7 Reasons Why Domain-Specific AI for Service is Important

AI is poised to transform every aspect of business, but service leaders face a unique set of challenges that require highly tuned AI applications with exceptional accuracy.

Large language models (LLMs) are driving the current wave of AI innovation with their ability to analyze vast amounts of data to generate predictions in seconds. It’s important to understand how LLMs are trained because it directly impacts the accuracy of the output.

Domain-specific refers to AI that is trained to understand unique industries, business functions, or use cases. Here are seven key features of domain-specific AI for service and why they’re important. 

1. AI that understands service 

Generic LLMs that are trained on publicly available data lack the context and governance required to make accurate predictions for specific business use cases. Complex service environments are a prime example, where industry, product, issue, and resolution terminology are highly specialized.

Neuron7’s AI is purpose-built for service environments, using LLMs fine-tuned to understand service data and use cases. This is a foundational building block for accurate, actionable AI predictions.

2. AI that understands industry-specific product terminology

Service intelligence data across industries (e.g., medical devices, industrial equipment, high-tech) may share similar use cases, phrasing, and KPIs, but they each bring a set of terminology unique to their industry.

Your service LLMs should be trained on your industry-specific nomenclature to generate accurate AI outputs.

3. AI that understands your organization’s service language, syntax, and semantics

Enhancing the accuracy of AI is achieved by layering in your organization’s service data. This includes manuals, knowledge base articles, past cases, engineering notes, and any other data about problem resolution.

This combination of LLMs trained on 1) service use cases, 2) industry terminology, and 3) your organization’s service data delivers the highest level of accuracy.

4. Faster time to value and ROI

Service leaders need AI to drive measurable improvements in service metrics (first call resolutions, first time fix, call handle time, faster onboarding, mean time to repair, parts wastage, etc.). The ability to move the needle on service metrics relies on the accuracy of your AI solution. Service domain specific AI gives you a fast track to accuracy and ROI.

5. Accuracy & explainability drive user adoption

Resolving and preventing issues are the heart of service. Deploying AI that delivers accurate resolutions with clear links to the source of the information is key to fostering trust and adoption.

6. Capture and share knowledge

Neuron7’s AI continually improves accuracy by incorporating feedback from users. Your experts can optimize resolution paths that are shared with the rest of the organization in real-time.

“The reaction has been wonderful. They love it. We have it interfaced directly in the consoles they use out of Salesforce on a daily basis. So we get better customer service, we get more efficiency, and we get happier employees as we’re doing this.”
John Page
President of Global Services, Keysight

7. Enhanced AI in your service environment

AI capabilities are now baked into most major service platforms, but the features typically focus on simpler use cases like issue classification and links to articles.

Many enterprise organizations need AI that resolves issues across thousands of complex products. Neuron7 seamlessly integrates with Microsoft, Salesforce, SAP, and ServiceNow to bring domain-specific AI to your teams where they work.

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Resolve issues faster with AI that understands your service data and continually learns as you use it.