Artificial intelligence has made its mark on the finance industry, and its impact will only continue to grow in magnitude in the coming years. As such, it’s no longer just the tech wizards and industry analysts who must understand the core tenets of AI— anyone who works in finance should develop a general knowledge of AI and its implications for their role.
But, while we’re all familiar with AI as a concept, the complexity and multifunctionality of the technology can be difficult to wrap your head around. Understanding the terminology alone can be challenging— especially when it comes to the topic of today’s blog post: the difference between artificial intelligence and machine learning.
AI and machine learning are often used interchangeably, typically because the delineation between the two can’t exactly be summed up in a few sentences. But, AI and machine learning are indeed distinct concepts— and understanding the fundamentals of each is essential to understanding their impact on finance technology.
After you read today’s blog post, you’ll no longer scratch your head when these two terms cross your radar. Read on to learn the difference between AI and machine learning, and how these technologies affect commission management.
What is artificial intelligence?
The term artificial intelligence refers to a field of technologies that have the ability to mimic human abilities or behaviors. AI systems enable human-like problem-solving capabilities in machines through a combination of computer science and complex datasets.
Though we often talk about AI as one distinct technology, it’s actually an umbrella category that encompasses many different forms of technology, including deep learning, robotics, natural language processing— and machine learning.
What is machine learning?
Though it’s often conflated with artificial intelligence, machine learning is actually a specific application or subcategory of AI. It refers to an AI-powered technology’s ability to use algorithms to analyze data and autonomously make informed, human-like decisions or improvements based on that analysis.
The word “autonomously” is the critical differentiator. Technology enabled with machine learning is able to perform actions on its own, without you instructing it to perform those specific actions.
An example of AI vs. Machine Learning
Let’s illustrate the distinction and relationship between AI and machine learning with an everyday example. Say you’ve just purchased a sleep aid technology for the purpose of improving your sleep quality and consistency. The tool has voice recognition capabilities, so you’re able to get into bed and say “Power on. Set alarm for 8:15 a.m. Play relaxing music for one hour.”
The product uses AI to recognize your voice and perform the commands you’ve spoken aloud. Now, let’s say that one week after purchasing the sleep tool, you turn it on and receive the following message:
“I’ve analyzed your sleep patterns over the past seven nights. Based on your average time-to-fall-asleep of 32 minutes, try getting into bed at 10 p.m., forty minutes earlier than your current average of 10:40 p.m.”
This is an example of machine learning. The AI-powered tool has analyzed the information it’s gathered and “learned” key insights about the subject (your sleep), which it has then used to provide data-driven recommendations.
The Benefits of AI for Compensation Management
The evolution of AI is groundbreaking for finance professionals, due in large part to the unavoidable complexity of compensation management and other financial processes. So, before we dive into machine learning specifically, let’s review the key benefits of AI in all its forms.
Increase operational efficiency.
Compensation management is an inherently inefficient process. Creating and improving commission plans requires you to build and change complex formulas, while backtracking to address the inevitable errors or miscalculations that arise.
AI can streamline this process significantly by automating plan creation and maintenance. It can pull, process, and organize data, and then build plans based on the rules you input into the AI-powered system. Telling AI what to do instead of doing it yourself will not only speed up the process, but also reduce the potential for human error.
Recommended reading: AI Won’t Replace Sales Comp Managers: Here’s Why
Simplify complex financial systems.
AI can also mitigate learning curves and make compensation plans easier to understand— for you as well as for your commissioned employees. With natural language processing capabilities, AI can translate complex formulas and commission calculations into language that’s easy to understand, whether you’re a seasoned expert or you’re managing plans for the first time.
Likewise, the same natural language processing capabilities can be leveraged for compensation communication between the finance team and commissioned reps. A rep who doesn’t understand their commission statement won’t have to contact you with questions if an AI-powered chatbot can provide them an answer in a matter of seconds.
Recommended reading: The Impact and Administrative Overhead of a Bad Sales Commission Process
Enable faster, more informed decision-making.
If you’ve managed commission plans before, you know that any change to sales compensation can’t be made lightly. You have to consider so many factors, from sales motivation to your business’s objectives, while also analyzing a wealth of historical commission and customer data.
AI’s ability to rapidly analyze data will help you make these decisions much faster. AI algorithms continuously gather and organize data in real-time, so the insights you need to make an informed adjustment are always at the tip of your fingers.
Recommended reading: A Day in the Life of an Effective Sales Compensation Manager
Machine Learning: Key Applications for Compensation Management
Now that we’ve covered some benefits of AI in general, let’s zero in on machine learning specifically. Here are some of the primary ways the analytical and predictive capabilities of machine learning can improve your approach to compensation management.
1. Optimize commission plans.
Designing the optimal commission plan is a delicate balancing act. Your goal is to offer the right incentive to drive specific sales behaviors. But, it’s nearly impossible to strike this perfect balance with intuition and manual analysis alone. That’s why commission plans often miss the mark— either offering a suboptimal pay mix that doesn’t drive performance, or overshooting incentives and driving unnecessary costs.
Machine learning can help you strike the perfect balance between your incentive and your desired outcome. When you apply machine learning technology to historical data, it can uncover patterns in highly complex datasets— patterns that you’d never be able to identify on your own.
For example: let’s say you want to increase the commission rate for deals involving a new product. You’re unsure if a 3% commission increase is the right change to make, or if your targets could be met with a 2% increase. Machine learning has the power to analyze and interpret past commission data and predict what a 3% increase will yield, vs. a 2% increase.
Recommended reading: Building Your First SDR Commission Plan
2. Improve commission forecast accuracy.
Machine learning’s ability to predict outcomes is one of its most valuable benefits. As a machine learning model gathers a larger quantity and diversity of data, it grows more powerful in its ability to identify patterns— which it then uses to predict future patterns that can greatly inform your sales forecasting.
Just think about how many trends and correlations lay hidden in the wealth of data your company has accrued. For example, what’s the relationship between deal size and customer churn? How do specific shifts in the marketplace impact your business’s revenue? How does an increase in commission rate influence sales numbers?
On your own, you might be able to answer a few of these questions by drilling down into specific datasets. But, no amount of manual analysis could ever unpack the hundreds of variables that influence future commission outcomes. Therein lies the power of machine learning. It can analyze all these variables at once and show you what patterns are hiding in that overwhelming web of data.
Recommended reading: Ready or Not: Busting the Myth of Commission Automation Readiness
3. Personalize sales incentives.
No two sales reps are the same. In a perfect world, you’d be able to tailor each salesperson’s commission plan and overall incentive compensation package to their individual performance, strengths, and preferences. But, a wholly personalized strategy is simply infeasible given the amount of analysis, communication, and iteration it requires.
With machine learning, however, you can introduce an element of personalization to your commission plans without expending a massive amount of time and resources. Machine learning can recommend personalized incentives and rewards based on analysis of individual rep performance.
These recommendations will not only help you increase motivation for individual reps, but keep motivation consistent across a large and diverse sales organization.
Recommended reading: Using Automation to Address Sales Burnout
4. Enable more agile commission adjustments.
Your company, customers, and overall business sector are constantly shifting— and keeping up with these shifts is one of the great difficulties of managing commission programs.
Machine learning can enable you to become more dynamic and flexible in your ability to adjust commission plans on the fly. In real-time, it can track and highlight changes in sales data, market demand, customer activity, and more important factors that influence your organization’s selling activities.
Instead of playing catch-up, you’ll have a constant stream of relevant insights that will help you identify top sales performers, high-value customer segments, anomalies in recent commission earnings, and more. The speed and digestibility with which machine learning delivers these insights will enable a far more agile approach to plan improvement.
Recommended reading: Re-programming Commission Operations for Maximum Growth
5. Improve sales productivity and effectiveness.
One of the key benefits of machine learning is the speed with which it can perform “what-if analysis”, making it an invaluable tool for sales professionals who need guidance on what selling activities and behaviors will be most effective.
Sales reps are always hungry for more insights into what they need to do to reach their goals. If they come to you with questions, you might be able to comb through past sales data and give them an educated guess on what accounts to target or how many deals they’ll need to close to hit their quota.
But, machine learning can predict outcomes and advise sales reps on a much greater scale. It can respond to the scenarios that sales reps present and provide much more specific, accurate feedback on what actions will result in what outcomes. The result? You save time on analysis and communication, while sales reps have constant visibility into what they should do to achieve success.
Recommended reading: A Foolproof Framework for Better Incentive Communication
AI and machine learning are incredibly vast and multifunctional technologies. It’d be impossible to review every single benefit of AI, or every way that machine learning can support your day-to-day workflows— not to mention the future use cases that have yet to emerge in this era of constant AI innovation.
But, you don’t have to become fluent in AI to realize its benefits for finance professionals and compensation management programs. All you need is a basic knowledge of what AI can do, what makes machine learning unique, and how these technologies can support and enhance your role. With that understanding, every new advancement in AI will become an opportunity for you to reach new heights as you evaluate new commission or finance solutions.
Spiff is a new class of commission software that combines the familiarity and ease-of-use of a spreadsheet with the power of automation at scale- enabling finance and sales operations teams to self-manage complex incentive compensation plans with ease. Spiff is designed to facilitate trust across organizations, motivate sales teams, increase visibility into performance and earnings, and ultimately, drive top line growth. The platform’s intuitive UI, in-depth reporting capabilities, and seamless integrations make it the first choice among high-growth and enterprise organizations.