Documented data governance boosts AI returns, survey finds

Convertr has published research linking documented data governance with stronger returns from AI projects. The survey covered 176 Marketing and Revenue Operations leaders at US companies.

Teams with documented data governance were far more likely to meet or exceed expected returns from AI deployments than those with major gaps or no governance in place. The study found that 84% of teams with documented processes hit or beat their expected AI return, compared with 27% of those without them.

The findings point to a wider problem in how companies manage lead and contact data as AI is used more heavily in marketing and sales processes. Poor controls over incoming records can affect scoring, routing and outreach before staff have a chance to intervene.

Another result highlights tension between confidence in customer records and the quality of decisions based on them. While 71% of respondents said they trust their CRM data, 76% said their teams had made a significant marketing or pipeline decision based on data that later proved wrong, or was suspected to be wrong.

The research also found that weak or missing standards can disrupt coordination across commercial teams. The survey found that 95% of teams said unenforced or absent data standards create misalignment between sales and marketing, while 70% described the effect as moderate to severe.

Emma Bowkett, Chief Executive Officer at Convertr, said the issue becomes more urgent when AI systems act on data as soon as it enters company systems.

"AI acts on lead and contact data within seconds of it arriving, so a gap in governance can shape pipeline and revenue decisions before anyone has a chance to catch it," Bowkett said.

She added: "The teams seeing a return on AI have made data governance a leadership decision. Sales and marketing agree the standard together, and every record that feeds the pipeline is held to it."

AI in the loop

The survey suggests some companies are introducing AI into sales and marketing workflows before agreeing basic rules for the data those systems use. It found that 28% of teams using AI for lead scoring have never agreed on the required fields for an inbound record.

That gap matters because automated systems can process flawed records faster than teams can correct them manually. Data from events or third-party providers can enter internal systems and move through several automated steps before a person reviews it.

In practice, that means one incomplete or inaccurate record can influence several downstream actions. If the same record is used in scoring, routing and outreach, any initial error can carry through each stage.

Three factors

Convertr identified three common features among teams that reported returns from AI use. The first was an agreed standard between sales and marketing covering the fields an inbound record must contain, with that standard documented and approved by both sides.

The second was a control at the point where data first arrives, so each record is checked before it reaches the CRM. The third was a named owner with clear responsibility for data accuracy, rather than uncertainty between marketing, Revenue Operations and IT when a record fails a test.

The argument reflects a broader shift in how businesses assess AI investments. While much attention has focused on model selection and use cases, the findings suggest operational discipline around data may be a more basic factor in whether companies realise measurable returns.

For Marketing and Revenue Operations teams, the findings also suggest data governance is no longer a back-office issue. If AI systems are used to inform pipeline decisions, the quality of lead and contact data becomes directly tied to budget allocation, sales alignment and revenue planning.

The survey was conducted between May and August and focused on US-based Marketing and Revenue Operations leaders. Its results indicate that confidence in AI outcomes may depend as much on controls over incoming data as on the tools companies choose to deploy.