How to Apply AI Sales Enablement to Accelerate Revenue Growth


  • Artificial Intelligence (AI) can be applied as an underlying sales technology that improves both the efficiency and effectiveness of every salesperson and sales activity.
  • Research and empirical evidence show that when AI sales enablement delivers insights that aid a sale methodology, sales process or sales win plan, sales win rates increase by double digits.
  • Demonstrating a clear and measurable AI ROI is best done using a 3-step sequence.
Johnny Grow Revenue Growth Consulting

Research performed for the Sales Excellence Report found that the Best-in-Class sales leaders (i.e., the top 15 percent) use AI 2.3X more frequently than their lower performing peers. That's a significant difference that should not go unnoticed by sales leaders seeking revenue growth.

But technologies only become sustainable when they show a clear and measurable ROI.

So, to demonstrate an ROI for AI, it is important to begin with high impact sales use cases that drive the most important revenue outcomes and then leverage AI for technology automation, performance measurement and continuous improvement.

And one more thing. There is no need to seek out new problems or opportunities to validate AI. A better approach is to apply AI to your existing sales challenges and growth opportunities.

A 3 Step Approach to Maximize AI for Sales Growth

The Johnny Grow Artificial Intelligence Framework integrates prescriptive use cases, measurable sales outcomes and Salesforce Einstein AI technology.

Here is our three-step sequence to demonstrate how AI drives the most important sales results.


Identify the Highest Impact Sales Use Cases

Sales enablement with AI is less about the technology and more about the top-line revenue results. That's why we start with prioritized sales outcomes and then assemble the use cases, data, and technology to achieve them.

Several AI sales use cases are shown below.

Einstein AI for Sales

See the #sales use cases and revenue outcomes that are improved with #AI technology.

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To organize and add context to sales use cases, Gartner recommends using an information capabilities framework. We have standardized on this type of framework to support multiple types of sales use cases and make repeated use of a common set of interfaces and technologies.

We typically create salesforce use cases in agile user story formats so that they have specific goals, stated beneficiaries and clear acceptance criteria. Sales-enabled AI use cases will vary by client and industry but should always be supported with evidence-based best practices that fully leverage the technology to achieve one or more of the five overarching sales outcomes shown in the above diagram.


Prepare Your Data

Data quality is a precursor to AI success.

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

A data quality program ensures lead, customer and opportunity 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 becomes compromised at different times, you will also need continuous data quality measurement. To aid data quality programs we generally use a data quality dashboard.

Customer Data Quality Dashboard

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

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

Sales teams have lots of data.  But most do not make much use of it.

In fact, while many companies claim data is their most valuable asset, a research report from IBM shows that most companies only use about 2 percent of their data.

Once you have accurate and complete data, you need to assemble a data transformation process to convert raw data into insights and actions that improve salesperson performance. A sample salesforce data transformation solution architecture is shown below.

Sales 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 which sales leads are the most likely to make a purchase, which sales win plans need more differentiation or a more compelling value proposition, or which sale opportunities need engagement?


Apply Einstein AI Technology

Now it's time to apply AI software technology.

This step is greatly facilitated with Salesforce Sales Cloud with its Einstein AI. This packaged technology reduces technical barriers and put AI configuration into the hands of business analysts and sales managers.

Salesforce Einstein

The Point is This

The most successful sales leaders are defined by their ability to collect and curate accurate customer and sale opportunity data, transform that data into actionable insights and prescriptive recommendations, and apply those insights with every selling interaction.

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 AI for the salesforce is that clients achieve an average payback period of 7 months and an average ROI of 148 percent at the end of the first year. The ROI increases in subsequent years as most of the investment has been fully absorbed and the AI algorithms improve over time.

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