A big source of business for us at Talla is failed chatbot projects. Building chatbots for customer support is hard because most of them are scripted. You have to go in and do some regular expression matching and then label language intents, and then map those to a workflow. It's a lot of work to create an interaction, and then the bot doesn't really learn much once it is deployed. As the world changes and new support issues arise, you have to go do a bunch of work to add those new issues.
"You never get a second chance to make a first impression" isn't just a truism; it's a warning about deploying customer support chatbots before they are ready. If you prematurely hand off customer interaction to an artificially intelligent chatbot before that chatbot has been properly trained, you can do far more harm than good.
The frustrating thing about reading AI news is that it covers a lot of the stuff that doesn't matter. As someone who runs an AI company, invests in AI companies, and writes a newsletter about AI (and thus reads a lot of AI news), I thought it would be good to highlight 5 key ideas that I seem mostly missing from the frameworks people use to think about AI. I do talk to a lot of smart people who know these things, in fact, some of the ideas came from AI. executives I've spoken to about AI. adoption, but most people, I believe, are missing these key pieces.
If you are looking to deploy a chatbot for support and have started to investigate the market for chatbot providers, you've probably been left with your head spinning trying to understand how they are different. Chatbot companies have different ways for building bots (how much is it out of the box ready, vs designed by you?) and different places they can deploy (website, facebook, slack, etc). This post is going to look quickly at the AI, or, in some cases lack of AI, that these bots have. In general you can divide them into one of 3 camps.
Today, we’re excited to launch our Customer Assist product to help businesses automate self-serve support by using Talla to answer support tickets directly on their site. With Customer Assist, end customers interact directly with Talla, giving them the power to get immediate, accurate answers to their support inquiries.
We've all had interactions with clunky or incoherent chatbots that made their owners look dumb, but few companies know what steps to take to ensure their own bots don't make the same mistakes. Before you entrust a chatbot to help with customer service and support, make sure you've given it the tools it needs to avoid embarrassing itself.
Every time technology shifts, people are caught off guard and there is this phase in the market where the new technologies are being thought of using the old frameworks. That’s where we are with AI today. Buyers are looking at enterprise AI software like it’s one of the SaaS solutions they bought in 2014. It’s not.
This is the second in a series of Talla customer spotlights highlighting the real ways that companies are using AI, chatbots, and automation to make their employees more productive and increase engagement. We recently interviewed rLoop’s Co-Founder and Project Manager Brent Lessard to learn more about how they use Talla.
Companies have a love-hate relationship with chatbots. They can be a great resource to sales and support teams - when they have been trained properly and provided with the right information. But, quite often, they come up short of expectations. But all is not lost! When the correct planning and training is put in place, which includes combining the chatbot concept with a formidable knowledge base and highly tuned automations, automated technologies can be extremely valuable to an organization.