You’re looking to automate your call center in order to deliver faster customer service, reduce costs, and improve customer retention. There’s a lot of voice AI providers who can help you achieve those goals, but you could also save money and build your own voice AI. After all, Amazon Lex, Google Dialogflow, and IBM Watson offer the building blocks needed, and IT is used to building tools and integrating them. This sounds like a good plan, right?
But hold on. Before you make your business case, there are a few things to consider. Building your own AI is like designing and building a house from scratch, instead of hiring an experienced contractor to do it all for you. You can do it on your own, but whether it will end up delivering the outcomes you desire is another matter. Not to mention what it will cost you in the end.
The two options you have are working with a fully managed voice AI service or building it yourself via API vendors. A fully managed service offers proprietary conversational AI specifically built to solve Tier-1 calls on the phone, while APIs provide general-purpose AI components that experienced developers can use to build voice or text-based conversational interfaces.
As you consider whether to build or buy, here are eight reasons why it’s more effective to buy voice AI.
It takes significant resources to build everything from the ground up
Here it’s really a matter of expertise and dedicated time invested in developing a platform specifically designed to deliver best-in-breed results. Fully managed services offer out-of-the-box components, including conversational design interfaces, AI models with built-in continuous learning, dashboards, advanced reporting and analytics, and quality assurance. Conversational AI experts have developed the platform to deliver the highest value possible so you don’t have to.
Building voice AI requires off-the-shelf API components, including text-to-speech (TTS), transcription, and basic intent models. Your internal teams, who are most likely not conversational design experts, are responsible for building everything either by scratch or by engaging with a third-party integrator.
Deployment takes quarters or years
How fast do you want to add voice AI to your call center and have it serving customers? By working with a managed service, it takes an average of eight to 12 weeks. Your IT or AI department will need at least four months, if not longer, to build your own AI.
You need to build and maintain integrations
Voice AI is only as good as its ability to integrate with your core systems, such as your contact center software and CRM. Without integrations, the AI can’t access or update customer data. This limits the AI’s functionality and makes it extremely difficult to extract analytics.
A fully managed services solution offers pre-built integrations to major contact center software and CRM providers that make connecting easy and clear-cut. They typically also include an API framework for all other integrations that might be needed. These integrations are managed and automatically updated by the vendor, which means you can get up and running fast.
When you build AI, you’ll need to use generic APIs, implement the integrations yourself, monitor and manage them, and manually update them whenever one of the applications or software systems has updates.
Generic AI models aren’t built for telephony
The AI models offered by API providers aren’t built for serving customers over the phone. When you apply them to telephony, the result is an aggravating customer experience. Customers experience misunderstandings and long pauses in between turns in the conversation. You wouldn’t think a matter of mere seconds makes much difference, but it does.
One of the biggest factors for whether voice AI enhances or detracts from the customer experience is how fast the AI can respond and converse with humanlike speed. Customers expect to interact at a normal speaking pace. When voice AI cannot do so, even by just a couple of seconds, customers become frustrated by the slow back-and-forth and feel their time is being wasted.
Fully managed services have honed their AI to respond in less than one second, eliminating the delays that can damage the customer experience. AI APIs typically cannot get down to less than two seconds. This one extra second delay is critical. It can be the difference between a customer fully resolving their issue with the AI and hanging up because they’re so fed up with the slow pace of the conversation.
API services lack conversational accuracy
Voice AI must be able to discern customer intent. If the voice AI can’t accurately understand what the customer wants and means, it will quickly become a negative customer experience.
Fully managed services are capable of a 95% or higher intent accuracy rate, whereas AI API platforms are typically only 80% accurate. This fact alone calls into question whether building voice AI yourself is worth it, as misunderstanding 20% of what customers are saying is an unacceptable failure rate and can lead to customer churn.
Conversational design is tricky and requires expertise
Does your IT team have deep expertise in conversational AI design? Probably not. Can you hire independent consultants who require high fees? Yes. But this is an area where managed services excel right out of the gates.
Fully managed services employ the latest best practices in conversational design, and the call flows are designed and maintained by conversational AI experts. They take into account the importance of flexible and non-linear conversations, custom speech models, named entity recognition, advanced spell-out, fuzzy matching, and multiple intent detection. The result is voice AI that handles the most complex of customer conversations. They also offer visual IVRs that work alongside voice to create multimodal customer experiences and outbound call capabilities with API and dial logic. These capabilities are all included when you buy a voice AI solution and will be continually updated by the vendor as the field of AI advances.
Building voice AI requires your IT team to manage tools for linear conversations and use natural language APIs. After taking on this lengthy, custom work, your voice AI may be incapable of doing much more than the typical Q&A conversational style of voice home assistants.
You need to devote resources to continuous improvement of the AI
For voice AI to be a sound investment for your call center, it has to be capable of continuous learning and retraining. Who will manage and maintain your voice AI models to ensure they’re constantly improving? What about testing and quality assurance?
Fully managed services come with active-learning, testing, and quality assurance to ensure constant retraining and improvement. When you build your own AI, you’ll need to take on all of these maintenance responsibilities. Otherwise, your AI won’t improve.
Partnering with a voice AI vendor is a better use of your time and money
A final question you should ask yourself is, even if you have the resources to build it yourself, is that the best strategic use of your talent and time?
Choosing a fully managed services platform gives you everything you need right out of the box, with the assurance that you’re getting technology built by leading conversational AI experts and the proven functionality that other businesses are already experiencing. Your IT and AI teams are freed up to focus on other projects and develop technology that can only be done in-house.
Also, building voice AI may appear to be lower in upfront costs, but there’s a number of costs you’ll need to consider along the way: time spent continually developing, managing, and updating AI models, cost savings lost due to lengthy deployment times, and the cost of building in-house without the true AI or conversational design expertise needed to deliver the outcomes you want.
Fully managed services deliver higher value for the cost and faster results that lead to improved customer experiences. You can start seeing these results in less than 60 days, instead of spending months or years on development and risk never going live.