AI vs Senior Support Specialist

Traditional Customer

Support

In developing AskGuru, we discovered that customer support often resorts to a tried-and-true method when faced with complex questions: consult a senior colleague. This approach, while effective, is time-consuming and can overburden senior staff.

to the Rescue

Enter AI, which thanks to recent advancements, can now participate in meaningful dialogue across vast knowledge domains. A less obvious but equally impressive feature of modern AI is data representation. It's like a decoder ring, transforming complex text into a mathematical space, unreadable to humans but perfect for creating a robust search engine.

Text to embeddings conversion

This search engine, equipped with semantic search, can handle typos, misspellings, synonyms, and varied phrasing, making it an ideal tool for customer support. Link this search to a conversational model, and you've got an intelligent chatbot capable of understanding the user's question and formulating an appropriate response. Our own tests on real data show that such systems clearly outperform the previous generation of (quite advanced!) chatbots.

AI is a

Co-pilot

What if this AI system served as a co-pilot rather than a customer-facing bot? It could provide support agents with the most suitable response and help them find additional resources, essentially acting as a digital mentor. This would be particularly beneficial for junior specialists and teams providing support for other businesses, offering a solution to the challenges of handling new information and frequently switching contexts.

Fear

not

Does this spell the end for human customer support? Not at all. Here's why:

AI, while vast in its knowledge, can't provide specifics for unique organizational, product, or business case scenarios. Detailed documentation still needs to be maintained and updated for both human and AI agents.

AI systems need initial expert supervision to ensure accuracy and appropriate tone. While some systems can learn and improve with real-world data, they still require calibration.

Building an effective AI system isn't as simple as plugging in APIs and foundational models. A successful system requires careful attention to detail, fine-tuning, and proper infrastructure.

Think of AI as fuel for your business vehicle. The right kind can turbocharge your engine, but it alone can't ignite and propel the vehicle. AI can't singlehandedly drive your business to success, but it can certainly give it a significant boost when used correctly.