AI’s wider access to advertising, audience, sales and operational data could make familiar campaign metrics easier to challenge—but it does not make reach, clicks or attributed conversions useless. Those figures describe delivery or observed response. They do not, by themselves, show whether advertising caused additional business results or whether those results will recur.
That is the central argument in Ben Kartzman’s October 1, 2026 article for Attain: as AI systems are used to optimize toward business outcomes, marketers will need to defend the data and causal logic behind their performance claims, not just point to a healthy dashboard. The article argues for greater scrutiny; it does not report a study proving that AI has already changed KPI practice. Read Kartzman’s article.
What changes when AI can see more of the advertising supply chain?
Digital advertising creates a trail of data across planning, audience selection, campaign activation, measurement and budget allocation. AI can help connect and interpret more of that information. Kartzman’s concern is that this makes it harder to rely on a metric simply because it is familiar or makes activity look successful: an optimizer aimed at business outcomes has little reason to preserve a KPI that does not help explain those outcomes.
This is a case for asking better questions, not for discarding every established measure. Reach and impressions can describe delivery; engagement rates can describe observed response; attributed conversions can show how a measurement system assigns credit. The decision is whether any of those measures answers the question at hand.
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Which question does a KPI actually answer?
| Measure or approach | What it can tell you | What it does not establish on its own |
|---|---|---|
| Reach and impressions | How many people or ad exposures a campaign recorded, according to its measurement system. | Whether exposure changed behavior or produced additional sales. |
| Engagement rate | How often measured users interacted with an ad or campaign element. | Whether those interactions caused a business result or were valuable enough to justify spend. |
| Attributed conversions | How a chosen attribution approach assigns credit among marketing elements for observed conversions. | How many conversions would have happened without the advertising. |
| Incrementality measurement | An estimate of the value generated beyond a counterfactual baseline—what would likely have happened without the activity being assessed. | A universally correct answer independent of the study design, data and assumptions. |
Attribution and incrementality are related, but they answer different questions. Attribution allocates credit; incrementality asks whether marketing produced an effect above a counterfactual. The IAB’s November 2025 commerce media guidance treats incrementality as a causal-impact question and describes several ways to estimate it. Its recommendations are a methodology reference for commerce media, not a rule that one method fits every advertising environment. See the IAB guidance on incremental measurement.
How should you evaluate evidence of advertising impact?
Start with the budget or strategy decision, then ask whether the evidence is strong enough for that decision. A campaign report meant to describe delivery has a different job from an estimate used to move substantial budget between channels. A useful review should examine the counterfactual, bias, data integrity and reproducibility—not just the reported result.
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- Define the outcome. Specify what counts as a business result, such as a purchase or another agreed conversion, and make the definition consistent across the data feeding the analysis.
- Ask what would have happened otherwise. Identify the counterfactual baseline and how credible it is. If a method cannot distinguish the campaign’s effect from activity that would have occurred anyway, its causal claim is limited.
- Check bias and noise. Examine how the method controls factors that could distort the comparison and whether the apparent signal is distinguishable from random variation.
- Audit the inputs. Look for weak identity signals, inconsistent taxonomy, unclear conversion definitions and low-integrity purchase data. More processing power cannot repair unreliable or mismatched inputs.
- Test whether the result can be repeated. Consider whether the design and data are documented well enough to reproduce the analysis and whether an observed effect persists under relevant conditions.
The IAB commerce media guidance discusses experiments, model-based counterfactuals, econometric models and hybrid proxies. These are options to evaluate, not a universal ranking: the relevant comparison is how credibly each approach constructs a counterfactual, controls bias and separates signal from noise for the decision being made. Review the IAB’s method guidance.
Why AI does not make weak measurement reliable
An AI system can process and optimize against the data it receives, but that does not mean its conclusions are trustworthy. Kartzman puts it this way: “AI is only as useful as the inputs, definitions and feedback loops surrounding it.” That is his argument about the conditions for useful AI, not a guarantee that a particular system will improve measurement.
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If audience identity is uncertain, conversion events are defined differently across systems, campaign taxonomy is inconsistent or purchase data is unreliable, an AI-driven recommendation may reflect those defects. The practical safeguard is to inspect the chain from source data to decision: what was collected, how terms and outcomes were defined, how the model was evaluated, and what feedback would correct a bad recommendation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What if the KPI is visibility in AI-powered discovery?
AI visibility is a distinct measurement problem. The IAB reported on August 3, 2026, that more than 20 companies sell AI visibility measurement tools and that differences in methodology can produce different answers for the same brand or publisher. That vendor count describes the tool market; it does not show how many organizations have changed their measurement practices or prove that visibility figures predict sales. Read the IAB announcement.
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The IAB names four dimensions for AI visibility: Presence, Prominence, Portrayal and Persuasion. Before using a visibility result to make budget or strategy decisions, assess its measurement design:
- Query volume: How many queries underpin the result?
- Sample size: Is there enough evidence for the comparison being made?
- Prompt coverage: Which prompt types are included, and are they relevant to the audience or question?
- Cadence: How often is the measurement repeated?
- Reproducibility: Can the same approach produce a comparable result again?
- Platform coverage: Which AI platforms are included or excluded?
These are emerging industry guidance, not settled proof that a visibility score causes or predicts commercial outcomes. Different methods may be useful for monitoring presence or portrayal, but a score should not be treated as interchangeable with incremental sales impact.
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What should marketers demand from AI and measurement providers?
Kartzman argues that some advertising software features—particularly workflow or reporting conveniences—may be reproducible internally as AI tools improve. He does not claim that all advertising software will disappear. His more durable-value test is whether a provider contributes dependable data, a clear connection to outcomes or expertise that cannot be recreated as a convenience layer.
For buyers, this suggests evaluating a provider by more than its interface or automation claims. Ask what data it contributes, how it defines outcomes, whether its measurement supports causal claims, and whether its methods can be scrutinized and repeated. The same discipline applies to AI recommendations: a machine-generated budget change is a proposal to assess, not evidence that the underlying measurement is sound.
Human expertise still matters in Kartzman’s account because someone must judge data fitness, causal inference, model bias, experimentation design, signal decay and whether a recommendation merits a budget change. AI may help with analysis; deciding whether its evidence is fit for a consequential decision remains a measurement responsibility.
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