As businesses try to meet rising customer expectations for speed, personalization, and efficiency, AI has become essential for scaling operations without compromising service quality.

From simple FAQ chatbots to sophisticated artificial intelligence (AI) agents that resolve issues, automate workflows, and enhance human agents, AI’s role in customer experience (CX) is rapidly evolving. 

But with so many AI solutions on the market, how do you know which one is right for your business?

In this article, we’ll break down the five key questions every business should ask before launching AI:

  • Should I adopt AI?
  • Should I build AI myself?
  • Should I use my help desk’s AI add-on?
  • Should I go with AI trained on public data?
  • Should I invest in a dedicated AI solution?

By the end, you’ll have a clear framework to evaluate AI solutions, avoid costly mistakes, and choose the best approach for seamless, scalable customer support.

The limitations of traditional generative AI in customer support

Generative AI is the buzzword of the moment, and for good reason—it has made significant strides in automating certain tasks. 

However, when it comes to resolving customer support issues, generative AI as it exists today has notable limitations.

The majority of generative AI tools focus on answering FAQs by pulling information from a knowledge base. This approach, known as retrieval-augmented generation (RAG), can resolve about 20% of customer support inquiries. Many of the tools integrated into popular help desks, such as Intercom, rely on this retrieval-based technology.

But what about the other 70% of customer issues? These often require nuanced reasoning, complex logic, API integrations, password resets, troubleshooting, and more—tasks that go beyond simple text generation. 

This is where agentic AI comes in.

The future of support

Our Agentic AI is designed to operate like a human agent. It doesn’t just generate responses—it reasons, plans, and takes action. This means it can execute tasks autonomously, making it a game-changer for customer support automation.

At Forethought, our AI doesn’t just retrieve answers; it understands business logic, connects with APIs, and resolves issues without human intervention. Our second key differentiator is that our system is fully trained on a company’s specific data, making it highly effective in delivering accurate and relevant solutions.

Because it is both agentic and data-driven, our AI can assist in multiple capacities—it can fully resolve issues, support human agents in a copilot mode, and even generate valuable insights for businesses.

Agentic AI is set to redefine the customer support experience. While the term might not be widespread yet, it's only a matter of time before it becomes a key player in AI-driven automation. 

How to launch AI effectively

Customers today expect fast, personalized, and seamless support experiences. Customers expect chat responses within minutes—no one wants to wait on hold for an hour or receive an email response 24 hours later. They want help instantly.

Beyond speed, personalization is just as critical. Around 76% of customers expect businesses to know who they are, what products they use, and what issues they’ve encountered before. 

They don’t want to start from scratch every time they reach out for support. And companies are feeling this shift—87% of support teams report that customer expectations are rising year over year.

As more customer interactions move online, businesses must prioritize making these experiences fast, personalized, and scalable. The best way to achieve this? First, by optimizing and upskilling support teams. But as demand grows, scaling solely through human effort becomes impractical. That’s where AI and automation come in.

The five-step AI playbook

Before diving into AI, it’s crucial to ask five key questions. These will help determine the right approach and ensure AI adoption aligns with your business needs.

The five-step AI playbook

By carefully considering these five questions, businesses can make informed decisions about AI adoption and implementation, ensuring a balance between efficiency, scalability, and customer experience.