AI isn't enough: What companies need to fix before their sales results will change

AI isn't enough: What companies need to fix before their sales results will change

Many companies see artificial intelligence (AI) as an off-the-shelf solution to a variety of business problems. AI will personalize outreach. It will score leads. It will write the emails, predict churn, prioritize accounts, and tell salespeople exactly what to say and when to say it.

This is happening at some companies. But many businesses are investing heavily in AI-powered tools, deploying them across their go-to-market (GTM) teams, and still not seeing the results they expected. Revenue projections fail to improve, conversion rates remain stagnant, and frontline sellers become increasingly frustrated.

The fundamental problem most businesses are struggling with is poor data.

ZoomInfo examines why AI investments often fail to improve sales performance and what data quality issues are typically to blame.

The Foundation Nobody Wants to Talk About

AI tools are powerful but cannot conjure insights from nothing. They analyze and act on information that already exists in CRM records, prospect lists, contact databases, and account histories. If that underlying information is inaccurate, incomplete, or out of date, AI serves only as an accelerant to negative outcomes.

The uncomfortable reality for most organizations is that their data is a mess. People leave to pursue new opportunities, companies are acquired, and priorities change. By the time a sales rep reaches out to a "warm" contact in their CRM, there's a meaningful chance that person no longer works there, no longer holds the role a pitch was tailored for, or no longer has the authority to make a decision.

AI doesn't know any of that unless the data explicitly says so.

What ‘Broken Data' Actually Looks Like in Practice

Bad data wastes time and undermines confidence, but it also has a very real financial impact.

Survey data from ZoomInfo suggests that 95% of GTM leaders experienced negative performance stemming from poor-quality data in 2025. One in 4 GTM leaders aren't confident their GTM data is updated in real time to reflect key changes, with 2 in 5 enterprise GTM leaders sharing similar concerns about the reliability of the real-time data at their disposal.

Only half of GTM leaders are satisfied with their customer and prospect data, with integration across systems, intent and buyer signals, data completeness, and their ability to deduplicate redundant records standing out as critical vulnerabilities.

The Confidence Problem

Another dimension of AI's data problem that is rarely discussed is what bad data does to the humans using these tools.

Frontline salespeople who've been burned by bad contact information stop trusting the system. They start manually verifying everything, defeating much of the efficiency AI was supposed to create. Worse, they stop using the tools altogether and revert to the workflows they were comfortable with before.

Managers who have watched AI-generated forecasts miss the mark a few times in a row lose confidence in the models. They start overriding recommendations based on gut feel, which reintroduces the kind of inconsistency AI was meant to eliminate.

This erosion of trust is slow, but it's toxic to technology investments. It almost always traces back to AI promising results it cannot deliver because the data powering it isn't reliable enough to deliver them.

What Actually Needs to Change?

Most companies understand that their data quality isn't perfect, but very few take the time to quantify it. How many CRM contacts have verified, current email addresses? What percentage of accounts have accurate employee counts and revenue figures? How often is contact data refreshed? These are vital diagnostic questions, and the answers will reveal more about sales performance than almost any other metric.

One of the most common mistakes organizations make is treating data quality as a one-time initiative owned primarily by IT. Data decays continuously, so the solution has to be continuous, too. That means building processes or partnering with providers that keep your information current on an ongoing basis, not just when someone notices a problem.

Many organizations have data quality issues, not because good data doesn't exist somewhere in their technology stack, but because it isn't accessible across disparate systems; data trapped in one system that isn't accessible to another is storage, not intelligence. Getting the connective tissue right matters as much as the quality of any individual data source.

Data quality is a cultural challenge as much as it is an operational one. Organizations that take this seriously treat accurate, current data as a business priority, one that has visible champions at the leadership level and stakeholders across every major department of the business.

The Real Competitive Advantage

AI is a genuine force multiplier in sales. The companies that learn to use it well will have real advantages over those that don't. However, that multiplier only works if there's something worth multiplying. If the underlying foundation - the data, the processes, the systems of record - is unreliable, AI cannot fix the problem; it just scales it.

The companies that are seeing their sales results change are the ones doing the less exciting, less headline-worthy work of making sure that AI tools have a reliable foundation to build upon, and understand that data quality is a problem of discipline, not technology.

Solving it is entirely within reach, but only for the organizations willing to look at it honestly.

This story was produced by ZoomInfo and reviewed and distributed by Stacker.

Copyright 2026 Stacker Media, LLC

This story was originally published April 16, 2026 at 9:30 AM.