4 Artificial Intelligence (AI) Use Cases For Revenue Growth

Each year, sales compensation leaders face the challenge of refining processes, boosting efficiency and driving peak performance across their teams. Enter artificial intelligence (AI) and machine learning (ML)—game-changing technologies that are revolutionizing the way sales plans are designed, distributed and managed. By leveraging AI and ML, sales leaders are improving forecasting accuracy, driving better seller performance and, ultimately, boosting revenue.

In this article, we'll explore how AI is reshaping the sales compensation landscape and accelerating business growth. Plus, we’ll hear insights from three industry experts who are at the forefront of AI-driven sales compensation management:

• Deima Tankus, AI Research Leader, The Alexander Group
• Davis Giedt, Director, Analytics & Research Practice, The Alexander Group 
• Andrew Morales, Senior Director, GTM Strategy & Operations, CaptivateIQ

Here are four use cases that highlight key opportunities for AI to unlock greater efficiency and productivity for sales compensation programs. 

AI Use Case #1: Increased Planning Clarity and Communication 

For the first use case, compensation leaders transformed a static compensation plan PDF into a range of interactive formats, such as: 

• An instructional video using DeepbrainAI
• A game-based training sequence using Attensi
• Case studies highlighting plan attainment scenarios using ChatGPT

This team also trained an internal chatbot to handle compensation plan and administration inquiries. The result? These interactive formats improved plan communication, nudged seller behavior, and empowered predictive compensation models. 

Improving Plan Communication

“This first use case example really drives home the point of how sales actions will lead to payouts—both at the start of the plan and then at any point that questions might come up,” explains Deima. “Making an instructional video using DeepbrainAI also makes it a little bit more flexible if you're communicating the plan to different geographies, so that you can easily plug in a different language.”

Davis continues, “This is really interesting, because it takes away the game of telephone from the plan communication element. This is key, especially when you're talking about larger businesses where you've got thousands and thousands of sales reps. Then, we've got different geographies. We know generative AI tools are pretty effective at translations, so that you can communicate those plans and you're just increasing the scale, as well. You can also track really carefully whether or not sales reps have watched the videos in full and even give them quizzes at the end to ensure that they're actually understanding what they're being told. It's a pretty powerful tool and reasonably easy to get set up.”

Nudging Behavior 

“Generative AI that connects to your compensation plan data, pipeline models, and/or different machine learning models is something that we're seeing a lot of chatter around,” says Deima. “This could be a small language model that ingests a set of PDFs and answers a rep's targeted questions, kind of like an enhanced search.  If it connects to multiple systems, it can answer more dynamic questions, for example: based on your pipeline and your likelihood of conversion, how likely are you going to achieve X percent of your quota by the end of the month?”

These models are a great way to streamline common conversations and questions between sellers and the compensation teams. Plus, with gamification and transparency, sellers are more motivated to reach their objectives. In fact, 37% of employed U.S.-based adults said that greater visibility of company and individual goals would spur their performance.

Powering Predictive Compensation Models

“We've seen interest from hedge funds, PE firms and internal finance teams in trying to understand and predict what percentage of reps are going to make quota based on all the opportunities in the pipeline,” shared Davis. “AI tools can, ultimately, inform management in terms of where the business is heading.”

While nearly all sales compensation leaders would benefit from this type of accurate and automated predictive modeling, these types of tools are today only employed by large organizations with significant resourcing to support them. As these models advance, we expect adoption to become more widespread. “I think there's some pretty serious prompt engineering that needs to go into these predictive models to make them effective, which is why it's only something that our largest tech companies are focused on right now,” clarifies Davis. “We expect that engineering will get a lot simpler over the next one to two years, and this type of service offering will become more tangible for small to medium-sized companies.”

AI Use Case #2: Improved Employee Experience

In our second use case, a luxury auto brand improved top-line sales with AI-enabled compensation. On a monthly basis, the platform:

• Configures incentive program payouts for sales executives and team members
• Centrally uploads, manages and distributes incentives for all sales employees
• Offers personalized payout options, with more than 1,000 ways to redeem (cash, gift cards, experiences, etc.) 

The results? The platform created a unified compensation administration process, improved the sales team engagement, achieved 93% adoption of the tool by sales teams and 11% uplift in unit sales. 

Boosting the Experience

“The use case that we're highlighting here is improving the employee experience. Now what does this mean? Mainly this means that reps get credited correctly, paid out on time and that they feel like they're getting fairly compensated for their actions,” shared Deima.

She continues, “This company had a decentralized model with dealerships all over Europe and was having an absolute nightmare of a time trying to manage the sales compensation program across different geographies with different paid philosophies. They turned to an AI-enabled sales performance management (SPM) vendor that unified compensation administration to a single platform and then understood how sales reps wanted to get their payouts and created little nudges along the way. For example, ‘You’re this many steps away from attaining your next big payout.’ They saw a huge shift in the number of sales reps that were actively using the tool, a double digit increase in unit sales with this process.”

AI Use Case #3: Better Data Accuracy

In our third use case, a telecommunications company AFL achieved 100% data accuracy within six months of use with ELT (Extract, Load, Transform) AI. 

Unlocking Data Accuracy 

“One of the biggest issues that teams face is data analytics and reporting. The main takeaway for you for this section is that you need a good data foundation in order to make any AI-generated or AI-supported insights make sense,” suggests Deima. “So, having clean and accurate data is non-negotiable for sales compensation and RevOps. AI can help really both in managing the process of ingesting data and then performing a number of data transformations to clean incorrect data.”

She continues, “Now, even if your CRM is perfectly up to date, chances are it's not the only place that you're storing your data. And, this was the case for the telecommunications company that's shown here. The team was running into a lot of issues keeping up with their disconnected data systems as they grew. They wanted to centralize everything, and automate this reconciliation process to make it easy to continue growing. This vendor had an AI ELT tool—which stands for Extract, Load and Transform data—that was really good at picking up on data similarities across these different data sources, extracting the necessary information, identifying those connection points, to then load everything into one table. In the first six months that they were deploying the system, they saw 100% accuracy in the data that was flowing through the model. So, investing in this tool was a very powerful decision that is actually going to help for growth in the years.”

AI Use Case #4: Optimized Sales Forecasting Process

In our fourth use case, a software provider used AI-enabled call monitoring to assess and optimize the sales forecasting process.

• Sales manager forecasting calls were recorded. AI transcripts were sent to the RevOps team. 
• The RevOps team used ChatGPT to query transcripts (e.g., “What topics were discussed?”, “Who spoke the most?”). 
• Team used ChatGPT to determine “best practice” characteristics of calls led by effective sales leaders.
• The team trained leaders to follow best practice guidelines with continued monitoring. 

The result? The RevOps team cut hours of idle RevOps personnel time and effectively logged call effectiveness. The team defined a concrete rubric for successful sales forecasting calls. 

Uncovering Trends and Opportunities 

“This is a case of improving the sales forecasting process with a mix of machine learning and natural language processing (NLP). In this case, a RevOps leader felt like they were wasting resources by having a representative sit in on an hour-long weekly forecast calls with sales leaders,” explains Deima. “When NLP-generated call transcripts were starting to mature, the team decided to place a bot on each call instead of a RevOps professional and then query the call transcripts to understand the success of the forecast calls. With a big enough sample, they were able to apply machine learning to understand what successful forecast makers were doing differently than those that often miss their mark and they use these best practices to then retrain their sales leaders and also start to implement some checks that alert sales leaders that their forecast might be off.”

Andrew adds, “I think it’s also interesting being able to use AI-enabled call monitoring to try to tease out themes that are happening within the underlying sales process, as well, and bringing those insights to the forecast call. For example, at CaptivateIQ we use AI-enabled call monitoring to help us surface what competitors are popping up in some of our calls or within certain components of our sales stages, where we are seeing potential gaps, etc. It's reduced a lot of the manual work for our RevOps team to go through and listen to every single call or try to tease that information out of managers. And then we're essentially creating at scale insights to the various components of our sales process and then bringing that information into our forecast calls to be able to summarize at a higher level what are some of the trends and themes that we're seeing.”

Conclusion

As sales organizations strive to meet the challenges of 2025, AI and machine learning are proving to be game-changers in sales compensation and revenue growth. From improving communication around compensation plans and nudging seller behavior, to enhancing employee experiences, ensuring data accuracy, and optimizing forecasting processes, AI is transforming how sales leaders manage and motivate their teams.

Incorporating AI into sales compensation management isn’t just about keeping up with technology—it’s about staying ahead in an increasingly competitive marketplace. As the industry continues to innovate, those who harness the full potential of AI will be poised for sustained growth and success, driving both performance and profitability for years to come.