The Value of AI Quality for Enterprise Ready AI Agents

By Maria Zhang

So great speaking on a panel about Enterprise Ready AI Agents at the Inaugural AI Quality Conference! 

As I was discussing how mission critical Data Quality is to the overall Quality of AI solutions, on AI Quality and Data Quality: the fail state is “garbage in, garbage out.” We absolutely must avoid that situation at all costs! This question of quality sparked healthy debate amongst our panelists, who had different schools of thoughts:

• Supply chain manage your data – keep track of your data every step along the way as if they were valuable goods being shipped from origination to destination with many stops along the way.
• Take a holistic ownership of the data – for example: from generating logs to billing customers based on consumption imputed from the log data.

Regardless of the various methodologies, all the panelists agreed that Data Quality is a cornerstone for Enterprise Readiness. 

How do we define Enterprise Readiness for AI Agents?

To level set, let’s start with “Enterprise Readiness”: 

Enterprise readiness for SaaS (Software as a Service) products is a product’s ability to meet large organizations’ need for security, compliance, reliability, and support. These organizations are typically businesses with over 1,000 employees with complex evaluation, procurement, and decision-making processes.

AI requires “Day 1” Enterprise Readiness. Without that, we will have a hard time imagining adoption at scale in the enterprise world.

Our company is purpose-built for Enterprise Readiness. We are tip of the spear for a vibrant and fast growing market for AI Agents that work in the core of our customers’ businesses.

We call our AI agent the “Employee of the Century.” Why? Because the AI agent can be the best of both the human and machine worlds. On one hand, the agent is on 24/7, has infinite patience, always follows best practices precisely, can learn and adapt through fine tuning, alignment, reinforcement as well as other AI training methods. On the other hand, the agent also has a very high level of EQ. 

We’ve been told our AI model is so human-like that we organically use the term “hiring” when discussing “adoption” for our AI agents.

That being said, there are still risks of employing an AI Agent. Let’s take a look at these potential issues:

• Hallucination – just like any LLM, hallucination is inevitable on certain occasions. But an enterprise ready LLM must avoid harmful hallucinations 
• Misunderstanding – Lack of understanding of business rules in the large language model or other generative AI models.
• Misalignment – offensive, or not in tune with the brand voice. 

This means that in addition to the traditional definition of “enterprise readiness” there are new elements an AI solution developer must be conscious of to prevent these pitfalls. AI developers must take calculated risks to accelerate adoption but be enterprise ready for long term sustained success in generative AI solutions.

Thanks to my fellow panelists Pushkar Garg, Joe Reis, Chad Sanderson, our well-researched moderator Sam Partee and of course, conference organizer Demetrios Brinkman for inviting me to share my thoughts!

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