production with gen AI applications. Security and reputational risks can become obstacles to scaling solutions that show potential value.

These issues can derail gen AI programs, preventing companies from capturing the intended value and fostering innovation. However, it is possible to overcome these hurdles by building a platform that enables both innovation and risk management.

Our experience working with companies across industries has shown that successful gen AI platforms typically include three core components:

1. A Self-Service Portal

A self-service portal is essential for enabling developers to access tools and services securely and efficiently. This portal should facilitate developer enablement by providing access to validated gen AI products and capabilities, as well as management services for observability, analytics, and budget controls. By offering a user-friendly interface and a library of documentation, developers can quickly build and deploy gen AI solutions while adhering to compliance and security standards.

2. An Open Architecture for Reusable Gen AI Services

To achieve scale and maximize reuse, companies should focus on developing an open modular architecture that allows for the integration and swapping out of reusable services and capabilities. This approach can reduce the total cost of ownership and enable companies to leverage best-in-class capabilities from various providers. By building reusable gen AI application patterns and data products, as well as common libraries for gen AI applications, organizations can streamline development and reduce duplication of efforts.

While it may be tempting to rely on a single provider for all gen AI services, companies are better served by leveraging a mix of providers to access a wide range of capabilities. By building a platform that centralizes validated services and assets, companies can accelerate innovation, manage risk, and drive value creation with gen AI.

The Importance of an Open Architecture in Gen AI Platforms

As organizations continue to invest in artificial intelligence (AI) technologies, the need for scalable, flexible, and secure platforms to support these initiatives has become increasingly important. In particular, the emergence of generative AI (gen AI) platforms has introduced new challenges and opportunities for businesses looking to leverage AI capabilities at scale.

For this reason, the gen AI platform should focus on enabling integration, configuration, and access through an open architecture. This approach allows organizations to build and deploy AI solutions more efficiently, while also ensuring that they can adapt to changing business requirements and technological advancements.

1. Building Blocks of an Open Architecture

The core building blocks of an open architecture are infrastructure as code combined with policy as code. This allows for changes to be made at the core level and adopted quickly and easily by solutions running on the platform. The platform should offer libraries and component services supported by a clear and standardized set of APIs to coordinate calls on gen AI services.

2. Automated, Responsible AI Guardrails

To mitigate risk, manage ongoing compliance, and provide cost transparency, the gen AI platform should implement automated governance guardrails. These guardrails ensure that AI models are developed and deployed in a responsible manner, following ethical guidelines and regulatory requirements.

One effective way to implement these guardrails is through a centralized AI gateway service that manages access to approved AI models, provides cost attribution, and logs all interactions for analysis. This approach helps organizations track and monitor AI usage, ensuring that models are used appropriately and ethically.

3. Accelerating Innovation and Scale

By implementing an open architecture and automated governance guardrails, organizations can accelerate innovation and scale their AI initiatives. This approach allows application teams to focus on building products and services, rather than dealing with security and compliance issues.

For example, a large oil and gas company was able to accelerate the provisioning of new gen AI environments from over six weeks to less than a day by implementing a gen AI platform with automated guardrails. This approach also reduced approval processes by as much as 90 percent, enabling teams to quickly validate applications and services.

FAQ

What is an open architecture in the context of gen AI platforms?

An open architecture allows for integration, configuration, and access through standardized APIs, enabling organizations to build and deploy AI solutions more efficiently.

How can automated governance guardrails help organizations manage AI risk?

Automated governance guardrails ensure that AI models are developed and deployed in a responsible manner, following ethical guidelines and regulatory requirements.

Conclusion

Implementing an open architecture and automated governance guardrails in gen AI platforms is essential for organizations looking to accelerate innovation, operate at scale, and avoid common technical pitfalls. By focusing on integration, configuration, and access through an open architecture, organizations can build and deploy AI solutions more efficiently, while also ensuring compliance and responsible AI practices.

Ultimately, the payoff of investing in a gen AI platform with an open architecture and automated guardrails is significant, allowing organizations to capture the promise of AI and drive business value in a rapidly evolving digital landscape.