Solving Common Conversational AI Project Issues
This post was originally published on AI Time Journal.
By Chris Ezekiel, Founder & CEO, Creative Virtual
Back in 2019, I wrote an article on reasons why chatbot projects were failing or being abandoned before they even reached the testing stage. At the time, the conversational AI industry had been saturated with both false promises about the capabilities of the technology and a plethora of new start-ups with misleading claims about having AI-powered customer service bots. That left the chatbot and virtual agent landscape littered with poor-performing and failed projects along with negative press about the technology.
Fast forward to 2022, and much has changed in the world of conversational AI. One important change is the widespread acceptance that pure AI is not the right answer for automated customer service and employee support. Industry experts, analysts, and vendors (including those that previously claimed otherwise) now agree that a combination of humans and AI is the best approach to these chatbot and virtual agent solutions. This is coupled with important advancements in conversational AI technology that allow for the right balance of human and machine to create positive support experiences.
Conversational AI is now widely recognised as an important technology in digital customer experience and employee support strategies. During the pandemic, chatbots and virtual agents were a crucial tool for some organisations to meet the challenges of serving customers quickly and efficiently during a time when contact centres were overwhelmed and information was changing rapidly. These success stories demonstrate just how essential conversational AI technology is for successful digital strategies.
Yet despite all those success stories, some organisations are still struggling with a chatbot that’s not performing as expected, can’t be scaled as their business grows, or doesn’t properly reflect their brand. Over the past couple of years, I’ve heard a variety of reasons from business leaders on why their company is unhappy with their conversational AI tools. Here are the six most common issues they cite:
- I can’t expand my solution to support my growing business and customer base.
- I have limited integration options to create a seamless and personalised experience.
- I started my project with an inexperienced start-up that isn’t able to provide the technology updates and support I need from my vendor.
- I am struggling to manage multiple chatbots across different business divisions or departments.
- I am unable to staff my chatbot project with internal resources with the necessary knowledge and experience.
- I don’t own the user interface or training data with my current chatbot provider.
In those situations, it can be easy to mistakenly dismiss the technology as ineffective. Don’t fall into that trap! Instead of abandoning your investment and writing conversational AI off as a poor fit for your strategy, you need to engage with a vendor that has the tools and experience to get your project back on track.
My first recommendation for transforming a tool with any of these issues is to find a vendor that can leverage what you already have from your existing project or projects. You want to rescue your investment, not start over from scratch. If a vendor doesn’t have technology sophisticated enough to do this, then most likely their solution isn’t going to work for you in the long run anyway.
My second recommendation is to be very clear – both internally and with the vendor – about both your short-term and long-term goals for your chatbot. This is what is going to drive your conversational AI strategy and technology requirements. If you don’t know what you want to accomplish with your chatbot, then you can’t properly identify the functionality, integrations, reporting, etc. you need from a vendor.
And my third recommendation is to ensure you’re selecting a vendor with the right experience and expertise. You want to collaborate with a provider that has solved your issue before, understands the specific needs of your industry and/or use case, and has the references to back them up. Ask to talk to their current clients so you can hear first-hand from them what it’s like to work with the vendor.
If you’re interested in learning more, Creative Virtual has put together an eBook – Conversational AI Issues & Solutions – that talks about each of the common issues I listed above in more detail.
My overarching advice is: Don’t let any of these issues signal the end of your conversational AI project. Leverage what you already have to transform that project into the successful and valuable digital support solution your organisation needs.