This post is authored by Just Drive Media's founder and CEO, Ali Winkle.

For decades, comms teams have tried to prove their value through imperfect proxies. AI gives us a chance to connect the signals we already see—and use them to shape better decisions across the business.

I have been in this industry for more than two decades, and for that entire time, communications teams have been arguing about how to measure and prove their value. Not because the value isn't there, but because what communications produces - perception change and, ideally, behavior change - is genuinely hard to put a number on. Trust builds slowly and breaks fast. Authority builds over quarters, not overnight. The best crisis is the one that never happens, and you can't put a non-event on a KPI dashboard.

So we learned to measure what we could count instead - impressions, reach, share of voice - and automated sentiment that is intermittently accurate and tells you nothing about the underlying causes. These numbers aren't useless, but they mostly describe the weather. They can tell you that an announcement landed well or that customers aren’t happy with the latest product update. They rarely explain what produced that response or how it connects to the business.

Even the quality-versus-quantity debate has no universal answer. A company trying to influence a small group of enterprise buyers may care deeply about a handful of trade publications, while a consumer business needs broad mainstream awareness. The SEO team may value the authority of a link from what the communications team would deem a tier three publication. Demand generation wants attributable traffic, but PR cares more about whether the right message reached a particular group of people, and they aren’t about to ask a reporter with whom they have a trusted relationship to add a UTM code to a story.

Each team can be right about its own goal while the organization still misses what is happening across the whole system.

The problem gets worse inside larger companies, where every function is under pressure to present its strongest results. PR brings its best coverage. Social shows its highest-performing posts. SEO reports its rankings. Demand generation presents its leads. Everyone has evidence that their work mattered, but they are often competing with each other for budgets and executive buy-in, and no one is really looking at the throughlines.

Now, on top of that, most teams are being asked to measure their "AI visibility:" how often, how accurately, and how favorably a model mentions them. That instinct is right. It's a real signal, and it's worth tracking. But it's worth seeing it for what it is: a new sense organ coming online, not the whole nervous system.

LLMs also make the existing fragmentation much harder to ignore. They do not know that media relations belongs to PR, technical documentation belongs to product and backlinks belong to SEO. They draw from all of it, along with institutional sources, reference sites, customer conversations and content published by competitors, and return one synthesized answer. The LLM does not see the org chart. It sees every mention of the company as a part of the sum.

Adding one more metric to the report won’t move the needle. The opportunity is much bigger than that. It is to finally connect the numbers — earned, social, search, owned, customer, and AI — into something that can answer more consistently and comprehensively, not just what happened, but why, and what we should do about it.

We have spent years trying to prove the value of communications by measuring its outputs, while its more important value has been largely invisible.

The advantage communications already has

Communications teams occupy an unusual position inside a company. We sit close to leadership while spending much of our time studying the outside world. We hear what the company wants people to believe and see evidence of what they believe instead. We watch customer frustrations surface, competitors claim territory, reporters latch on to certain language and narratives evolve, and make decisions about how best to respond.

The most sophisticated teams have never stopped at coverage volume or reach. They study whether priority messages are showing up in the market. They compare how a company is described by its executives, journalists, customers and employees. They examine sentiment as well as the topics driving it. They layer media analysis with social and user-driven conversation and look for the gaps between the story a company wants to tell and the one people have actually absorbed.

This is intelligence work. And when all of this information travels to the right people, it can shape much more than communications.

Yet very few companies put real emphasis on intelligence and analytics. Most invest far more in collecting data than interpreting it. The result is a familiar collection of off-the-shelf dashboards that capture part of the picture, add plenty of irrelevant noise and rarely tell anyone what to do next.

It is even rarer for a communications team to assign someone, much less a team of analysts, to look across the data and interpret what it means. Some do, and we have been supporting those teams for the past two decades. But when communications budgets come under pressure, analytics is often one of the first things to go, despite being the navigation system that tells the team where to head.

The budget data helps explain why.

Gartner’s 2026 Communications Predictions report states that communications teams spend just 2.9% of their budgets on data and analytics, compared with marketing’s 8%. Nearly half of communications leaders say they cannot clearly demonstrate their function’s impact. A third are still seen inside their own companies as a cost center rather than a driver of the business.

Our success has historically been measured like that of a production function: judged by what we churned out rather than by what we sensed, understood and changed.

AI gives us much more capacity to do that consistently and at scale. But good analysis requires a different skill set — and a different mindset — from the one most communications professionals were hired, trained or given the resources to develop.

What intelligence makes visible

For one fast-growing consumer platform, our analysis identified growing customer-service friction as a measurable driver of negative sentiment and customer departures roughly a year before the company introduced a major support improvement. Intelligence helped us identify the problem, mitigate it during the new process development, anticipate reactions to the announcement, and measure the payoff.

For another, we flagged that negative coverage around trust was up 86%, and we told them exactly why. After multiple discussions with communications, social and product teams, an education campaign was launched, and by the following quarter that negative number had dropped by 45%.

We didn’t make those product or communications decisions. The analysis made the problems visible, helped size them and gave the teams a baseline for understanding how customers would likely respond.

That is the kind of business intelligence that could be inside what many companies still treat as a coverage report.

Intelligence isn’t just about damaging narratives, either. It can also help identify budding opportunities.

For another major consumer platform, our intelligence work found a program generating more negative online conversation than positive, while voice of the customer and NPS data showed a direct contradiction. If we hadn’t been looking at more than one data point, the obvious conclusion might have been that the company needed to communicate its messages more aggressively. But the analysis revealed a different problem. Positive voices already existed; they simply weren’t participating in the most visible channels.

That distinction changed the strategy. We recommended and led a formal advocacy program that gave those supporters a larger role in the community. In its first year, the volume of positive mentions rose roughly tenfold.

In all of these cases, the topline metrics were visible, but the intelligence needed to drive real business impact came from understanding what was underneath them.

Better evidence makes for better judgment

Real intelligence gives communicators evidence they can combine with experience, context and judgment when the next tricky situation arrives. This can be especially valuable when what’s needed is more than a “trust me, I’ve seen this movie before.”

One client wanted to understand how different responses affected the length of a particular kind of coverage cycle. We analyzed a full year of the company’s crises, logging each event, its volume, the length of the media and social cycles and how the company responded.

One event, addressed immediately by a named human — a person, not a corporate statement — ran its course in about two days. A comparable event left unanswered took seven. The sample was limited, but it gave the team evidence it could apply alongside experience and judgment.

Communications measurement should improve the quality of current and future decisions rather than retrofit data to prove the success of completed work. That is a higher standard for measurement, yet also a much more valuable role for communications.

What this asks of communications leaders

Gartner expects communications spending on data and analytics to double by 2029. Where that investment will go is less clear: only 14% of communications leaders say they plan to invest in narrative intelligence platforms in the next 12 to 18 months.

I suspect many communications leaders know they need better intelligence but are still trying to understand what the capability actually requires.

Buying a new tool is the relatively easy part. A platform can collect more signals, organize them and make patterns easier to spot. What it cannot do on its own is understand the history behind those patterns, decide which ones matter, or make sure an important finding reaches someone who can act on it.

When communications detects something moving in the market, interprets what it means, recommends a response and observes what happens next, it creates a feedback loop. Run that process once and you have a useful report. Continue it over time, with the learning from each intervention feeding back into the next decision, and it begins to function as an early-warning system for the business.

Building that capability takes investment, both financially and operationally. No off-the-shelf tool can do this on its own. It requires experienced analysts, useful technology and people who understand enough about communications and data to work between the two.

More than anything, though, it requires collaboration across departments.

Among our current clients, I can think of one doing this especially well. What makes the program unusual is not simply the sophistication of the technology. The teams are willing to share information across their traditional remits, examine the results together and follow an insight even when it leads somewhere unexpected.

That is much harder than it sounds. Department leaders are generally rewarded for focusing on their own goals and demonstrating the value of their own work. An intelligence system asks them to look at how their results interact with everyone else’s.

Communications leaders are well-positioned to help make that happen. Our jobs are already about connecting disparate groups within an organization to make sure we have all of the facts before presenting the right ones to the outside world.

We do not all have to become data scientists, but we need enough fluency to question a metric, recognize a meaningful pattern and understand the difference between evidence and certainty. We have to be willing to look beyond earned media and bring forward findings that may not make our own work look successful - even more important as AI makes things easier to produce.

AI can turn almost any half-formed idea into a polished strategy or piece of content and confidently tell the founder or CEO that it is brilliant. What it cannot reliably do is recognize when that idea is wrong for this company, this market, or this moment, unless it has access to real market intelligence and someone capable of challenging its answer.

That job is ours.

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Frequently Asked Questions

1. How does a brand measure communications effectiveness?

Most teams measure it through proxies: impressions, reach, share of voice, and automated sentiment. Those count outputs. A more useful approach measures whether the messages a company prioritized are showing up in the market, how the company is described by its executives, journalists, customers and employees, and which topics are driving sentiment, then reads those findings alongside search, owned, customer and AI data. The standard to hold any of it to is whether the measurement improves the next decision rather than retrofitting proof onto work already finished.

2. Why aren't impressions, reach and share of voice enough to measure communications?

They describe what happened without explaining what caused it or how it connects to the business. They can confirm that an announcement landed well or that customers are unhappy about a product change, but they rarely identify why. Automated sentiment has a similar limit: it is intermittently accurate and says little about the underlying drivers.

3. What should communications teams measure beyond media coverage?

Whether priority messages are present in the market, how the company is described across different audiences, the topics driving sentiment rather than the sentiment score alone, and the gap between the story a company wants to tell and the one people have absorbed. The useful findings usually sit underneath the topline numbers. In one Just Drive Media engagement, a program was producing more negative online conversation than positive while voice of the customer and NPS data showed the opposite, and the explanation was that supportive customers already existed but weren't participating in the most visible channels, which pointed toward an advocacy program rather than louder messaging.

4. Should communications teams measure AI visibility?

Yes. How often, how accurately and how favorably a model mentions a company is a real signal and worth tracking. It is one input rather than a full measurement system: language models draw on media coverage, technical documentation, backlinks, reference sites, customer conversations and competitor content, then return a single synthesized answer, so the number only means something when it is read alongside earned, social, search, owned and customer signals.

5. How much do communications teams spend on measurement and analytics?

Gartner's 2026 Communications Predictions report puts communications spending on data and analytics at 2.9% of budget, compared with 8% in marketing. In the same research, nearly half of communications leaders say they cannot clearly demonstrate their function's impact, and a third are still seen internally as a cost center. Gartner expects analytics spending to double by 2029, though only 14% of leaders say they plan to invest in narrative intelligence platforms in the next 12 to 18 months.

6. What does communications measurement require beyond a platform?

A platform can collect signals, organize them and make patterns easier to spot. It cannot understand the history behind a pattern, decide which patterns matter, or make sure a finding reaches someone who can act on it, so the capability also takes experienced analysts and people fluent enough in both communications and data to work between the two. It takes cross-department cooperation as well, because PR, social, SEO and demand generation each measure against their own goals, and the connections only surface when the results are examined together.