AI Is Moving Faster Than Sales Operations Can Absorb — And It’s Becoming a Problem

Everyone in sales right now is being told the same story: “AI will fix your pipeline hygiene, forecast accuracy, lead scoring, rep onboarding, account research, territory design, call notes, proposals, QBR decks, and also make espresso.”

Here’s the quieter truth: sales operations teams are the ones being asked to make that happen… and they’re underwater.

Even though AI adoption in sales has jumped fast, HubSpot reports it rose from 24% in 2023 to 43% in 2024. That is a big leap, but adoption does not automatically mean the organization is ready to operationalize it. Most companies still say they’re struggling to get consistent, scalable value from AI.

BCG reports that 74% of companies have yet to show tangible value from AI. In other words, many organizations are experimenting, but far fewer are consistently capturing measurable impact.

So if you’re in Sales Ops / RevOps and it feels like everyone is telling you, “just turn AI on,” you’re not crazy. You’re being handed responsibility for something the company hasn’t actually set up to succeed.

Let’s talk about the real struggles.

The data still isn’t clean enough to trust the AI

Most companies are still running on a tech stack with dozens (sometimes hundreds) of partially connected systems. According to Salesforce MuleSoft’s 2025 Connectivity Benchmark, the average enterprise manages 897 applications, yet only 29% are integrated. That is a recipe for data silos and fragmented workflows.

That creates two problems:

1. AI can’t see the full picture. Forecasting, lead routing, next-best-action — these models are only as good as the customer, pipeline, and activity data you feed them.
2. Reps don’t believe the output. Only about 35% of sales professionals fully trust the accuracy of their company’s own data (Salesforce’s State of Sales 2024 Report).

Sales Ops is stuck in the middle. If the AI bot suggests “Call Acme, they’re ready to buy,” and Acme actually churned last quarter, but nobody updated status in CRM… guess who gets blamed? Not the model. You.

If leadership wants AI-driven forecasting or pipeline risk scoring, the first project isn’t “turn on AI.” The first project is “fix the data model across CRM, finance, CS, marketing automation, and deal desk so AI isn’t generating outputs from unreliable inputs.”

Everyone wants AI — but without the process change or foundation to support it

When you introduce AI assistants that write call summaries, draft next steps, push notes into CRM, etc., you’re not just “adding a tool.” You’re changing how reps record activity, how pipeline gets inspected, how managers coach, and even who’s allowed to talk to the customer.

This is organizational change management — and that’s the step most companies under-resource. Leaders say, “roll out AI to the field.” Reps say, “why should I trust this thing?” Managers say, “does this change how I’m measured?” IT says, “are we allowed to send this to the cloud?” Sales Ops is expected to answer all five… this quarter.

In practice, the biggest obstacle to AI adoption is not the technology itself. It is the work required to redesign processes, incentives, and trust models around it.

Executives are reading headlines about “AI agents closing deals” and “autonomous forecasting,” but many orgs haven’t even standardized opportunity stages across regions.

The trap for Sales Ops: CFO says “show ROI this quarter,” CRO says “get it live next month,” and you’re still defining what a successful AI pilot even looks like. You’re being told to prove ROI before you’re allowed the time to implement the process changes that create ROI.

AI still requires human oversight — and teams don’t have the bandwidth

GenAI is powerful, but it’s not flawless. Sales Ops ends up reviewing call summaries for accuracy, ensuring that AI-generated plans don’t leak sensitive data, and rewriting AI outreach to sound human. You didn’t remove work — you shifted it.

Sales Ops used to focus on reporting, quota, pipeline, and GTM enablement. Now it’s data governance, model QA, and vendor evaluation — with the same team size. According to Salesforce’s State of Sales 2024 Report, a third (33%) of sales operations professionals using AI say their teams lack resources or headcount to support the new technology. Another 33% cite insufficient employee training as an adoption hurdle. This highlights a significant resource gap in AI adoption. There’s also a talent gap: few people understand both sales process and AI deeply enough to operationalize it.

Risk, compliance, and customer trust

Half the employee base in many orgs worry about AI accuracy, security, and misuse of data. Buyers are noticing when messaging feels automated or invasive. Sales Ops must now own governance: data storage, transcript policy, NDA boundaries, and quote generation — all within AI use.

The expectations are too high, too fast

Leadership sees headlines and vendor claims about faster cycles and better conversion. The pressure is real, but the results usually show up only after data, process, and governance are in place.

Where do we go from here

• Stop selling “AI.” Sell process improvement assisted by AI.
• Pick 1-2 friction points (like call notes or deal scoring) and win there first
• Make data quality a C-suite issue – it’s revenue infrastructure
• Build a plain-English “AI usage policy for Sales”
• Be honest about capacity – AI rollout takes resources

Final Thought

AI in sales is real. Teams using it well are seeing shorter deal cycles, faster prep, and more predictable pipeline.

But AI is not replacing Sales Ops — it’s making Sales Ops the most critical function in the GTM engine. If you control the data, process, rollout, policy, and measurement, you don’t just support sales — you become the operating system for how revenue runs.

Sources & Background Research

• Salesforce State of Sales 2024 Report
• McKinsey Global AI Adoption Survey, 2024
• Salesforce MuleSoft 2025 Connectivity Benchmark
• Gartner Future of Sales 2025 Research
• Microsoft Work Trend Index on AI Adoption, 2024
• BCG – AI Adoption in 2024
• HubSpot State of AI in Sales Report, 2024
• Deloitte Tech Trends 2026

SalesGlobe is a leading sales effectiveness and data-driven creative problem-solving firm. We specialize in helping Global 1000 companies solve their toughest growth challenges and helping them think in new ways to develop more effective solutions in the areas of sales strategy, sales organization, sales process, sales compensation, and quotas. We wrote the books on sales innovation with The Innovative Sale, What Your CEO Needs to Know About Sales Compensation, and Quotas! Design Thinking to Solve Your Biggest Sales Challenge.