Next Monday: Why Media Quality Matters More in the Age of AI

Artificial intelligence has made it easier to produce digital content at scale. In the hands of capable creators, that can mean faster production and more ways to develop useful, engaging work. But the same tools can also produce large volumes of low-quality content designed primarily to attract clicks and advertising.

For advertisers, that creates a practical problem. An ad can generate an impression, appear to perform efficiently, and still be shown somewhere that provides little value to the brand.

In this episode of Next Monday, host Ibarra Villaseran speaks with Tinee Cruz of DoubleVerify, who brings 13 years of experience across digital media, agencies, ecommerce, publishing, and media technology. Their discussion looks at how AI is changing media quality and why advertisers need a clearer view of where their ads appear, whether they are actually seen, and whether the people seeing them are real.

The issue becomes more important as AI increases the amount of content available online. More inventory gives advertisers more places to appear. It also gives them more places where they may not want to be.

AI Has Increased Both Opportunity and Risk

AI itself is not the problem.

Tinee points out that creative people can use AI tools to produce strong and entertaining work. The concern is what happens when the same ability to generate content at scale is used with little attention to quality.

The discussion refers to this as AI slop: mass-produced content created with little human supervision, often designed primarily to attract traffic or advertising rather than provide something useful to the audience. As more of this content enters websites and social platforms, advertisers face a larger pool of inventory of uneven quality.

That changes the media question. Getting an ad served is not enough. Marketers also need to know where it was served and whether that placement was worth paying for.

Start With Four Measures of Media Quality

Tinee explains that DoubleVerify looks at media quality through four areas: fraud, brand suitability, viewability, and geography.

Fraud asks whether the impression came from an actual person rather than invalid traffic, emulators, or bot farms. Brand suitability considers whether the environment where the ad appears is appropriate for the brand and its message. Viewability asks whether enough of the ad was actually visible to the user. Geography checks whether the ad reached the market it was intended to reach.

These are straightforward questions, but they affect how campaign results should be interpreted.

If an ad generates impressions but is barely visible, the problem may not be the creative. If traffic is coming from bots, higher numbers do not necessarily mean greater reach. If an offer intended for the Philippines is being served elsewhere, the campaign may be spending without reaching people who can act on it.

Before changing the campaign, marketers need to know what actually happened.

An Impression Does Not Always Mean an Ad Was Seen

Viewability is a good example of why surface-level metrics can be misleading.

Tinee explains that the criteria for a viewable impression can vary across platforms. In some cases, an impression may be reported even when only a small portion of the advertisement appeared on screen. The customer may never have seen the offer, message, or other important parts of the creative.

Without that information, marketers may diagnose the wrong problem. A campaign with weak sales could lead a team to revise the creative, change the offer, or adjust targeting when the more basic issue is that much of the advertising was not properly seen.

This is where measurement helps separate assumptions from evidence. It gives marketers a clearer starting point before deciding what needs to change.

Better Viewability Does Not Mean Every Placement Is Better

The study discussed in the episode shows progress in some areas. Tinee says overall viewability was up 11% in the Philippines and APAC region, which she associates with better optimization and platform selection among the clients represented in DoubleVerify’s data.

At the same time, the study found brand suitability violations up 24%.

The two figures illustrate why marketers should not judge media quality through a single metric.

More people may have an opportunity to see an advertisement, but the environment surrounding that advertisement still matters. An impression can be viewable while appearing beside content that conflicts with the brand, undermines the campaign message, or creates an association the advertiser would rather avoid.

Visibility and suitability need to be considered together.

Context Can Change How an Advertisement Is Received

Tinee uses a simple comparison: imagine a highly visible billboard positioned directly above a dumpster. The billboard itself may be easy to see, but the surrounding environment affects how people experience it.

Digital advertising works in a similar way.

A chocolate advertisement appearing beside health content may create an unintended contrast. An airline promoting a seat sale would not want that message placed next to coverage of an aviation disaster. The ad itself has not changed, but the context around it has.

Brand suitability therefore goes beyond avoiding obviously harmful content. It asks whether the surrounding environment supports, distracts from, or conflicts with what the campaign is trying to communicate.

As AI makes content production easier, paying attention to that context becomes more important.

Consumers Notice Poor Placement

Media quality is not simply an internal advertising concern.

According to the figures discussed by Tinee, around 42% to 43% of consumers globally and regionally feel negatively about a brand when its advertising appears beside low-quality or unsuitable synthetic content. In the Philippines, that figure rises to approximately 45%.

The Philippine findings add another consideration. Tinee says more than half of Filipino respondents who were negatively affected by this type of placement would tell other people about it.

A poor placement therefore does not necessarily stay between the advertiser and the person who saw it. It can move into group chats, conversations, and social media.

This gives marketers two reasons to pay attention to media quality. Poor inventory can waste advertising money, and the wrong placement can affect how customers perceive the brand.

A Lower CPM Is Not Automatically Better

Efficiency remains important, particularly when marketing budgets are under pressure. But one of the episode’s clearest warnings is against treating a lower CPM as proof that media buying has improved.

CPM, or cost per thousand impressions, tells marketers what they paid for exposure. Lowering that cost can allow a campaign to purchase more impressions with the same budget.

The problem is that cheap impressions and useful impressions are not necessarily the same thing.

Tinee argues that marketers need to consider quality alongside CPM. An advertiser may achieve a lower cost while buying inventory that is poorly viewed, unsuitable for the brand, fraudulent, or otherwise unlikely to contribute to the campaign.

This is particularly relevant when optimization systems are designed to pursue efficiency. A metric can improve while the campaign moves further away from what the business actually needs.

The objective should not simply be to buy more impressions for less. It should be to make each advertising peso work harder.

Look at the Trend, Not Just the Snapshot

Digital media conditions change quickly. Tinee notes that the content environment can shift from month to month, including changes in the volume of AI-generated material and higher-risk content.

A single campaign report can tell marketers what happened during one period. It may not show whether media quality has been steadily improving or deteriorating.

That is why Tinee recommends looking at trends rather than isolated snapshots. Tracking the same quality measures over time gives marketers a better basis for deciding where to adjust spend, tighten controls, or investigate changes in campaign performance.

This follows a basic measurement principle: data is more useful when it provides context. One number may identify a result. A pattern can help explain whether that result is becoming a larger problem.

Measurement Should Be Part of the Plan From the Beginning

Measurement is difficult to retrofit after a campaign has already started.

Tinee acknowledges why verification technology can appear to be an additional cost, particularly when it is introduced midway through an existing campaign. Her argument is that measurement should instead be considered when the media plan is being developed.

If teams invest time in the creative, targeting, platform selection, and media plan but only later discover that significant portions of their inventory were not viewable, suitable, or legitimate, much of that work has already been spent against an incomplete picture.

Setting the measurement approach at the beginning establishes the baseline that will be used throughout the campaign. It also means optimization decisions can be based on the same definitions from the start rather than introducing a new standard halfway through.

Measurement should not be something added only when performance becomes difficult to explain.

Find the Real Baseline Before Trying to Scale

Campaign performance can look different once poor-quality inventory is removed from the calculation.

Tinee describes this as establishing the real health of the campaign. Before trying to scale what appears to be working, marketers need to separate valid, useful inventory from placements that should not have been counted in the same way.

That can result in smaller numbers initially. But a cleaner baseline gives marketers something more useful to work from.

Without it, a campaign may scale both the good and the bad at the same time. More impressions, more reach, or lower costs can look positive while also increasing exposure to unsuitable or low-quality inventory.

Better measurement does not exist to make campaign reports look better. It exists to make them more accurate.

Brand Consistency Belongs in the Brief

Optimization creates another risk when teams focus too narrowly on performance metrics.

A campaign can be adjusted repeatedly to improve reach, CPM, clicks, or other indicators. But if those adjustments move the advertising into environments that no longer fit the brand, the numbers may improve at the expense of consistency.

Ibarra raises this point during the discussion: optimization can gradually take a campaign away from the brand if brand requirements are not part of the original brief.

Tinee’s recommendation is to establish brand values, campaign messaging, suitability requirements, and key performance indicators at the beginning. Optimization can then happen within those boundaries rather than treating brand considerations as something to check later.

This creates a more deliberate approach. Performance still matters, but the campaign does not need to trade away the brand to achieve it.

AI Can Create the Problem and Help Manage It

The episode does not frame AI as something marketers should avoid.

The same technology contributing to rapid content production can also support optimization and measurement. Tinee discusses AI-based tools that can help advertisers work toward performance goals while maintaining predetermined brand suitability settings.

The distinction is in how the technology is used.

AI can help creative teams produce better work. It can also generate low-quality content at an enormous scale. It can make the media environment harder to manage, while other AI systems can help marketers identify and respond to those changes.

That puts more responsibility on the marketer rather than less. Automation can process more information and make adjustments faster, but businesses still need to decide what good performance means and what boundaries should not be crossed.

What Marketers Can Do Next Monday

Tinee’s recommendation is straightforward: measure the ads and establish the real baseline.

Start with the four questions discussed throughout the episode. Is the advertisement actually being seen? Is it appearing in an environment appropriate for the brand? Is a real person seeing it? Is it being served in the geography the campaign intended to reach?

The answers may reveal that reported campaign numbers are stronger or weaker than expected. That is useful information either way. The purpose of measurement is not to confirm what the team already believes. It is to provide a clearer picture of what is actually happening.

Once that baseline is understood, marketers can make more deliberate decisions about where to reduce waste, where to adjust controls, and where there is a sound basis to scale.

The Takeaway

AI is increasing the amount of content available to advertisers, but more inventory does not automatically create more value. An impression can be inexpensive without being useful. It can be counted without being properly seen. It can reach a real customer while appearing in an environment that works against the brand.

That makes the fundamentals of digital advertising more important, not less. Marketers need to know who they are trying to reach, where their ads are appearing, whether those ads can actually be seen, and whether the reported numbers reflect genuine opportunities to communicate with customers. As Ibarra observes near the end of the discussion, these are familiar advertising fundamentals operating in an environment that AI is accelerating.

Better performance starts with an accurate view of the campaign. Measure first, establish the real baseline, and improve from what the evidence shows. AI may change the speed and scale of digital advertising, but it does not change the need to know where the advertising money is going.