Businesses measure their corporate communications with systems that are built for speed and that deliver results that look good in dashboards. But the metrics they use ignore the most critical information because they say nothing about if audiences trust the brand, engage with the brand or buy from the brand. On its own, that gap is bad and leads to uninformed decision making. But AI is silently widening the gap between the metrics that get reported and the metrics that actually report if a business’s communications are effective.

Communications Measurement

When Metrics Miss the Point

Communications measurement systems are built for speed and produce results that look good in dashboards. AI is silently widening the gap between the metrics that get reported and the metrics that reveal whether communications actually work.

The Reporting Gap

What Gets Reported

Budgets, raises, bonuses, promotions and personal reputation all depend on metrics that look good and are simple to justify. Communications professionals are incentivized to claim these metrics measure success because compensation and reputation depend on that claim being accepted.

  • ▶Clicks and impressions
  • ▶Citation shares in AI results
  • ▶Webinar registrations
  • ▶Algorithmic reach and surface rate

What Cannot Be Captured

Short-term metrics cannot capture trust. Trust grows slowly and erodes quickly. Unlike clicks, trust is nearly impossible to directly measure – proxies look at trends over years, not quarters.

  • ▶Trust in the brand
  • ▶Purchase decisions
  • ▶Cultural resonance and sustained brand attention
  • ▶Audience movement toward a decision

The AI Distribution Layer

AI Amplifies the Problem

AI does a lot of work in communications, but neither faster nor better than people. Instead it amplifies existing problems. AI platforms sit between communications professionals and the people consuming the content they create, adding behaviors businesses can sometimes attempt to influence but can never control.

AI Does the Consumer’s Work Too

AI also does the work of consumers by searching, finding and evaluating content before returning answers to people. Neither citation share nor algorithmic reach measures whether the right person saw or understood a message, or moved closer to making a decision.

Key Takeaways

Wrong Questions, Clear Answers

The measurement systems currently in use produce clear answers to the wrong questions – and that is the incentive. The systems reward what can be reported, not what needs to be known.

Lost Audience Visibility

Organizations that get better at performing well in dashboards often lose visibility into whether audiences are actually responding in the process.

Confused Leadership

Executive leadership routinely treats platform metrics as audience outcomes. That confusion rewards the wrong performance signals and leaves organizations blind to whether their communications are actually working.

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Why Communications Metrics Miss What Matters

Budgets, raises, bonuses, promotions and personal reputation all depend on these easy to report metrics that look good and that are simple to justify. People working in communications are incentivized to claim that these metrics effectively measure their success because their personal compensation and reputation depend on their lie being perceived as true. Short term metrics cannot capture trust, and it grows slowly and erodes quickly. Unlike clicks, trust is almost impossible to directly measure, and the proxies used to measure trust look at trends over years, not quarters.

How AI Widens the Communications Measurement Gap

AI is doing a lot of work in communications, but it does it neither faster nor better than people. Instead, AI amplifies the existing problems because the platforms sit between communications professionals and the people consuming the content those professionals create. AI generates ideas, writes drafts, produce videos and even sometimes make final content decisions. It also does the work of consumers by searching, finding and evaluating content before returning answers to people. These new AI layers add behaviors that businesses can sometimes attempt to influence but can never control.

Platform Metrics vs. Audience Outcomes in Communications

There are two main bucket of metrics in communications. The first measures what a platform did, and the second measures what an audience did. These are not the same thing and it is dangerous to confuse the two. One common metric is citation share, or how often a particular AI chose a source; another common metric is algorithmic reach, or how often a platform surfaced content through traditional search engine rankings and organic visibility. Both citation share and algorithmic reach are important metrics that tell part of a story; but neither measures if the right person saw or understood a message. Citation share and algorithmic reach don’t report if the user who saw a message moved closer or further away from making a decision.

Key Takeaways on Measuring Communications Effectiveness

  • The measurements systems currently being used systems produce clear answers to the wrong questions, and that is the incentive.
  • Organizations that get better at performing well in dashboards often lose visibility into whether audiences are actually responding
  • Executive leadership routinely treats platform metrics as audience outcomes