When it comes to maximizing growth and revenue, you may be missing something — something big. It’s the harnessing of predictive AI to enhance and deploy sales performance management (SPM). Companies with sizable sales forces — whether in the technology, medical device, pharmaceutical, industrial, or consumer goods industry or some other sector — must consider adopting AI-driven SPM.

Consider the example of NovaMed, a fictitious medical device business based on a composite of companies I’ve worked with and observed. In 2022, the company faced a challenging crossroads: Revenue had declined for three consecutive years, and the previous year’s annual sales had fallen short of targets by 20%.

A closer look suggested that there were significant problems in the sales organization and its processes. Specifically, an outdated sales-territory structure had resulted in overcrowded markets, with too many sales reps fighting over too few accounts. Meanwhile, misaligned quotas and unmotivating incentives resulted in uneven performance across the broader sales team and individual groups within it. As NovaMed’s top sellers departed in frustration, revenue plummeted, helping to explain much of the downward business trend.

To address the problem, NovaMed leadership worked with outside consultants to implement AI-driven SPM — the strategic alignment of territories, quotas, and incentives through use of precision forecasting to adapt to market dynamics — which ultimately motivated and equipped the sales force to deliver much better results across the board. In situations like this, predictive AI emerges not merely as a tool but a critical application that powers and enhances SPM’s transformative impact in driving revenue, growth, and profitability through the sales engine.

Indeed, using predictive AI to inform SPM decisions became the secret sauce that enabled NovaMed to turn its sales process from woefully inefficient to a strategic advantage within two years. But it required thoughtful movement up a learning curve — from sales data chaos to clarity, insight, and market-leading performance.

This article will make the case to integrate AI-driven SPM into sales strategies and processes, highlighting five priorities organizations must set to make this work. While I will focus on a medical device business example, the ideas and advice below are relevant to any sales-focused business.

Predictive AI and SPM: New Power to Motivate Sellers

While companies have long harnessed value from SPM, predictive AI has turbocharged its potential impact. Many leaders across sectors recognize the general value of artificial intelligence: According to a 2023 SBI survey of CEOs, 94% of executives are eager to integrate AI into their sales programs. But the same study found that 87% have neither attempted to use AI in this way nor understand how to proceed.

That striking gap between desire and expertise/execution results in too many sales-focused businesses sticking with outdated methodologies and failing to take advantage of AI. This ultimately yields diminished win rates; extended, overlong sales cycles; disengaged sales reps; and unmet revenue and profit goals. These challenges are exacerbated by legacy technologies, multiple versions of spreadsheets, error-prone manual processes, and operations siloed across strategic planning and incentive compensation design.

Part of the problem is that attempts to harness AI to enhance SPM run into organizational hurdles, including doubts about AI’s decision-making capabilities, concerns about job security, cost issues, and low competence in implementation. In 2020, for example, McKinsey reported that only 15% of business machine-learning projects ever succeed. Still, the potential outsize gains from implementing SPM with AI through a thoughtful, comprehensive approach should make the effort worthwhile.

Indeed, top executives across industries recognize the power of getting AI-driven SPM right. For example, John Waldron, PepsiCo’s director of total rewards and global compensation, said this about applying this kind of SPM to optimally deliver the iconic food-and-beverage business’s sales strategy: “I want to make SPM the skeletal system of sales compensation because it really is about operationalizing and enabling what you want to do. … Sales comp, to me, is way more than pay mix, leverage, and KPIs, because ultimately what we’re trying to create is motivation.”

“The value of AI-driven SPM is that it realigns territories, quotas, and incentives, which ensures that sales teams are motivated and equipped to succeed,” said Marc Altshuller, CEO of Varicent, an SPM software provider.

In short, the time to develop cutting-edge AI-driven SPM capabilities is now.

How AI-Driven SPM Fuels Sales: Five Drivers

Based on my experience working with sales organizations at a variety of companies, these are the primary ways that AI-powered SPM helps generate better sales results.

Forecasting accuracy. AI analyzes historical data and market trends to provide highly precise sales forecasts, empowering organizations to make strategic decisions and investments with confidence. For example, AI-driven forecasting enables leaders to anticipate market shifts and proactively adjust their sales strategies rather than relying on reactive, instinct-based planning.

NovaMed used AI to more accurately identify revenue streams and match inventory levels to expected demand — better anticipating customer needs. The better forecasting also helped leaders understand how much a given customer was likely to spend over time.

Account scoring. AI-driven SPM uses behavioral and other data to score and prioritize customer accounts by potential value, allowing sales teams to focus their efforts on the right opportunities. This AI-powered account prioritization not only boosts conversion rates but also ensures that resources are allocated to the most promising accounts, thus enhancing the experience of high-value customers.

Too many sales organizations and reps make the mistake of trying to close every deal, including courting prospects that are less likely to buy much or at all, as was the case at NovaMed. The integration of predictive account scoring kept the sales team focused on the right accounts to maximize sales efficiency, growth, and profits.

Territory and quota optimization. AI-driven insights uncover intricate data patterns related to sales territories and quotas, enabling their optimization. Again, this ensures that sales efforts are strategically targeted for maximum efficiency.

There are two specific ways businesses benefit from these types of optimization. First, by understanding likely customer behavior, a company like NovaMed can do a better job of setting appropriate levels of incentive compensation — such as establishing more effective quotas/targets that hit the sweet spot between too easy and too challenging, to create optimal motivation levels for salespeople.

Second, this optimization helps companies allocate salespeople to specific accounts or territories in a fair, reasonable way. Naturally, if one seller is assigned to a territory that has all or most of the top accounts, they have disproportionate earning potential relative to their peers — and, likely, too much work. Consequently, assigning quotas and territories based on accurately predicted values goes a long way toward keeping sales professionals happy and productive while promoting transparency and fairness for these processes.

Seller capacity planning. Relatedly, AI-driven SPM helps determine the optimal sales force size for various market segments, accounting for the complexity of sales cycles and enhancing overall productivity by assigning the right number of sellers with the right skills to the right target markets.

This has become increasingly important as the speed of sales-planning cycles rises: Businesses must react to changing market and customer demands quickly, efficiently, and strategically. Regularly assessing capacity allocated to accounts and markets provides critical data to update AI predictions and improve iterative planning.

Cross-functional alignment. Finally, predictive AI facilitates real-time collaboration and integration across SPM-related functions, breaking down silos and enabling a unified approach that enhances decision-making, performance-tracking, and returns. This is about improving revenue operations broadly — a critical goal.

All of this work certainly involves challenges, both cultural and organizational. Succeeding with predictive AI means getting sales leaders to embrace the use of data and algorithms for better decision-making. Creating a data-driven culture can be an uphill battle, especially given that many sales leaders still think of sales as relationship-driven, not data-driven. A true commitment to changing the way decisions are made could also require an organizational reshuffling, a rethinking of organizational incentives, and more. Otherwise, predictive AI will struggle to drive the business forward.

The key is to think of sales as part of a “revenue engine” and to consider sales revenue as just one piece of the puzzle. That is, when a company’s marketing, sales, and customer success teams all share common data and insights across the entire revenue cycle, they will start to use their resources more effectively and strategically to drive the outcomes that actually matter. This happens through data sharing, joint goal-setting, and holistic performance monitoring across the entire sales ecosystem. Such alignment also reflects how AI output is critical to very senior executives, such as the relatively new and high-stakes role of chief revenue officer.

NovaMed was ultimately able to implement all of the drivers noted above to bring about a seismic shift away from the inefficient, manual processes of the past, redefining its SPM strategies and processes to reverse its revenue decline and unlock new growth.

Making It Work: Five Organizational Priorities

To fully realize the benefits of these drivers, organizations must prioritize critical capabilities and resources, including the following:

• Predictive analytics and AI integration, to enable forecasting of sales trends and provide actionable insights to optimize sales strategy and incentive programs.
• The development of SPM platforms that support seamless integration across sales functions, including territory management, quota-setting, incentive and compensation management, forecasting and analytics, performance monitoring, and reporting.
• Effective data management, to ensure a solid foundation for data integration, using AI-driven insights and connected processes effectively.
• Adaptive and continuous planning carried out in regular, iterative cycles that allow sales organizations to remain agile and responsive.
• An environment of communication, transparency, and shared goals, to facilitate stakeholder engagement and collaboration.

NovaMed took a thoughtful approach to these factors as part of its transformative shift to AI-driven SPM. Its leaders carefully considered the company’s investment in AI technologies and set clear expectations around how predictive AI should be used and the benefits that were expected to accrue. They dedicated real resources to training the sales team on how to use AI, with a consistent and clear message that AI was meant to help, not replace, sales leadership. Mindful of these challenges, NovaMed was able to successfully navigate the technical, organizational, and behavioral challenges that are critical to the success of any foundational shift.

Across sectors, leaders are rethinking SPM and recognizing that integrating predictive AI is no longer a futuristic concept but a present-day imperative. For those ready to step into this new era, predictive AI not only redefines SPM but also helps forward-thinking companies navigate today’s market complexities with agility and insight.

The post "Five Ways Predictive AI Can Improve Sales Performance Management" appeared first on MIT Sloan Management Review