Why are intelligent systems often associated with lack of compliance? Unpack the trade-offs derived from the characteristics of the types of AI systems and explore a potential path to overcome challenging limitations for enterprise adoption. 

After breaking down fundamental concepts and with a broader understanding of AI solutions, you’ll understand the challenges of AI adoption within enterprise environments, and how to strategically combine different AI approaches, instead choosing between them.

To unlock the power of AI and understand essencial concepts and commonly used buzzwords within the AI landscape, refer to the first post of this series, Essential Concepts for Navigating Enterprise AI Trends.

Risks of financial losses, regulatory exposure, brand damage, and flawed operations, are some of the concerns regarding AI innovation, especially in enterprise software. Such risks are imminent when a company opens space to a possible lack of compliance with business policies or governance standards. 

Understanding the reason why these risks exist, and how to overcome such limitations and challenges, is the next step, as it open doors for confidently discussing and driving core systems modernization and AI-driven innovation.

AI in Enterprise Systems: Limitations and Risks

Why would intelligent systems be associated with lack of compliance, you may think. Let’s unpack the trade-offs derived from the characteristics of the types of AI systems, and then, explore a potential path to overcome challenging limitations . 

NOTE: As Generative AI adoption is skyrocketing, let’s limit the scope of this article for learning purposes. If you are interested in learning more about other systems, please let us know. Share your areas of interest, topics or questions you’d like to hear more about! 

AI systems based on statistical predictions, such as Generative AI, can drive disruptive innovation. However, when used indiscriminately, can lead to:

• Factors of unpredictability, and inconsistent processes for decision-making
• Unexpected outcomes derived from hallucination. 
• Inability to ensure repeatable results from a same given set of inputs
• Inability to justify how a decision was made or why a particular response was given. 

Every technology has trade-offs, and software architecture plays a special role in addressing pain points. Let’s understand how. 

Overcoming Trade-offs and Driving Enterprise AI-Innovation 

There’s no need to avoid GenAI and other statistical AI systems. The solution relies on how it is adopted, and which strategies are available to ensure enterprise-grade compliance and predictability.

When tracking which applications would benefit from AI innovation, a cautious analysis should determine which of these applications are core to the business functioning. In other words, there should be a clear understanding of which are the functionalities and associated systems that drive core business operations, that directly involve business logic. As an example, a discount service, or a loan approval service, are examples of solutions that wouldn’t benefit from lack of control and unpredictability. 

TIP: To learn more about the types of AI and their associated outcomes and use cases, check out Architecting Intelligence Beyond LLMs.

So how do you safely adopt AI in such a case? How to overcome the unpredictability of GenAI or the lack of trust on Agentic AI decision-making? 

By designing intelligent solutions that rely on Guardrails, it’s possible to leverage the best out of GenAI and Agentic AI, while ensuring policy compliance and trusted autonomous operations. 

Hybrid Intelligence combines:

• the transparency and control of Symbolic AI, 
• with the creative and generative capabilities of GenAI, 
• and the autonomy and adaptability of Agentic AI.

When exploring the basic concepts, Symbolic AI was presented as being designed to drive efficient execution of reasoning and drive predictable results. Therefore, it fits perfectly as guardrails, the safety net for AI generation, as it can ensure predictable outcomes for operations that demand precision and high levels of control over its reasoning.  

To see a practical hands-on demonstration of how a discount policy sample would work when combining GenAI, like Claude, and Symbolic AI, such as Aletyx build of Drools, check out this short video:

By having a combination of intelligent systems, not only GenAI experience can be significantly improved within enterprise solutions; The level of trust and flexibility offered to AI agents unlocks a higher level of autonomy, where decision-making processes can happen with confidence. 

What’s holding you back from confidently architect your intelligent future? We can help.