To better understand where the market is headed, G2 interviewed 98 enterprise SPM practitioners and leaders across revenue operations (RevOps), finance, and sales. The study examines how organizations are using AI today, what's slowing broader adoption, and where leaders expect AI to deliver the greatest value over the next several years.
If you’re evaluating AI in SPM, the findings spotlight where organizations are succeeding and where challenges remain with AI Adoption in Sales Performance Management.
Before diving into the full research, here’s what sales, RevOps, and finance leaders should know.
AI and Sales Performance Management Today
The research clearly shows AI is now a key part of everyday Sales Performance Management. Rather than experimenting with isolated pilots, most organizations actively use AI for at least part of the SPM lifecycle.
That doesn’t mean AI has become fully integrated. Instead, many organizations are taking a measured approach, expanding AI only where confidence is high and risk is relatively low. The study suggests the next stage of AI in SPM is about helping teams move from selective adoption to embedded intelligence.
AI is Used Where Little Risk Exists
The research shows AI adoption naturally begins in low-risk workflows before expanding into higher-stakes business processes.
Forecasting, pipeline visibility, and performance analytics have emerged as the most common use cases because leaders can quickly compare AI recommendations against actual business
outcomes. If a forecast improves accuracy or identifies an at-risk opportunity, the results become visible almost immediately.
Compensation and quota decisions are different. Organizations remain cautious about using AI in compensation and quota decisions until they can trust its recommendations.
Selective-to-Embbed is the Current Status
Only 2% of respondents reported not using AI at all. Meanwhile, 93% already use AI somewhere within their SPM processes. The biggest opportunity lies in helping teams embed AI more deeply across the SPM lifecycle.
Many organizations have moved beyond experimentation, but AI remains concentrated in a handful of trusted workflows rather than embedded across planning, forecasting, compensation, and territory management.
AI is Being Used Alongside Manual SPM Efforts
The report found AI is often layered onto existing manual processes instead of replacing them.
Many organizations still manage compensation and quota planning via spreadsheets, reporting tools, or internally developed systems. Rather than operating on a unified technology foundation, AI frequently sits alongside disconnected systems and manual workflows.
The research makes one thing clear: successful AI in Sales Performance Management depends as much on a strong operational foundation as it does on AI itself.
So, What’s Blocking Deeper AI Adoption in Sales Performance Management?
Organizations are willing to use AI where they trust the outcomes, but are far more cautious when AI influences decisions that impact revenue, compensation, or employee pay.
The barriers aren’t primarily technical. Instead, they're rooted in trust, data quality, governance, and organizational readiness. Overcoming these challenges will determine which organizations successfully move from selective AI use to embedded AI.
Trust, Explainability, and Validation
Nearly half (47%) of respondents identified trust, explainability, and validation as the single biggest barrier to expanding AI across SPM. Leaders want to understand how AI reached those recommendations and be able to defend them when decisions affect quotas, forecasts, or compensation.
As AI begins influencing higher-stakes business decisions, explainability becomes a business requirement rather than a technical feature. Organizations are far more likely to trust AI when they can clearly understand the reasoning behind its recommendations.
Data Quality
Even the most advanced AI depends on accurate, reliable data.
32% of respondents said data quality and confidence in their underlying information create major barriers to broader AI adoption. Incomplete or disconnected data limits AI's ability to generate meaningful insights and reduces confidence in its recommendations.
For many organizations, improving data quality is the first step toward expanding AI beyond forecasting and analytics into more strategic processes.
Change Management or Governance
21% of respondents identified change management and governance as their biggest challenge. Introducing AI into SPM often disrupts established workflows, approval processes, and decision-making responsibilities.
Technology alone doesn't transform business processes. Successful adoption requires clear governance, defined ownership, and organizational alignment.
Workflow Resistance
Beyond formal change management, organizations also face day-to-day workflow resistance.
The study found that 71% of respondents experience workflow inertia as AI expands into compensation, quota, and planning processes. Employees naturally resist changing familiar workflows, especially when those workflows affect compensation or sales performance.
The Trust Concern with AI in SPM
According to the G2 research, 63% of respondents want AI validated on real or their own data before they'll trust its recommendations. Generic demonstrations or marketing claims aren't enough. Organizations want evidence showing AI can deliver accurate, repeatable results within their own business environment.
This finding reflects a broader shift in buyer expectations. Features alone aren't enough. Organizations want confidence that AI can support critical decisions involving revenue, planning, and compensation.
Where Using AI in Sales Performance Management Has Significant Value
Although organizations remain cautious about expanding AI into every aspect of SPM, the research identifies several areas where AI delivers measurable business value.
The most successful use cases share one important characteristic: they're easy to validate. Leaders can quickly measure whether AI improves accuracy, reduces risk, or helps teams make better decisions, creating the trust needed for broader adoption.
Forecasting and Performance Analytics
Forecasting and performance analytics continue to lead the way for AI in Sales Performance Management. 90% of respondents said these areas deliver the greatest value today.
Forecasting is a natural starting point for AI because leaders can quickly measure whether its recommendations improve business performance.
Quota and Territory Planning
Only 9% of respondents said quota, territory, and broader planning optimization represent AI's greatest value today. However, that relatively small number reflects current adoption, not future potential.
As organizations gain confidence in AI, many expect it to play a larger role in optimizing quotas, balancing territories, and supporting strategic planning. Rather than simply reporting what happened, AI has the potential to help organizations make smarter decisions before plans are finalized.
The Impact of AI in Sales Performance Management
While much of the conversation around AI focuses on future possibilities, organizations already report measurable results today. The clearest gains come in areas that are easy to measure, including compensation anomaly detection, forecasting accuracy, and pipeline visibility. These early wins help build confidence for broader AI adoption.
Anomaly Detection & Disputes
AI is already reducing manual work in compensation management. One organization reported 40% less time spent validating compensation data and 25% fewer disputes per cycle after implementing AI for anomaly detection. Overall, 47% of respondents cited improvements in accuracy, speed, and risk detection as key operational benefits.
Lifecycle Value
Organizations believe AI can improve the entire SPM lifecycle, but legacy processes remain a barrier. Teams still relying on spreadsheets and homegrown systems often spend 40% to 50% of their time resolving data issues and manual adjustments instead of focusing on strategy and growth.
Forecasting Accuracy
27% of respondents said AI delivers its most measurable value through improved forecasting accuracy, while many also spotlight better pipeline visibility and earlier identification of at-risk deals.
As organizations see AI consistently improve forecasts, confidence grows, paving the way for broader adoption across SPM.
Does AI In Sales Performance Management Need to Be Governed?
The report makes clear that as AI takes on a larger role in forecasting, compensation, and planning, organizations want people to remain accountable for decisions that affect employees and business performance.
The study found 86% of respondents favor human-in-the-loop governance for compensation and forecasting decisions.
According to the report: “Trust is earned as the system proves itself, which is exactly why explainability and proof matter so much. The organizations that design for human-led governance first give AI the conditions it needs to earn a wider mandate over time. Human-led is not a passing phase to be automated away; it’s the operating model buyers expect the next generation of AI to be built on.”
Where The Future of AI in Sales Performance Management Is Headed
AI adoption is no longer the story. What organizations do next will determine who gains the greatest value from AI.
Interestingly, 14% of respondents said they’re open to giving AI greater autonomy over governance decisions. While most organizations still favor human oversight, this signals growing confidence that AI will play a larger role in business-critical decision-making as trust continues to grow.
Quota & Territory Planning
While forecasting remains AI’s strongest use case today, respondents believe the next wave of value will come from planning.
The study found that 68% expect AI’s greatest impact over the next two to three years to come from quota, territory, and planning optimization. AI will increasingly help organizations determine where opportunities exist, optimize territory assignments, and recommend quota strategies before decisions are finalized.
This represents an important shift for the SPM industry. AI is evolving from a tool that analyzes past performance into one that helps organizations make better planning decisions across the revenue lifecycle.
Hybrid Pathway
The research also reveals a clear preference for how organizations want to adopt AI. 84% favor a hybrid approach, starting with packaged AI that delivers immediate value before extending and customizing it with their own systems and data.
This hybrid model allows organizations to realize quick wins while maintaining the flexibility to tailor AI as business needs evolve.
It’s also consistent with Xactly's vision for Sales Performance Orchestration (SPO). Instead of solving isolated problems, SPO helps organizations coordinate decisions across forecasting, quotas, territories, and incentives using trusted AI built on connected data.
Forecasting & Analytics
Although planning represents the future, forecasting delivers the most tangible value today.
90% of respondents identified forecasting and performance analytics as AI’s strongest use case. Organizations continue to rely on AI to improve forecast accuracy, identify pipeline risks earlier, and provide greater visibility into sales performance.
How Xactly is Using AI in Sales Performance Management
Teams succeed with AI by combining modern technology with transparent, secure systems that support confident decision-making. That’s the philosophy behind Xactly’s approach to AI.
Xactly takes a responsible approach to AI, emphasizing explainability, security, and human oversight. These commitments are reflected in Xactly's AI Guiding Principles, which ensure AI empowers revenue teams while maintaining transparency and trust.Organizations looking to move from selective AI adoption to embedded intelligence can explore several AI-powered capabilities from Xactly, including:
- Xactly Intelligent Revenue Platform: Combine planning, forecasting, incentive compensation, and analytics into a single connected system of action. AI agents help organizations identify trends, surface insights, and optimize decisions across the full revenue lifecycle, giving sales, finance, and RevOps teams the intelligence they need to plan with greater confidence.
- Xactly Intelligence: Through a Fleet of AI Agents, Xactly’s platform transforms sales and compensation data into actionable insights. From improving forecast accuracy to identifying performance trends and potential risks, the Fleet helps organizations make faster, more informed decisions.
- Xactly Intelligence Agents: A Fleet of Agents automates repetitive tasks and delivers personalized recommendations. By assisting with analysis, planning, and decision support, these proprietary-data-fueled agents help teams work more efficiently while keeping people in control of critical business decisions.
Together, these capabilities help organizations move beyond isolated AI use cases toward connected Sales Performance Orchestration.
Start Using AI in Sales Performance Management
AI is already transforming SPM. The G2 research shows the greatest opportunity still lies ahead.
Organizations are moving beyond experimentation toward embedded AI that supports forecasting, planning, quotas, territories, and compensation decisions. Success depends on building trust through explainability, high-quality data, responsible governance, and connected platforms that enable AI to deliver value across the entire revenue lifecycle.
If you’re ready to move beyond AI experimentation and build a more connected SPM strategy, request a demo of Xactly's Intelligent Revenue Platform to discover how trusted AI can help you orchestrate sales performance.
