Guide to building vs. buying an AI Agent for customer service
This guide will help you assess the build vs buy question from 5 main angles so you can make a confident, informed decision.
Learn MoreFinding a conversational AI platform for customer service can be stressful. Unless you know exactly what you’re looking for, it’s tough to find the best conversational AI platform for your business.
In this guide, we discuss seven key questions you should ask the provider when looking for the right conversational AI that will transform your customer service. We’ll show you what a good answer to these questions looks like, so you can identify the top providers from the average ones. Let’s jump right in.
If you want the best solution for your business, start by looking for a conversational AI platform that offers a powerful combination of cutting-edge technologies. Some of these technologies have a long way to go, but adopting them now has benefits. As Ross Gruezemacher, Assistant Professor of Business Analytics at the School of Business at Wichita State University, explains in his HBR article :
"Aggressively adopt new language-based AI technologies; some will work well and others will not, but your employees will be quicker to adjust when you move on to the next. And don’t forget to adopt these technologies yourself — this is the best way for you to start to understand their future roles in your organization."
Here’s the bare minimum you should look for in a modern conversational AI platform.
ML and NLP are the core technologies powering conversational AI platforms. All the other conversational AI technologies (like sentiment analysis) depend on ML and NLP. A platform that doesn’t use ML or NLP is just an scripted chatbot responding to basic FAQ inquiries already found in your KB, not a generative AI agent.
Speech recognition helps a computer understand human speech. Speech recognition systems involve multiple components, including acoustic modeling, language modeling, and speech decoding to accurately convert and interpret speech.
Once the system receives input from a human, it breaks the speech down into basic components called phonemes. The system processes phonemes using a complex series of algorithms to interpret the spoken words.
A speech recognition system must overcome multiple challenges to accurately interpret speech — accents, background noise, and mumbling are common culprits. But thanks to machine learning, systems get better over time.
Data is a key driver of customer experience. But data gets trapped in silos when your tools aren’t integrated.
An AI agent needs access to multiple data sources to offer a seamless and personalized experience. You can always custom-build integrations, but that’s time- and resource-intensive. Ready-to-use integrations are a lot more cost-effective.
Before you ask this question, prepare a list of integrations you need. Add all the tools you use daily — your CRM, ERP, and invoicing tool — to this list. Ask the provider if they offer an out-of-the-box integration for these platforms or if they have an open API so you can build an integration yourself.
If an API isn’t on the table, ask the provider if they can cook something up for you. If they’re not keen, swipe left and consider another provider.
The platform’s analytics should be as live and dynamic as the conversations it facilitates. Analytics and reporting offer value in three ways:
The analytics feature collects data and uses it to measure performance. You can measure and monitor customer experience KPIs like customer satisfaction score (CSAT) , containment rate, and Automatic Resolution Rate (ARR%) in real time right from your analytics dashboard.
For example, you could check your ARR when there’s a sharp increase in support tickets. A low ARR indicates that the AI agent is unable to offer solutions customers are looking for.
Predictive analytics can make predictions based on historical data. AI agents use predictive analytics to predict user intent, recommend next-best actions, and drive conversations toward conversion.
“Those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real-time.”
- McKinsey
Once the ML algorithm makes predictions, generative AI uses them to generate engaging responses that provide an immersive experience. For example, when a customer contacts a travel booking AI agent and asks, “I’m looking for flights from New York to London on March 20,” the AI agent recognizes that customers generally follow up with questions about prices for the economy and business class. The AI agent proactively responds, “We have 11 flights from New York to London on March 20. Would you like to see the prices for economy or business class?”
Reporting helps you get a quick performance overview, but preparing reports can be a massive headache. Crunching numbers is one thing, but you also need to invest time into making reports look aesthetically appealing — let’s face it, nobody wants to read through hundreds of rows of numbers.
A built-in reporting feature substitutes manual effort by auto-generating visually stunning and informative reports that helps you and your team make a great impression on C-suite executives. We’re talking sleek dashboards with charts and pie graphs. When you need to look at your customer support KPIs, just head over to the reporting and dashboard, and voilà!
Imagine a world where all AI agents look and feel the same and generate the same answers. Boring, right? That’s why customization is non-negotiable — it enables you to add your own flavor to the AI agent. Here are some customization options you should look for:
Some providers offer even more customization options. For instance, Ada lets you customize your AI agent’s generative content with guidance . Guidance helps Ada answer questions based on your preferences. For example, you can guide the AI agent on how to respond when the customer’s text includes or begins with specific text variables.
Conversational AI platforms collect and generate data that can supercharge your business . However, this makes them an attractive target for cybercriminals. Security breaches can lead to legal troubles and, more importantly, a damaged reputation. This is why security is mission-critical.
Here’s what you should look for to ensure your data remains secure:
An AI agent must interact respectfully with the customer and shouldn’t engage in dangerous or harmful topics. Responses should be safe and accurate. Conversational AI platforms that use publicly available LLMs may generate incorrect or offensive answers because they contain information from all over the internet.
Not only is this bad news for your reputation, it can have legal and financial repercussions for the company. Using first-party support documentation and internal data minimizes the risk of misinformation or hallucination.
Ada was built with a focus on safety and accuracy to optimize for Automated Resolution . At Ada, we use multiple methods to ensure that the responses AI Agents generate are retrieved from the documents found in your knowledge base.
When speaking to a sales rep, ask how their conversational AI platform tackles hallucinations and knowledge base management.
Fancy features are nice, but they don’t mean much without results. Ask the provider how their solution has benefited previous clients before you put money on the table. Top providers publish case studies to showcase how exactly other clients used the platform to achieve the results you expect to achieve from the platform and have multiple testimonials from happy clients. Ask the provider to share these with you.
Here’s a general overview of things to look for when the provider offers case studies or other examples of successful implementation:
Once you find a conversational AI platform that checks all the boxes, request a demo . Be sure to make notes of things you like and questions you have during the demo. The more questions you ask, the more confident you’ll feel about investing in the platform.
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