5 tips for conversational AI RFPs
Discover the essentials of creating an effective RFP for conversational AI solutions.
Important RFP considerations
Important RFP considerations when buying conversational AI
With thousands of conversational AI vendors to consider , finding the right solution to your CX problems is understandably challenging.
While we believe there is a more collaborative and effective approach to finding a specialist voice partner, many organizations are required to carry out the Request for Proposal (RFP) process.
In this post, we’ll assess the effectiveness of using an RFP when buying conversational AI and explore five things you should consider when creating your RFP for an AI voice assistant.
The discovery of call center voice AI solutions is restricted
If you’re looking to deploy a call center voice AI solution, there’s a high chance you want to streamline your contact center operations. You know your business better than anyone, where processes could be more efficient and the outcomes you want to achieve. However, call center voice AI is complex. You’ll need to understand how these technologies integrate into your operations and why one technology can drive more value to your business than another.
Despite your best efforts, the structured format of an RFP document can limit your ability to communicate your specific requirements and business needs effectively.
Instead, understanding call center voice AI’s place in your organization is achieved through thorough research and detailed conversations with potential vendors.
Call center voice AI projects require a high degree of collaboration and customization. That’s why it is essential to use the sales process to work with vendors, ask questions, share ideas, and understand how the technology will drive better outcomes specific to your business needs.
Don’t just fill out forms — engage with your potential partners to ensure they can add value to your business.
RFPs require valuable time and expertise
Issuing and reviewing an RFP is a significant investment of time, money, and effort for your business. It can be challenging, with multiple departments and stakeholders required to issue the RFP.
Coordinating the process usually takes months. Competing priorities and differing opinions of what is required of the technology can cause roadblocks due to multiple rounds of feedback and revisions.
With a unified view across your business, technical and product requirements can become clear. Lack of alignment increases the time to value, and your team’s ability to gain valuable insights into customer behavior, identify skill gaps, and improve processes is delayed.
Conversational AI can quickly exceed SOWs
The RFP process requires organizations to outline the entirety of their call center voice AI project upfront. The drawback of this approach is that during the deployment process, conversational technologies, like voice assistants, will inevitably require ongoing changes.
When changes happen, an RFP is used as a reference point. If functionality or services fall out of the agreed scope, it can lead to delays. Responding to these changes requires a flexible approach where you and your chosen vendor work in partnership to adapt.
It’s essential to be aligned on what ongoing services your partner will deliver to avoid surprise costs after conversational AI deployment , such as managing the significant amount of data generated as a result of deployment.
5 tips
5 tips for writing a conversational AI RFP
We’ve seen dozens of RFPs for conversational AI from companies that want to deliver natural-sounding voice assistants, but often these RFPs fail to take into account the differences between voice and text-based conversational AI solutions. A smart approach to RFP design will help you find the best solution for your business.
While many vendors offer voice solutions, they often do not have the specialist voice capabilities required to deploy experiences that your customers will engage with. Here are five things you should consider when creating your RFP for an AI voice assistant, to ensure that you find the perfect balance between efficiency and experience.
1. Understand the challenges of the voice channel
How a person speaks differs fundamentally from how they type or text and requires a specialized set of technologies and capabilities.
Consider the following:
- Accents – we all sound the same when we type, but when we speak, we have different accents, styles, and cadences of speech.
- Background noise/poor connections – voices are muffled over the phone, and customers often prefer the voice channel over text in noisy environments like driving or when outside.
- Synonyms, idioms, slang – we tend to speak more colloquially than we type, using everyday turns-of-phrase that may not be expected.
- Storytelling – we tend to be more concise when typing, but in speech, we tell long stories with lots of detail, most of which is unnecessary to complete a task.
What’s more, there’s no graphical user interface for speech, so the voice assistant needs to be able to engage callers and guide them through finely crafted, concise spoken utterances. Where chatbots can use buttons and menu options to guide callers, voice assistants need to start conversations with open questions to engage callers. Customer service representatives don’t start conversations with, “If you’d like to talk about billing, say billing.” They say, “How can I help?” Voice assistants should do the same.
PolyAI has developed several NLU models trained on billions of real-world conversations. Our customer-led voice assistants can handle multi-turn conversations and understand callers whatever they say, and however they say it, enabling even the most complex questions to be successfully contained.
2. Understand the voice tech stack
For a c onversational assistant to engage in customer-led conversation , it must be successful at listening, understanding, and responding to what the customer is saying.
Understanding what the caller is saying
Automatic speech recognition (ASR) – Does the vendor offer additional ASR tuning mechanisms and Spoken Language Understanding (SLU), giving you the capacity to understand callers over noisy phone connections?
Natural language understanding (NLU) – Do they use Large language models (LLMs) developed for the specific requirements of customer service use cases?
Refresh approach
Refresh your approach to conversational AI
The right voice AI solution will help your contact center improve operational efficiency and handle routine calls efficiently. This will allow your agents to focus on more complex and valuable interactions, improve the user experience, and deliver on key contact center metrics.
Revolutionizing enterprise customer service with lifelike voice AI
PolyAI helps enterprises be the best versions of themselves in every customer call by consistently delivering the best brand experience, achieving accurate resolution, and uncovering data-driven business opportunities. We offer the world’s most lifelike voice AI for enterprise customer service and deliver high-quality, human-like conversations optimized for customer engagement at scale.
Choose right solution
How to choose the right voice AI solution for your contact center
While an RFP can be a valuable tool, it may not be necessary or effective when looking for a call center voice AI partner. We’ve created this guide to give you a straightforward view of the voice AI landscape, the technology and resources required, and the options you have to buy voice AI. This will allow you to focus on what matters most to your business: delivering exceptional customer experiences.
FAQs
Conversational AI RFP FAQs
Is it possible to hand over AI conversations to human contact center agents?
An effective voice assistant should be able to hand off to an agent effectively during customer interactions. Many contact centers and customer service platforms have implemented this capability, and it offers several benefits, including improved customer experience, more efficient handling of complex issues, and greater flexibility.
