AI marketing output is outpacing business measurement

AI can help a marketing team produce more emails and landing pages. The CEO still needs to know whether that work creates pipeline or revenue. Research commissioned by Knak, which sells a marketing production platform and benefits commercially from greater investment in marketing-production technology, found a substantial gap between engagement and commercial measurement in large organizations. For executives, AI maturity therefore requires evidence of business outcomes and the operating practices behind production.

Datalily conducted the research through Centiment on behalf of Knak among 333 marketing decision-makers in the U.S., U.K., and Canada, including Knak customers, from April 29 to May 18, 2026. Every respondent worked at a company with at least $50 million in annual revenue, used an enterprise marketing automation platform, and sent at least five marketing emails per month. The findings apply to this surveyed enterprise marketing population.

Producing more with AI does not prove business value

The key distinction for executives is between production capacity and business impact. Respondents reported using AI for tasks ranging from first drafts of email and landing-page copy or image generation to coding, brand and compliance checks, translation, and localization. These uses can change how work moves through marketing production. Greater output alone does not establish whether campaigns influenced opportunities, pipeline, or revenue.

This changes the maturity question senior leaders should ask. Counts of AI use cases, generated assets, or teams with access to AI show adoption and activity. Demonstrating value requires measurement tied to business outcomes, workflows that can handle production, and controls that determine what is ready to deploy. Knak’s commissioned research identifies gaps across these areas, although the findings reflect a study commissioned by a company with a commercial stake in marketing-production technology.

The clearest gap is between engagement metrics and business outcomes

The survey found that 69% of respondents measure email and landing-page performance using click-through rates, while 41% track revenue or pipeline influence. Knak describes this as marketers being 68% more likely to measure click-through rate than revenue or pipeline influenced. The measures answer different management questions. Click-through rate indicates engagement with an email or landing page, while revenue and pipeline influence connect marketing activity to commercial outcomes.

For a CEO or CMO evaluating returns from AI, that distinction matters. A team may produce more campaign variants while still reporting primarily on engagement. Higher production demonstrates activity and may indicate efficiency. It does not establish incremental commercial value. Credible ROI assessment requires a connection between the changed production process and business outcomes.

The distinction also affects capital allocation. Production volume, turnaround time, clicks, and similar measures can show operational movement. Pipeline and revenue measures give executives evidence closer to the financial results used to compare investments. Without that connection, deciding which AI use cases deserve further funding becomes harder.

Human judgment remains in the production workflow

In the commissioned survey, 88% of respondents said AI-generated marketing content requires moderate or substantial editing before it can be used. Generation is therefore one stage in a larger production process. The amount of editing matters when leaders assess productivity because human work remains between an AI-generated output and material that reaches a customer.

That work can include activities identified elsewhere in the survey, such as brand and compliance checks, translation, localization, and approvals. As generation expands, leaders need to examine the full path from request to deployment and measure the human intervention along it. A reduction in first-draft effort can still be valuable. Its operating value becomes clearer when the organization measures the total time and effort needed to reach approved deployment.

Human review also shapes how AI programs should be governed. When people continue to make decisions before deployment, those review steps are part of the operating model. Executives evaluating maturity should examine where judgment sits, how approvals work, and how much work occurs after generation. Those questions tie AI productivity claims to the actual production process.

Advanced adopters report different operating practices

Only 29% of respondents described their organizations as advanced AI adopters. Within that self-described group, respondents were more likely to report using AI agents to build and code emails and landing pages, conduct brand and compliance checks, and handle translation and localization. Other respondents were more likely to report using AI for first drafts of email and landing-page copy or image generation. The distinction places AI deeper in execution among the self-described advanced group.

Respondents who rated their organizations as advanced adopters were also more likely to use structured project-management tools and native approval workflows, meaning approval steps built into the systems used to manage the work. They were more likely to buy a dedicated tool when they found a capability gap rather than force an existing platform to perform the work. These findings show an association between self-described AI maturity, deeper AI use, and more structured production practices.

The survey design limits what executives can infer from this association. Respondents selected their own maturity category, so “advanced” reflects self-assessment. The findings do not demonstrate that structured project management, native approvals, dedicated tools, or deeper AI use caused better marketing or financial performance. Organizations describing themselves as further along with AI also report different ways of organizing the work.

AI maturity needs an operational scorecard

Executives can turn these findings into a scorecard that evaluates the work around AI alongside adoption. It should follow an initiative from generation through approval and deployment to business measurement. Four dimensions capture the issues raised by the survey and keep the review focused on observable operating conditions.

Consider an AI system that cuts the time needed to produce an initial landing page while the asset still passes through substantial editing and multiple approvals. The initial productivity gain may be meaningful. Executives need the end-to-end change in time and effort to understand its operating value, followed by business-outcome measurement to determine whether the changed process contributes to commercially relevant results. This keeps productivity and commercial performance separate until evidence connects them.

Performance context reinforces the need for that discipline. The survey found that only one in three respondents said they consistently meet or exceed performance targets for email and landing-page campaigns. This finding does not establish that AI maturity causes better or worse campaign performance. It does show that production volume alone is weak evidence of success when many respondents do not report consistently meeting or exceeding campaign targets.

A scorecard also gives leaders a clearer basis for investment decisions. An initiative that generates assets faster while requiring substantial human intervention and lacking a connection to pipeline has a different value case from one that reduces end-to-end production effort and supports business-outcome reporting. CEOs, CMOs, and marketing-operations leaders can use those differences to decide where further AI investment is justified and where the production process requires more work first.

Key takeaways for leaders

• Tie AI output to business value: Marketing teams may produce more assets with AI, but production volume alone does not demonstrate commercial impact. CEOs and CMOs can evaluate investment by connecting workflow changes to pipeline, revenue, and end-to-end productivity.
• Measure outcomes alongside engagement: The survey found 69% measure click-through rates, while 41% track revenue or pipeline influence. Marketing leaders can strengthen AI investment decisions by adding commercial outcome measures to operational and engagement metrics.
• Account for human work: Among respondents, 88% said AI-generated marketing content requires moderate or substantial editing. Marketing operations teams can measure editing, approvals, compliance checks, and total time to deployment when calculating productivity gains.
• Examine the practices behind AI maturity: Self-described advanced adopters reported deeper AI use, structured project management, native approvals, and greater use of dedicated tools. These findings are associations rather than proof of better financial performance, so investment decisions require outcome data.
• Build an operational AI scorecard: CEOs, CMOs, and marketing operations leaders can assess business outcomes, production processes, governance, and human intervention together. This provides a stronger basis for deciding where AI investment is producing measurable value and where workflows need further improvement.