A 3 Step Process to use AI to Transform Customer Service and Achieve a Triple Digit ROI


  • Artificial Intelligence (AI) is an underlying customer service technology that improves both the efficiency and effectiveness of almost every contact center service.
  • Research and empirical evidence show that when AI supports customer service processes, customer satisfaction improves by double digits.
  • And AI as part of customer service doesn't just benefit customers, it delivers an improved agent experience which increases agent productivity and decreases staff turnover.
  • Demonstrating a clear and measurable AI ROI in the contact center is best done using a 3-step sequence.
Johnny Grow Revenue Growth Consulting

Research performed for the Customer Service Excellence Report found that the Best-in-Class customer service leaders used AI 3X more than their lower performing peers. That's a significant variation that should not go unnoticed by anyone seeking customer service or call center improvements. Among the Best-in-Class contact centers, AI was described as a 'game changer' and driver of contact center progress.

Technologies become sustainable when they show a clear and measurable ROI.

So, to demonstrate an ROI for AI, it's important to begin with high impact use cases that drive the most important customer service outcomes and then leverage AI with CRM as the technology enabler.

And one more thing. Instead of seeking out problems or opportunities to validate AI, apply AI to your existing contact center problems or opportunities.

A 3 Step Approach to Maximize AI ROI

The Johnny Grow Artificial Intelligence Framework fully integrates prescriptive use cases, measurable business outcomes and AI technology.

Here's our three-step sequence to demonstrate how AI drives the most important customer service performance goals.


Identify the High Impact Customer Service Use Cases

Succeeding with AI is less about the technology and more about the outcomes. That's why we start with prioritized business outcomes and then assemble the use cases, data, and technology to achieve them.

Several AI customer service use cases are shown below.

AI for Customer Service

See the Customer Service use cases and contact center outcomes that are improved with Artificial Intelligence technology.

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The use cases vary by client and industry but are all supported with evidence-based best practices that fully leverage AI to achieve one or more of the five overarching customer service outcomes shown above.


Prepare Your Data

AI success is dependent upon accurate data.

Customer data can be a company's most valuable asset. However, it decays at about 2 percent per month so without a CRM data maintenance program that asset can quickly become a liability.

A data quality program ensures customer data is accurate and complete, secured and available to those who need it, and in compliance with company policy and regulatory requirements.

And because different data will become compromised at different times, you will also need continuous data quality measurement. To aid data quality programs we often supply a data quality dashboard.

Customer Data Quality Dashboard

The data dashboard surfaces data quality deviations in near real-time so they can be remedied before they exacerbate and spread to multiple systems.

Recognize that a 10 percent increase in data quality will outperform 100 percent improvement in an AI algorithm.

Every contact center has data – lots of data.  But relatively few actually do something with it.

In fact, while many companies claim data is their most valuable asset, a research report from IBM shows that for most companies only about 2 percent of data is unearthed and made actionable (Source: IBM Institute for Business Value analysis; The Cognitive Enterprise, Part 1.)

So, once you have accurate and complete data, you need to create a data transformation pipeline to convert raw data into insights and actions that improve outcomes. A sample customer service data transformation solution architecture is shown below.

Customer Service Data Transformation Pipeline

Data only becomes an asset when it is converted from a raw material to a finished product of information or insight. Only then will the data provide answers to powerful questions such as how to resolve a case, how to proactively prevent a case from occurring or how to apply data to deliver a differentiated customer experience.


Apply Artificial Intelligence Software

Now it's time to apply AI technology.

This step is greatly facilitated for contact centers using popular CRM software systems such as Microsoft Dynamics 365 with its Azure Machine Learning and Salesforce with its Einstein AI. These packaged systems remove technical barriers and put AI design and configuration into the hands of business analysts and contact center managers.

These AI software tools can mine, harvest and structure data into algorithms and models to deliver logic apps, propensity models, probabilistic methods, next best actions and answers with confidence levels. They can leverage Natural Language Processing (NLP) to allow agents to interrogate data and extract answers and insights with Question-and-Answer voice commands. And best of all, they can improve accuracy over time as they accumulate more data and self-learn.

Every customer interaction and case can be improved when more relevant data is converted to actionable insights. That's why everybody in the contact center benefits from AI.

The Point is This

The most successful contact centers are defined by their ability to collect and curate accurate data, transform data into actionable insights and prescriptive recommendations, and forward this information to the agents or self-service channels where they can resolve customer cases and create differentiating customer experiences.

It's a complex undertaking for sure, which is why those who succeed will achieve competitive advantage over those who don't.

Our experience in implementing CRM with AI in the contact center is that clients achieve an average payback period of 6.5 months and an average ROI of 139 percent at the end of the first year. ROI increases in subsequent years.

Data is the fuel, AI is the engine, and insights are the destination.