Conversational AI pre-trained to specific vertical industries is the fastest way to build and launch new self-service experiences.
In the past few years, AI has made a significant impact on the way we interact with technology. Users can now engage across multiple different device types using natural language, which offers a more seamless and intuitive experience when compared to traditional point-and-click interfaces. This shift in how people interact with their devices has led to an increased demand for conversational AI solutions across all industries. Law offices, plumbing companies, and local governments are all looking at ways to enhance their digital self-service tools.
Conversational AI powers digital self-service solutions like intelligent search and intelligent virtual assistants. These are often implemented to address an acute pain point for the user or the organization. They could be considered as solutions in response to rising customer service calls due to difficulty finding information on a website (painful for the user and expensive for the organization) or a decrease in available employees to manage inbound live chat queries. Regardless of the initial cause, once a decision is made to solve the problem with conversational AI technology, the desire is to implement quickly and seamlessly.
Large organizations will sometimes opt to build from scratch as part of a broader investment in conversational AI. That often means standing up internal development, analytics, and user experience groups dedicated to the function. In organizations without the desire or budget for in-house expertise, it can be much more difficult to find the resources to invest in building conversational AI experiences from scratch while staying on budget and on time.
At XAPP AI, we believe that the best way to build quickly and seamlessly, is by starting with pre-trained conversational models. These pre-trained conversational models can leverage industry specific data sets and AI algorithms to allow for faster deployment, with less hiccups after implementation.
To put it simply, building from scratch is like opening a box and finding a bunch of blocks with no instructions. Building with a pre-trained model is like opening a box and finding those same blocks…but with detailed diagrams and instructions, and some pre-assembled foundations to help you get started.
With pre-trained models, you can skip the initial training phase and get your chatbot up and running quickly—in as little as a few hours.
Some may approach pre-trained models with some skepticism. This is especially true in industries with a lot of specialized jargon and a history of high touch, human to human connection. Their unique requirements can be tough to manage effectively with a standard, “one size fits all” model. It was mentioned earlier that pre-trained conversational models can leverage industry specific data sets. That ability is actually one of the most powerful options available.
Language has a lot of nuance and different words have different meanings depending on the context of the industry vertical.
With budgets continuing to shrink and timetables for technology deployments getting tighter, a great way to build really strong experiences is to use pre-trained conversational models that are specific to a vertical industry.
Take for example the word, “gear.” If you repair bicycles it means one thing and if you sell outdoor apparel, it means something entirely different.
If the conversational model that you’re using is not trained to your specific jargon and use cases, it increases the likelihood that a user won’t be able to find what they’re looking for. When this happens, they’ll either opt for human support or they’ll abandon their efforts entirely. Both of these options cost organizations money.
Using pre-trained conversational AI models will put you on third base instead of in the batter’s box. There’s no thinking up all of the different words and scenarios you might encounter. Instead, any effort put in can be focused on customization to meet specific branding or business specifications. To give some more context on how much efficiency you gain from pre-trained models, XAPP AI was able to build 500 custom conversational AI solutions in just 60 days, using Optimal Conversation™ Studio.
That’s more than eight brand and business specific conversational AI solutions each day. A from-scratch build can take weeks or months. Using pre-trained conversational models specific to your industry is the most efficient and effective way to get new digital self-service experiences live.
