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Conversational AI Best Practices - Discover AI Opportunities And Their Impact To Your Organization

Congratulations! You’ve made the decision to continue building yourself as an AI Enabled Enterprise by pursuing the world of Conversational AI. With customizable dictionaries, trainable audio models, and sentiment analysis to capture a caller’s tone, Chat Bots are becoming more viable supplements to your call centers with each passing year.

Now that you’re invested, where do you begin? How do you successfully bring a Conversational AI into production? How do you even know which use cases are best worth pursuing? In my 4-Part Best Practice Series , I'll cover the below 9 steps to get a chatbot into production, broken up into business and development categories. In this article, we'll be covering the first two:


1. Discover Conversational AI Opportunities

2. Determine Opportunity Impact and Feasibility

3. Plan your project

4. Change Management


1. Pre- Development Essentials

2. Data Preparation

3. Test your new Chat Bot

4. Deploy and Maintain your Chat Bot

5. MLOps and Continuous Improvement


1. Discover Conversational AI Opportunities

The first step to implementing a Chat Bot in your organization is to see where it can have the biggest impact. To do this, it’s recommended to tie your Chat Bot opportunities to overarching company goals, your teams goals, and any problem areas it can help with. For example, say you manage the operational infrastructure of your company’s call center and they’re focused on insurance, you can use the following criteria:

Company Goal

· Increase membership by 20%

Team Goals

· Handle 20% more call volume

· Improve Net Promoter Score by 1 point

· Reduce call duration

Problem Areas

· Current CSRs cannot keep up during peak hours, resulting in upwards of 1 hour caller wait time

· Ramping up and retention of CSRs make it difficult and expensive to scale with growing demand

· Low number of SMEs causes calls to drag on while CSRs wait to receive the answers to satisfy Member callers

Using the above, you can think about how Conversational AI can help you meet your goals and resolve trouble areas. Let’s break down one of the above pain points and formulate it into a use case:

Problem Statement:

Members experiencing 1 hour wait times during peak hours


· As flu season comes up, members are calling in to check their coverage

· At end of month, members are calling in to make their payments via phone

· Claim Escalations require the CSR to reach out to an internal SME, who are limited and require members to once again go on hold

How can a chat-bot help?

· An FAQ chat bot can answer questions on coverage, freeing up CSRs from redundant questions

· A payment chat bot can process payments directly from users without the need of a CSR

· CSRs can internally interact with a chat bot to extract data from knowledge bases, reducing the load on SMEs and replying quicker to members

Looking at the above, by framing company goals and pain points, you were able to think up of 3 different Conversational AI use cases. Using this method, you can brainstorm many targeted, relevant opportunities to bring Conversational AI into your business. Now that you have your ideas, how do you decide which to tackle first? That will take us to our next step

2. Determine Opportunity Impact and Feasibility

Now that we have a list of use cases, the next step is to vet them out and prioritize them. We do this by asking ourselves two different questions:

· How much value and impact can this AI opportunity provide?

· How complicated will it be to implement?

From the use cases we came up with in Step 1, let’s continue this exercise with the Coverage use case. We’ll start by answering the above questions.


We determined in the brainstorming exercise that one of the large goals of the company Is to increase membership by 20% in the next year. That will mean call centers need to be scaled up to handle an upwards potential of 20% more calls.

Looking at the problems we’ve listed above, hiring, training, and retaining CSRs has not been a trivial task – and handling 20% more call volume while scaling up the team is going to be a difficult and expensive process. All of the use cases we came up with can help alleviate CSRs or SMEs, and now it’s our job to find out by how much.

Say that we are able to pull analytics on the conversation transcripts from these member calls. We find that 15% of all calls coming in are members asking questions related to Coverage.

With that information, we can start crunching numbers:

Assume the following:

· 1000 currently employed CSRs

· Avg CSR salary is $35,000 a year

· Growth goal: +20%

That means:

· Current spend: 1,000 x $35,000 annually = $35,000,000

· Future Spend: 35,000,000 x 1.20 = $42,000,000

· Increased cost: $7,000,000

Coverage Bot Potential:

· 15% of call volume is related to Coverage

· Assume that 33% of callers who interact with the bot will ask to be routed to a CSR immediately, which means

· 10% of call volume can be covered by Coverage chat bot

Putting these numbers together, we can come up with the below value potential:

· Future Spend w/ Coverage Chat Bot = Current Spend x (Growth Target – Coverage Bot)

· In other words, $35,000,000 x (1.20 – 0.10) = $38,500,000

This gives us our Cost savings estimate for the Coverage chat bot:

· Future Spend - Future Spend w/ Coverage Bot = $42,000,000 - $38,500, 000 = $3,500,000 annual cost savings

By implementing the Coverage chat bot – we can save this health insurance company $3,500,000 per year in operational costs related to expansion, and even further as growth exceeds the target of 20%.


We’ve just determined that implementing the Coverage chat bot can save an estimated $3,500,000 during its first year of implementation. Now the next step is figuring out how complex seeing the project to completion will be.

To determine how feasible the Coverage chat bot is, we need to ask ourselves a few questions from two different fronts – the business end and the technical end. Let’s start with the business end:

Business Questions:

· Does the project have an executive sponsor?

· Do we have the correct resources available to deliver this project? Do they have capacity? (text/audio conversation transcribers, Dialogue Designers, Bot Developers, backend developers, Voice Engineering, etc.)

· How much change management will this require downstream? Does this impact how CSRs currently do their job? Will they need additional training?

· How about risk and compliance? Is there PII/PHI data? What about HIPAA?

Technical Questions:

· Do we need the bot to support chat or voice? Does it need both?

· How many conversation logs do we have logged or recorded? How difficult will they be for transcribers to access?

· Has a tool suite been selected or do we need to POC different products? If not, how much will procurement of the tools and infrastructure cost?

· How complex will the infrastructure workflow look? How many APIs would we need to access? Is it expected to run on-prem? Cloud only? Hybrid?

· Is there a DevSecOps workflow that can be utilized for CI/CD, operationalizing, and monitoring? How about MLOps to track performance of any Speech To Text models over time?

There is a lot to consider, but answering the above when vetting a new potential Conversational AI use case can save a lot of pain upfront when you determine the level of effort and understand gaps or risks involved.

Putting it all together

With a value proposition and feasibility determined, you can properly create a roadmap of Conversational AI use cases to implement over time and prioritize them accordingly. Also, if you are looking for internal sponsorship, having clearly defined value propositions goes a long way in securing executive interest.

About Cedrus: If you’d like a guided approach in brainstorming and planning Conversational AI opportunities on your way to Enterprise-wide AI, Cedrus Digital specializes in the AI transformation journey for companies of all sizes. Come work with our experts to set you on your path in not only Conversational AI, but NLP, Vision, Predictive Analytics, and Knowledge Graphs as well. Besides brainstorming, we specialize in the planning, management, and delivery of AI projects. Let’s partner together.

Like what you’ve read? Stay tuned to our blog for regular posts from our AI experts including best practices, tool selection, the value that each area of AI can provide, case studies, use cases, and more!

Ez Nadar is Head of AI Solutions at Cedrus Digital. He helps customers brainstorm, prioritize, plan and deliver Enterprise-Wide AI solutions

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Mar 22, 2021

Looking forward to the next one in the series

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