How to Start an Enterprise Conversational AI Project

How many times have you talked to Siri, Google, or Alexa today?

If you’re like the majority of consumers, you’re probably finding that you’re doing more talking than looking at your mobile phone lately. You’re letting these AI assistants take care of doing mundane things like finding directions, checking the weather and stock market, and texting your friends and family.

Brands are noticing this too.

In a recent Gartner report, Top Strategic Predictions for 2017 and Beyond: Surviving the Storm Winds of Digital Disruption, 20% of surveyed brands said they expect to abandon their mobile apps by 2019. A trend that is further explained and justified by another study conducted by Fifth Quadrant, Conversational Commerce and ChatBots: Business Consumer Usage and Attitudes, where respondents believe that 40% of all mobile interactions will be via virtual assistants by 2020.

These are just a few of the many reasons you might be thinking about starting a Conversational AI project for your company. In this article, Clinc wanted to share our experience and learned best practices from supporting several successful enterprise conversational AI projects.

Why Start an Enterprise Conversational AI Project

I’ve already touched on one of the strongest justifications for conversational AI – consumers are opting for voice user interfaces (VUIs). But, this shift in preference is just the start of a more significant trend in how consumers expect enterprises to serve their needs.

Consumers are shopping and buying via voice interfaces

Our homes, offices, vehicles, and even pockets are becoming filled with AI assistants. And we’re doing more than checking the weather and asking them trivia questions. Consumers are starting to enjoy the convenience of having a personal shopper within earshot. Conversational shopping and buying are becoming more than a novelty.

•  Voice commerce sales reached $1.8 billion in 2017 and are expected to climb to $40 billion by 2022 (OC&C Strategy Consultants).
• 22% of AI assistant owners (e.g., Amazon Alexa and Google Home) have made a purchase (Edison Research), and 5% of all consumers have used a voice interface to make a purchase. This number is expected to rise to 50% by 2022 (MoffetNathanson).
• All of these numbers seem reasonable considering 26% of US adults currently live in a household with at least one personal assistant (MoffetNathanson).

Consumers are more likely to buy with conversational AI

Not only are consumers beginning to get more and more comfortable using conversational interfaces for shopping, but it also seems to increase the likelihood of completing a purchase.

•  In the same Fifth Quadrant survey that we referenced earlier, 77% of respondents said they were more likely to complete a transaction with the immediate help of a conversational AI assistant.
• 85% of businesses believe that conversational AI assistants, at the online point of sale, improved conversion rates.
• And, 65% of consumers said they liked businesses that use messaging because they were fast and convenient.

Framework for Considering Your First AI Project

We’ve talked about the keys to successful conversational AI projects in the past, so I’ll review the highlights here.

1. Find a Real Business Problem – Leave academic research projects to academia. If you’re going to seriously consider introducing conversational AI into your enterprise, bite off a real problem. One of the best places to look for pain point is in the areas of customer service, support, or sales. These folks are always looking for ways to do a tough job better.

2. Look for Productivity or Cost Saving Opportunities – Nothing gets executives excited and ready to fully fund initiatives like an increase in productivity or sales (revenue) or a decrease in waste or costs (expenses). Make sure your AI project targets a measurable increase in revenue or a reduction of costs.

3. Understand Your Data Sets – Know where you’re going to get the data for your conversational AI to work against. This might require you to find datasets within your enterprise or acquire third-party data. In both cases, you want to start working with internal data stakeholders and IT as soon as possible to ensure you can fuel your AI assistant(s).

4. Use a Conversational AI Platform – One of the fastest short-cuts to a compelling AI implementation is by leveraging a conversational AI platform. Using a conversational AI platform, like Clinc, your enterprise can go to market in weeks, not months. These platforms already have many of the essential elements of an exceptional customer experience built in – powerful AI engine, experiential learning, natural language processing, conversation management, and the ability to launch personalities and customizations to fit your company’s brand.

Forming Your Conversational AI Team

One of the best and most complete guides to building conversational AI teams is a document, similarly titled – Building Conversational AI Teams written by Jason Brenier, Vice President of Strategy at GeorgianPartners.

If you’re looking to begin acquiring a dedicated, in-house artificial intelligence product team, then I would recommend downloading and ear-marking that document for your executive and HR teams. But, in doing that don’t become paralyzed in the quest for the ideal AI team and skills.

To get your first conversational AI product out the door, you can upskill a smart team of AI motivated and initiated individuals. Because of the increasing power and simplicity of conversational AI platforms and AI assistant programming interfaces, most initial AI projects start without a single new hire.

Here’s a proven framework for assembling this first AI dream team, especially if you’re going to use a platform approach.

• Conversational Project Manager – This needs to be someone with organizational influence and product vision. You’re likely to require large doses of both to get business support, resources, and access to data.
• Sales and Marketing Support – Most conversational AI projects begin by solving customer service challenges. Therefore, you want domain experts assisting the team in building experiences that customers will be eager to use.
• Business Analyst or Conversational Designer – These are the folks that will be doing much of the heavy lifting. They will be the primary users of your conversational AI platform and will design and optimize voice prompts, dialogue flows, personas, conversations and scripts, and conversational repair.
• Integration Engineer(s) – Data is the fuel for successful AI. These engineers will help you find, integrate, and pre-process data sets to make your AI assistants smarter. They will be hooking your AI assistant into the best APIs, in-house data sets, and efficiently acquiring and organizing new training data.

In all cases, you want a team that understands the business problem you’re trying to solve and is committed to addressing it with a conversational AI approach.

Getting Your First Conversational AI Project Completed

We’ve covered all of the planning and preparing stuff. Now, it’s time to get the project done.

Deploying the first iteration of your conversational AI product needs to be the number one priority. There is so much hype around AI that many enterprises are still skeptical of the technology’s viability. That’s why it’s crucial to get that first launch and demonstrate the value of serving your customers with conversational AI.

Clinc can help expedite that first launch. Using a conversational AI platform, like Clinc, you can build real-world AI solutions in a few weeks like Ford did for a new in-car experience or even develop more complex financial AI assistants like USAA, Isbank and Barclays did in a matter of 2-3 months.

Clinc, the Fastest Way to Wow! Market-ready Conversational AI Experiences

Are you thinking about a conversational AI project for your business? Let us help you through the process of creating an applied AI experience within your company.

Schedule a discovery session with one of our AI business analysts.