How AI has made bad measurement worse

Date:
Thu, 16 Apr 2026 10:29:52 +0000

Description:
AI made untrusted measurement more dangerous. CMOs need a solution.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Tech Radar Pro Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Become a Member in Seconds Unlock instant access to exclusive member features. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. You are
now subscribed Your newsletter sign-up was successful Join the club Get full access to premium articles, exclusive features and a growing list of member rewards. Explore An account already exists for this email address, please log in. Subscribe to our newsletter For a decade or more, CMOs have been told
that better technology would finally solve measurement. First, it was attribution. Then it was omnichannel dashboards. Now its AI.

But the uncomfortable truth is that AI didnt fix measurement, it has simply made untrusted measurement more dangerous, because it can create false confidence and lock in the wrong decisions faster. Ran Avrahamy Social Links Navigation

CMO of AppsFlyer. The pressure on CMOs has never been higher. Boards expect growth that is faster and more efficient. While the need to become AI-powered has become increasingly prevalent. CEOs expect marketing to be accountable, predictive, tech-savvy, and resilient. Article continues below You may like Vanity metrics are jeopardizing AI ROI AIfirst browsers and the end of the pageview economy Rebuilding trust in AI with responsible adoption

Yet most marketing organizations are still operating on measurement systems built for an era when the web was the center of the customer journey,
channels were fewer, and signals were easier to interpret.

When the foundation is shaky, adding AI doesnt just accelerate decisions; it accelerates the wrong ones and makes them harder to unwind. Fragmented customer journeys What makes it so difficult, of course, is that modern customer journeys are fragmented across mobile apps , web, CTV, retail media, offline touchpoints, and emerging platforms that didnt exist five years ago.

Mobile has now become the gravitational center of consumer behavior, and it
is where measurement has been most stress-tested by privacy changes and
signal loss. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners
or sponsors By submitting your information you agree to the Terms &
Conditions and Privacy Policy and are aged 16 or over.

Yet many measurement systems still treat mobile as just another channel
rather than the connective tissue that links the entire journey. The result
is data that appears comprehensive on the surface but is riddled with blind spots beneath. Conversions appear disconnected. Paths seem linear but arent.

Performance signals over-index on whats easiest to measure rather than what
is driving outcomes. CMOs are seeing reports that dont line up with reality. This needs to change. Confidence is not accuracy AI systems are remarkably good at creating confidence. They produce forecasts, recommendations, and optimizations that can feel precise and authoritative on the surface. Dashboards can look smarter. Outputs can feel sophisticated. But confidence
is not accuracy. In practice, false confidence just lead to worse decisions faster. What to read next Why agentic AI pilots stall and how to fix them Responsible innovation: what 2026 should look like for businesses harnessing AI Before you roll out more AI, answer this: Who's accountable?

The problem is that when key signals are missing, AI tools will fill in the gaps with assumptions. Those assumptions get reinforced over time. Budgets shift, and strategies get locked in. Teams trust the outputs because they
look advanced, even when theyre grounded in only partial truth.

In other words, AI can give marketing leaders a false sense of certainty at the exact moment they need clarity most. Once teams operationalize those outputs, the feedback loop can become self-reinforcing, making it harder and more expensive to unwind errors.

Most conversations about AI in marketing focus on tools, models, and capabilities. But the foundational question should be is our measurement infrastructure producing data we can trust? Trust is about fidelity. But it can be difficult to see how customers move across environments.

This is why so many early AI initiatives didnt meet expectations. The technology didnt fail, but the underlying measurement infrastructure was
never designed for autonomous or semi-autonomous decision-making.

Measurement is not a supporting metric. Its the foundational infrastructure that determines whether AI becomes an accelerator of a liability. Mobile at the core One of the most persistent misconceptions in marketing measurement
is that omnichannel means treating all channels equally. In practice, this means understanding how they connect and where behavior occurs. For most consumers that center of gravity is mobile.

Mobile is where identity is strongest, engagement is deepest, and intent is most clearly expressed, even when the final transaction happens elsewhere.
Its where discovery, comparison, loyalty, and repeat behavior increasingly live.

Its also where marketers learned to measure with fewer deterministic identifiers, tighter consent expectations, and constant platform change.

In too many stacks, mobile measurement is still based upon web-era
assumptions and reporting conventions adapted to apps , rather than mobile-grade standards built for privacy constraints.

Yet without a reliable anchor point that holds up under privacy constraints, omnichannel measurement becomes a patchwork of proxies and assumptions.

When mobile is treated as an afterthought, teams end up optimizing to what their platforms can most easily observe, not what customers do. A fork in the road CMOs need to start asking harder questions about measurement: Where are our biggest blind spots across channels and devices? Which decisions rely on modelled assumptions rather than observed behavior? What data do we treat as
a source of truth? And are our systems designed to support automation ?

From there, the focus should shift to strengthening the measurement infrastructure on which AI sits. AI works best when it is atop systems built for todays complexity rather than retrofitted onto frameworks designed for yesterdays simplicity.

That means designing measurements that connect journeys end-to-end, not channel by channel.

In a world where AI increasingly shapes decisions, measurement becomes the control layer to know what to trust, what to question, and when to intervene. As decision-making becomes more autonomous, the cost of getting that wrong only rises.

CMOs face a fork in the road. Treat measurement as the foundation for AI-driven marketing, anchored in mobile-grade standards that hold up under privacy pressure. Or keep stitching together channel reports and feed AI a partial view of reality. I know which I would choose. We've featured the best customer experience (CX) tool. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and
brightest minds in the technology industry today. The views expressed here
are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro



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