8 Keys to a Successful Conversational AI Deployment

So, you’re thinking about introducing a conversational AI project into your business?

There’s a multitude of ways you could have arrived here.

Maybe all the AI buzz has you thinking you need to get a project on the books, or risk being left behind (or lacking that bullet point on your Linkedin profile). Possibly, you’re looking for a creative way to get an edge on a larger competitor. Or maybe you’re just curious about how AI could benefit your particular business or industry.

In any of these scenarios, you’re probably looking for a quick, easy win. A project that will help you build understanding and support within your organization for larger AI initiatives in the future. But, like any leading-edge technology, your AI project can risk getting mired in the experimental weeds if you don’t clearly define the benefits to the business and a short path to those benefits.

To that end, let’s take a look at eight keys to creating the most direct and successful path to your first conversational AI deployment.

1. Start with a Real Business Problem

One of the most common reasons AI implementations fail is because they lack a clear business problem to solve. Too often our expectations of artificial intelligence are still in the realm of science fiction. For instance, we assume that AI will find problems to fix or reveal treasure under a mountain of unstructured business data.This is my first cautionary note: These kinds of soft, aspirational objectives make for great research projects, but disastrous business initiatives.The priority for a new conversational AI project should be to clearly define a customer need and finding a project team of subject matter experts that are committed to solving that problem with conversational AI and Machine Learning. After that, your business case and project plan are not so different from any other technology project in your organization.

A strong conversational AI proposal will be one that has well-defined goals and objectives, focused on solving a specific business need, and filled with very detailed use cases that we expect conversational AI is well suited to tackle.

In a simple phrase, your goal with your first conversational AI project is to create a clear vision with a pragmatic plan.

2. Think Big, But Work Small

Think big, but work small is one of my favorite technology project mantras. This simple philosophy is well suited for implementing any leading-edge technology but particularly appropriate for new AI initiatives.We’re not looking to create revolutions, but rather evolutions.  In doing this, it’s quite alright to think big about your ultimate vision. Have grand plans for where conversational AI will take your business in the future, but your starting point should be small and specific.

When looking for the right place to start, I recommend looking for areas with lots of customer interactions and a clear expectation of how conversational AI might make those experiences better.

Common examples might include:

• Customer service calls that are filled with routine and frequently asked questions, but force customers to wait in long, unprioritized queues for simple answers
• Drive-through ordering that is typically guided by well-defined menu choices, but forces customers to experience a slow and error-prone process at the hands of employees that can be overwhelmed by this inherently frenetic traditional human-to-human interface
• Routine transactions that can be assisted by machines learning from your repeated behaviors and then assisting in reducing your participation in these repetitive activities. A perfect example of this is what x.ai has done for scheduling meetings.

From these examples, can you envision a time when your AI assistant might be able to handle all of your customer service calls and online chats or manage your entire email inbox, not just scheduling? I can.

But, while keeping those lofty aspirations in mind, don’t send your first conversational AI project on that kind of death march to oblivion.

3. Focus on Transactions

In the examples above, you might have noticed a focus on business problems that involve transactions. I think that Conversational AI experiences are far more satisfying to customers when they feel like they’ve accomplished something or received something valuable from the interaction.

That’s why I always recommend focusing on creating experiences where AI can resolve a problem, complete a task, or deliver something valuable for the customer.

4. Identify and Integrate Key Data Sets

Having identified the business problem we want to solve and the small part of that pain point we want to address, it’s time to find the data for our AI to work against.

Finding the right data sets, and effectively integrating them is another place where AI projects can run aground.

You can avoid this fate by making sure that you clearly understand the problem to be solved and the benefits of solving that problem for a customer. Then go looking for data sets that can support a knowledge base for the solution.  This approach helps inform a system that guides (supervises) your customers and machine learning that will produce useful outcomes.

The wrong, but all too common approach is one where your AI project is defined by a general curiosity about what might be in your customer data sets. This mindset will put too much reliance on the customer base to supervise (unwittingly) your machine learning, in hopes of creating something useful.

5. Include Data Curation and Crowdsourcing

The quality of artificial intelligence and machine learning solutions are fueled by the quality and quantity of its data. Therefore, every conversational AI project should have a definite plan around how it will acquire and curate conversational data.

Like many of the components necessary to implement AI, doing this from scratch can feel daunting. Fortunately, platforms like Clinc’s are democratizing conversational AI by building in tools for data curation and crowdsourcing, making it easy to quickly gather large amounts of quality data to train your AI.

6. Incentivize Usage

I mentioned earlier on how important it is to place your conversational AI solution into a large stream of customers – to fuel it with data and learning opportunities. However, that might not be enough to overcome the natural resistance to trying new things.  Think about creative ways to incentivize the
use of your new conversational AI.  Convenience is often enough.

Thinking about the Fast Pass concept at Disney theme parks can probably spark a half-dozen ideas for customer service use cases (chokepoint use cases) that your conversational AI project could work to improve.

7. Provide a Machine to Human Escalation Path

Bad customer experiences can quickly escalate and escalate to very high levels in your organization. This is an important use case to consider in any conversational AI project because of the potential impact on customer satisfaction.

Unfortunately, technology has the potential to equally solve and cause problems at a very high rate of efficiency.  Consequently, it’s wise to plan a seamless escalation path from machine to human in any conversational AI deployment.

8. Education

Last, but not least is the importance of education.

The faster you educate and demonstrate to your C-suite the reality of AI, how it works, and the fact that it’s ready for primetime business problems the more impact you can have with your AI projects.

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.