New research from Salesforce confirms what every sales leader already knows. The gap between high performers and everyone else isn't talent. It's infrastructure.
The numbers in Salesforce's 7th State of Sales report aren't surprising. That's what makes them damning.
60% of rep time goes to non-selling work. 46% of sales pros with AI say data quality issues are actively hurting their sales. 51% of sales leaders say tech silos delay or limit their AI initiatives. 40% of managers say they don't have enough time to coach their teams.
These aren't new problems. They've been documented in edition after edition of this report, year after year, while the tools in the stack multiply and the numbers barely move.
AI Was Supposed to Fix This. It Hasn't.
The promise was straightforward: AI would absorb the administrative burden, surface the right insights, and give leaders the visibility they'd always wanted. And yet here we are. More AI tools in the stack than ever before. The same percentage of time lost to non-selling work. The same forecast calls reconstructing pipeline history from memory. The same coaching conversations that never happen because the manager spent the week forecasting instead.
The reason AI hasn't moved the needle isn't the quality of the models. It's the quality of the data they're running on.
The Salesforce report is explicit about this. The top data issues among teams with AI agents are manual errors, duplicate data, and incomplete records. Data and analytics leaders estimate that 19% of their data is completely inaccessible, and most believe their most valuable business insights are buried inside that inaccessible 19%. Tech silos aren't just a productivity problem. They're an AI problem. Siloed data limits agent outcomes and AI insights in ways that no amount of model improvement can overcome.
You can't build intelligent revenue operations on a foundation that depends on humans remembering to fill things out.
The Coaching Problem Is Actually a Data Problem
One of the most quietly devastating findings in the report: 40% of sales managers cite lack of time as their primary obstacle to coaching their reps. Meanwhile, 46% of reps say they rarely get feedback on their sales conversations. 75% of reps say they're more likely to hit their targets with a coach or mentor.
The math here is brutal. Leaders know coaching works. Reps want coaching. And yet it's not happening — not because leaders don't care, but because they're spending their limited time doing something else: chasing down pipeline data, reconstructing deal history, and trying to make sense of a forecast built on incomplete information.
This isn't a time management problem. It's a data infrastructure problem wearing a time management disguise. When the CRM requires constant interrogation just to understand what's actually happening in the pipeline, coaching gets crowded out. The data work expands to fill the available time, and the strategic work — developing people, building skills, creating the conditions for a team to perform at its ceiling — gets deferred indefinitely.
What High Performers Are Actually Doing
Here's where the Salesforce report becomes genuinely instructive rather than just sobering.
High performers aren't buying more AI tools. They're building better foundations. 79% of high performers are actively prioritizing data hygiene to improve AI outcomes, compared to 54% of underperformers. High performers are 30% more likely to consolidate to a single platform. 84% of teams without an all-in-one platform plan to consolidate their tech.
The pattern is unmistakable. The leaders pulling ahead have made a strategic decision to treat their data infrastructure as a competitive asset rather than an operational afterthought. They've recognized that the AI tools everyone has access to will only differentiate the teams that built the foundation underneath them correctly.
94% of sales leaders with AI agents say they're critical for meeting business demands. But agents need comprehensive, unified data to understand customers and the business. Without it, they produce confident-sounding outputs built on incomplete inputs, which is arguably worse than no AI at all, because it introduces false confidence into decisions that matter.
The competitive advantage isn't the AI tool. It's the data underneath it. High performers know this. That's why they're winning.
The Structural Fix
The path forward isn't another tool. It's a different kind of infrastructure — one that captures revenue data automatically at the source, structures it consistently across every team and system, and makes it available to every AI tool, every agent, and every leader who needs to make a decision from it.
This means eliminating manual data entry as the primary input mechanism. It means building a common data model that every system of record draws from and contributes to. It means treating data hygiene not as a periodic cleanup project but as a continuous, automated process that runs in the background while your team focuses on selling.
When the data foundation is solid, the downstream effects compound quickly. Forecasts become auditable rather than reconstructed. Pipeline reviews become strategic rather than investigative. Coaching conversations happen because leaders have time for them, because the data work is done by the system rather than by the people. AI agents deliver reliable outputs because they're operating on complete, structured records rather than whatever the rep remembered to log after the call.
This is the infrastructure investment that separates the teams in that top tier from the ones still working around the gaps.
What We Shipped This Month
At GTM Engine, everything we build reflects the same conviction: the data foundation is the leverage point, and solving it at the infrastructure level changes everything downstream.
This month's release reflects that directly. Forecast History gives sales leaders an auditable record of exactly how pipeline moved across any time window, so forecast calls are anchored in data rather than reconstructed from memory. The Revenue AI Report Builder turns any pipeline question into a decision-ready chart in seconds, making the data answerable in real time without waiting on a separate team to build the report. Comment Threads attach deal context permanently to the record it belongs to, so handoffs happen with full history intact and coaching feedback lives where it's actually useful. And a redesigned onboarding experience ensures the platform gets adopted rather than shelved, because infrastructure only creates value when teams actually use it.
None of these are features for their own sake. They're expressions of the same conviction the Salesforce data keeps confirming: the leaders who win are the ones who fixed the foundation first.
GTM Engine is a Revenue Data Platform built on the Common Customer Data Model, the data infrastructure that makes AI-native revenue operations possible. All statistics sourced from the Salesforce State of Sales, 7th Edition, 2025.
About the Author
Robert Moseley IV is the Founder and CEO of GTM Engine, a pipeline execution platform that’s changing the way modern revenue teams work. With a background in sales leadership, product strategy, and data architecture, he’s spent more than 10 years helping fast-growing companies move away from manual processes and adopt smarter, scalable systems. At GTM Engine, Robert is building what he calls the go-to-market nervous system. It tracks every interaction, uses AI to enrich CRM data, and gives teams the real-time visibility they need to stay on track. His true north is simple. To take the guesswork out of sales and help revenue teams make decisions based on facts, not gut feel.
