How to approach a conversational AI RFP (with checklist)

5 minutes

October 31, 2024

Key Takeaways

RFP limitations in conversational AI

Traditional RFP processes can be restrictive when sourcing voice AI solutions, limiting flexibility and understanding of complex requirements. Engaging directly with vendors is often more effective.

Specialized voice AI needs

Voice interactions differ from text, requiring specialized technology to handle accents, background noise, and conversational nuances. Choosing a vendor with voice-specific expertise is crucial.

Collaboration is key

A successful voice AI project depends on close collaboration between your team and the vendor, ensuring flexibility, ongoing support, and alignment with your business goals.


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.

Important RFP considerations when buying conversational AI

At PolyAI, we have encountered numerous companies that adopt the RFP process to find a specialist call center voice AI partner to help them create an effective customer-led voice assistant and improve contact center automation efforts. But is going to RFP the best way to buy?

Here are some reasons why RFPs can be ineffective in finding the best call center voice AI partner to solve your business problems.

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 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.

To give your customers the freedom to speak naturally and the confidence that they will be understood requires a solution that is designed and built specifically for conversational voice interactions.

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.


Some questions to consider in your RFP
  • Does the company have demonstrable success deploying voice assistants for customer service?
    – Ask to see case studies, hear call recordings, and ideally, an opportunity to call in and test yourself.
  • Does the implementation team include voice-specific design expertise? i.e. designing for spoken language.
    -Ask to have them present their voice design and implementation methodology. Ensure voice-specific guiding principles (i.e., brevity, tonality, etc).

2. Understand the voice tech stack

Poor speech recognition is behind countless bad customer experiences over the phone. How often have you heard “Sorry, I didn’t get that” from a conversational IVR?

Telephony-bound voice is inherently challenging from a speech recognition perspective. Voice solutions must account for background noise, poor quality, and various dialects and accents. All of these factors can make understanding a caller’s words difficult.

For a conversational assistant to engage in customer-led conversation, it must be successful at listening, understanding, and responding to what the customer is saying.

Understanding how a vendor’s tech stack works is essential to create an effective voice solution. Some critical technical capabilities to consider include the following:

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?

Identifying callers

Alphanumeric capture – Does the vendor have a proven track record of accurately taking down alphanumeric strings (e.g., order numbers, ZIP codes, phone numbers) without requiring the caller to use the phone’s keypad?

Name recognition – Does the vendor have a solution for taking down caller’s names?

Extracting values

Entity extraction – How does the vendor extract values from long utterances and understand values even if they are given in a non-traditional format, for example, “my wife and I” = 2 people)?

Non-linear conversations

Dialogue policy – Can the solution facilitate customer-led conversations by allowing callers to go off-topic with unrelated questions and bring the conversation back to resolve the original query?

Consistent brand voice

Natural-sounding voice – Does the voice assistant have a natural-sounding voice?

Dialogue design – Is there sufficient design support capabilities to ensure scripting and voice direction have maximum impact over the phone?


Some questions to consider in your RFP
  • How does the vendor solve for speech recognition over the phone?
  • How does the vendor account for the accuracy of information collection, given noisy phone connections?
  • How does the vendor ensure a natural-sounding and on-brand voice that engages callers?
  • Which languages does the vendor currently support?

3. Know your project team

Often, solutions that seem low-cost and low-effort at first end up becoming overly complex in production. Additional products and solutions may need to be added, incurring hidden costs. As the build becomes more complex, deployment is pushed further and further away and in some cases, it never actually happens.

When considering a voice partner, you should understand up-front what will be required from you, your team and your company to ensure a successful deployment.

Pre-launch

Consider how much time and input is required of your team and what additional technologies need to be built. Some vendors require training data, such as call transcripts and FAQs, to be gathered manually, which can cause an implementation to take months, if not years.

Using a low code or no code solution may seem attractive at first, but will require an in-house development and design team or consultancy partner to build. If you go this route, make sure that you understand exactly what will need to happen to ensure great customer experiences are delivered at the end.

Launch

A successful deployment requires collaboration, and a team focused on making tweaks to improve performance. Ensure that your chosen partner will be closely monitoring the system in its first weeks, ready to make changes to enhance performance.

Post-launch

Some vendors provide the framework for business users to build voice assistants themselves. If you are thinking of launching a voice assistant this way, be clear on your ongoing partnership and services. If this isn’t outlined in your RFP, it can lead to delays and unforeseen costs for additional functionality and services.

Many voice solutions create a significant amount of data that, when structured correctly, provides invaluable customer information, such as why your customers are calling. Be clear about how you will access this data post-launch and turn it into a usable format.


Some questions to consider in your RFP
  • What training data is required and how does the vendor expect it to be delivered?
  • How does the vendor support you through the launch process?
  • What does the process of ongoing maintenance look like?

4. Know your strengths

Designing, launching, and maintaining a voice assistant requires a team of specialists, including dialogue designers, machine learning engineers, implementation engineers, voice user interface designers, and project managers, among many others.

Your technical team’s capabilities will significantly impact your approach to launching a voice assistant and the vendor you choose to work with. It can prove costly and inefficient to attempt to build a voice assistant with a team that hasn’t been through the process before. Understanding the strengths of your internal teams will help you identify the features and services you will need from a specialist voice partner.


Some questions to consider in your RFP
  • What expertise and skills are expected from your side?
  • How much time will you need to give throughout the process?

5. Be mindful of your business goals

With multiple stakeholders involved in the RFP process, it is common for business goals and objectives to be clouded by conflicting priorities and opinions about what is expected from a specialist voice partner.

Keep asking what you want to achieve throughout the RFP process. It will help you ensure that your chosen vendor provides the functionality and scalability your business needs.

For example, if your goal is to improve customer insights, you will need to prioritize vendors that offer real-time structured conversational data. Or, if you’re scaling your customer service operations internationally, you may need to consider multilingual voicebots.

We often see RFPs that ask specific questions about capabilities that they believe will help them solve certain goals. But asking questions that focus more on how a vendor will solve your problems can open up new and unforeseen opportunities that will empower you to deliver something amazing.


Some questions to consider in your RFP
  • How will the vendor help you to solve [insert specific business goal]?
  • How has the vendor solved [specific business goal] for other companies?

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.

With seemingly endless categories of technology – all making similar promises about virtual assistants that reduce call volume, improve self-service and CX, and cut costs – it can be difficult to make future-proof technology decisions that translate into both customer and operational benefits.

By carefully evaluating your options and aligning them with your business goals, you can enhance customer satisfaction and deliver exceptional customer experiences.

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.

Experience our voice assistants firsthand

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.


Discover how PolyAI can help. Speak to the team today, see and hear the technology first-hand, discuss security, support, and customization, and compare PolyAI to other voice platforms.

Guide

Build vs Buy:

A clear overview of the technology, resources needed, and purchasing options for voice AI.

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Conversational AI RFP FAQs

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.

The most appropriate time for an AI call assistant to hand off to a live agent is unique to your business case. All customer queries are important, but they will require an order of priority, which the call type can determine.

NLU effectiveness depends on various factors, including the quality of the NLU model, the complexity of the user intents, the training data, and the specific application or domain.

The quality of training data is a crucial factor—well-trained models with diverse data perform better, while models lacking domain-specific knowledge may misinterpret inputs.

Pre-trained large language models (LLMs) developed especially for customer service use cases should require less training data enabling faster time to value.

AI tools operate based on the data and algorithms programmed by humans, lacking their own ethical reasoning. Ethical AI and empathetic voice assistants require careful design, oversight, and adherence to ethical guidelines to ensure responsible and fair outcomes.

Here are some examples of how businesses are successfully using generative AI:

  • Content creation: AI is being used to automatically generate marketing copy, blog posts, social media updates, and even product descriptions, speeding up content production.
  • Coding assistance: AI is assisting developers by generating code snippets, suggesting optimizations, or even completing entire functions, making software development faster and more efficient.
  • Image and video editing: AI tools are being used to enhance and manipulate images or videos, including automating edits, creating visual effects, or even generating entirely new footage.
  • Personalized recommendations: E-commerce and entertainment companies use AI to generate personalized product or content recommendations based on user behavior, improving customer engagement and sales.

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