Our intent data shows that a growing number of organisations are either exploring or actively deploying AI and large language models (LLMs) across their sales cycle.
From lead scoring and prospect prioritisation to message generation and workflow automation, AI is rapidly becoming embedded in how go-to-market teams operate.
Used well, the upside is clear. AI has the potential to reduce manual effort, accelerate execution, and improve how teams identify and engage high-quality prospects - using signals like intent, timing, and likelihood to buy. That’s the outcome every sales leader is working towards.
But there’s a gap emerging between that vision and reality, and it comes down to something far less talked about. The quality of the data powering these systems.
In our recent research at Cognism, one finding stood out. 75% of revenue leaders say data quality is their biggest challenge.
That’s significant, particularly at a time when more teams are building AI into their go-to-market strategies. While AI promises better decisions and faster execution, it is entirely dependent on the data it’s trained on and fed.
If that data is incomplete, outdated, or fragmented, the outputs won’t just be imperfect; they’ll be misleading. And instead of improving performance, AI risks scaling the very problems teams are trying to solve.
This is what happens when you layer AI onto years of accumulated data issues:
These challenges have existed for years, but what’s changed is the impact. Because when AI is introduced into this environment, it operationalises these issues.
At that point, data quality becomes a direct barrier to growth.
One of the biggest - and most underestimated - issues is how quickly go-to-market data becomes outdated. Nowhere is this clearer than at the C-suite level.
Our research shows that across the UK, France, and Germany, more than half of C-suite data is inaccurate within two years due to leadership churn.
The rate of decay is even more pronounced in certain roles:
The US market is marginally more stable, but the pattern is the same: half of the records become inaccurate within 25 to 27 months. What this means in practice is simple:
And if AI models are relying on that data, they’re effectively being painted at opportunities that no longer exist.
At the same time, the window to engage buyers is getting shorter.
Our data shows that 78% of decision-makers allocate significant budget within their first 90 days in role. While data is decaying faster, buying decisions are made earlier. That creates a very narrow window for go-to-market teams to:
If that timing is missed, the opportunity is often gone.
The organisations seeing real impact from AI are investing in their data first. They understand that being effective in this environment means being able to:
In other words, they’re becoming fluent in data. That fluency doesn’t come from a single platform. It comes from building the right foundations:
If your data is strong, AI can help you move faster, target better, and execute more efficiently. The conversation needs to shift. From: “How do we adopt AI?” To: “Is our data ready for it?”
As AI becomes more embedded across revenue teams, access to tools won’t be the differentiator. Every organisation will have them. The advantage will come from the ability to trust - and act on - the data those tools rely on.
Because ultimately, AI doesn’t create competitive advantage on its own. It amplifies the quality of what you already have. And in go-to-market, that starts with data.