Why a solid framework is the key to success in AI customer service

It’s a common misconception that artificial intelligence (AI) can solve every customer service problem on its own. The reality is that AI is only as effective as the framework that supports it.

Take a recent case involving Air Canada, where the airline was ordered to pay damages to a passenger after an AI agent gave incorrect information about bereavement fares. This costly mistake underscores a critical lesson: without clean data and clear processes, even the most advanced technology can fail.

“AI can’t make decisions on its own without clear guidelines or case-specific data,” cautions Guillaume Luccisano, CEO of Yuma AI. “Without the right context, it risks making errors that could easily be avoided.”

In this blog, we’ll explore why accurate data, well-defined workflows, and ongoing human oversight are essential pillars of effective AI ticketing systems. We’ll also show how Yuma AI’s approach ensures precision and reliability throughout the customer experience (CX).

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Debunking the myth of autonomous AI

While AI is an incredibly powerful tool, its performance is only as good as the information it’s given. Human input is vital to ensuring that it accurately understands customer queries and delivers appropriate responses.

Simply feeding AI large volumes of historical data doesn’t guarantee optimal outcomes. If that data is inconsistent, incomplete, or filled with errors, the AI will replicate those same mistakes.

“Just because there’s a lot of historical tickets doesn’t mean the AI will automatically perform well,” explains Luccisano. “If those tickets were mishandled or the data was compiled incorrectly, you’re setting the AI up to repeat the same errors.”

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To avoid these common pitfalls, businesses should consider the following when implementing AI ticketing systems:

• Provide comprehensive, accurate, and well-organized data: The quality of input data directly affects the quality of the AI’s output.
• Establish clear guidelines and use cases: Ensure the AI understands when and how to respond, and regularly monitor its performance.
• Work closely with your AI partner: Proper collaboration can prevent missteps in deployment and ensure smooth integration into existing workflows.

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How poor data hygiene undermines AI

Imagine this scenario: an e-commerce business relies on AI to automate its ticketing process. However, the historical data fed into the system is riddled with inconsistencies, incomplete details, and improperly tagged tickets. When a customer files a support request, the AI struggles to identify high-priority issues or provide relevant solutions, leading to frustrating, irrelevant responses.

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Or, take another hypothetical: an AI system is trained using outdated return policies, but the company has since made changes. Without current information, the AI continues to provide incorrect details about shipping times and refunds. This miscommunication not only frustrates customers but also forces them to seek help from human agents, defeating the purpose of AI.

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These examples reveal the real risks of poor data hygiene:

• Confused interactions: AI systems can misinterpret requests if they’re trained on outdated or incomplete information.
• Incorrect responses: Flawed data leads to inaccurate answers, causing customer dissatisfaction.
• Inability to prioritize: Inconsistent tagging and categorization prevent AI ticketing systems from recognizing the urgency of specific queries.

To avoid these outcomes, businesses must focus on providing clean, organized, and up-to-date data. Regular audits, proper tagging, and clear guidelines are essential steps.

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The challenges of maintaining clean data in AI

Data within a business comes from numerous sources, each with its own format and level of accuracy. Without a clear strategy, this information can quickly become fragmented, reducing AI’s effectiveness.

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Organizations often run into a few common roadblocks:

• Data fragmentation: “How do we ingest as much information as possible about the product, the business, the industry — really anything relevant to the output?" asks Luccisano. "How do we store it in a way that’s useful and well-organized? And how do we get AI to understand an organization’s processes?” These questions are at the heart of maintaining clean data for AI ticketing systems. If businesses don’t prioritize organization from the outset, the AI may be ineffective.
• Context and relevance: Another challenge is ensuring the AI understands the data and can apply it within the context of the business. E-commerce companies often have product information scattered across PDFs, internal documents, email conversations, and elsewhere. “It’s hard to gather all of that information and organize it in a way that the AI can make sense of,” Luccisano points out.
• Deciphering nuance: AI must also be trained to recognize the subtleties of different requests. “Systems are getting better at understanding the nuances of queries,” says Luccisano. “If we can route a ticket properly, we can handle it.” The challenge lies in ensuring the AI is trained to recognize and route tickets based on specific nuances — which comes back to data structure and quality.

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How Yuma AI ensures clean data

At Yuma AI, we understand that maintaining clean data is paramount for any AI ticketing system. We’ve developed processes to ensure data quality at every stage, from collection to implementation.

• Custom data pipelines: Yuma tailors its data pipelines to fit each client’s specific needs, ensuring the AI can make sense of complex or industry-specific information. Rather than relying on one-size-fits-all data inputs, we adjust to accommodate unique organizational processes, product catalogs, and customer service workflows. This customization ensures data is properly structured and accessible.

• Specific intents: Yuma ensures that each AI agent handles only clearly defined tasks, reducing the likelihood of errors. “We structure and feed knowledge intentionally to trigger the right processes at the right time,” explains Luccisano. “This allows our AI to recognize the right intent and follow instructions step by step.”
• Data validation: Clean data isn’t a one-time task. Yuma AI continually checks and validates data to ensure accuracy. This includes regular audits and updates to keep the AI’s knowledge current, as well as implementing guardrails to prevent outdated or incorrect information from being used.
• Quality control: Our system runs built-in quality control checks on every message before it reaches the customer. This multi-step validation process catches potential mistakes early, ensuring that even in high-pressure situations, our responses remain accurate and aligned with customer expectations.
• High-quality models: Yuma AI prioritizes quality over cutting costs by using the best and most advanced language learning models (LLMs) for each scenario. “We test and test and use the best LLMs — we don’t go cheap,” says Luccisano. “Our goal is to build the best product in terms of safety and accuracy, bringing the highest quality support for our merchants.”

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Discover the Yuma AI difference

To fully harness the power of AI, it’s essential to prioritize clean data. At Yuma AI, we prioritize structured processes, high-quality LLMs, and rigorous quality controls to help our clients scale their CX efficiently.

Ready to transform your AI ticketing and customer support? Discover how Yuma AI can build a robust solution to meet your needs. Contact us to start your free trial.

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