August 4, 2020

The Truth About 3 Common Conversational AI Myths

When researching a conversational AI platform for your business, it can be hard to decipher what quality platforms look like. Every company has its set of differentiators and value props that – to most people – make them look sophisticated and, therefore, the right choice. But the truth is, not all conversational AI platforms are created equal. In this series, you’ll learn what to look for when evaluating conversational AI platforms and walk away with the knowledge you need to make an informed decision. Let’s start by dispelling a few of the common myths about building conversational AI.

Myth: Gathering quality data is a long process of trial and error

Truth: While it’s true that conversational AI products are only as good as the data behind them, gathering quality data doesn’t need to be a daunting task. One of the primary methods to both accelerate time and improve quality is by leveraging crowdsourcing when first building out models, and utilizing production data after deployment. This bootstrapping method allows diverse data to be quickly obtained. There is a reason the phrase “data is the new oil” comes up so often, it takes refinement to ensure maximum value. Any conversational AI vendor you select should allow you to easily collect quality data through its platform.

Myth: You need a lot of resources to build good conversational AI

Truth: Building quality conversational AI products doesn’t have to be a resource-intensive ordeal that requires trial and error when training murky data models. Production-level experiences can be accomplished with a robust toolkit that supports visibility into how data impacts your AI models. The ability to generate actionable insights on how to optimize AI models and identify opportunities for future iterations is critical to any AI building platform. These tools should empower users to know how to iterate on their models to minimize errors and increase accuracy. 

Myth: Building conversational AI is the same as software development

Truth: While creating a production-ready solution is an iterative, continuous process, it has some distinct differences from standard software development processes. Conversational AI platforms must support end to end and unit testing that covers all your various AI models and responses to ensure end-users will be supported from day one. Platforms should also support the collaboration of teams of developers that can work in conjunction and coordinate efforts to ensure streamlined delivery. Having confidence in your conversational AI product means having control over the various AI engines and being able to test them thoroughly to hit validation points and prevent regressions. 

 

Conversational AI

At Clinc, our mission isn’t just building quality conversational AI experiences – it’s educating anyone curious about AI technology and elevating the quality of AI being built, no matter the industry. If you want to learn more about Clinc’s platform, check our our platform page

If you enjoyed reading this and want to learn more about the technical challenges of conversational AI, watch Co-founder and CTO Dr. Lingjia Tang’s presentation at the MIT AI 2020 conference, here.