Jennifer Neeley

Strategic Marketing Advisor for Trust-Sensitive Growth

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Jennifer Neeley

The Ring Light Wasn’t the Problem. The Strategy Was.

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A debate about influencer behavior at the US Open debate exposed a broader influencer-marketing problem: access can generate attention while making the activation itself more visible than the event.

On September 4, Yahoo reporter Kelsey Weekman quoted me about influencer fatigue at the US Open. The line that made the article was that fatigue begins “when creator access starts to feel more visible than the event itself.”

I stand by it. But the easiest version of this story is also the least useful: influencers showed up, behaved badly and ruined something people love.

That is satisfying. It is also much too simple. This is not a complaint about creators. It is a question about the strategy built around them.

The backlash is not really about influencers

The US Open is a natural creator environment. Fashion, celebrity, luxury hospitality, sports fandom and spectacle already overlap there. Inviting people who can translate the experience for audiences who may not follow tennis is not inherently cynical. Done well, it is smart audience development.

The expansion had a stated purpose. The USTA told Front Office Sports that it approved close to 100 creator credentials in 2026, up from 54 in 2025, to broaden coverage of the tournament. It also said the first credentialed cohort generated more than 5.5 million social engagements. Players quoted by the Associated Press recognized the value of reaching new audiences even as they objected to behavior that interrupted play.

Reach was measurable. Meaning was the harder question.

The AP reported that a chair umpire stopped play while people in a suite recorded a video and that a bright ring light was used during another incident. Yet the accountability was more complicated than much of the backlash suggested. Forbes reported that the guests involved in the widely circulated suite incident were not part of the official creator-credential program.

That distinction does not weaken the strategic lesson. It strengthens it. This was not simply an influencer problem. It was an experience-design, access and governance problem.

Promotional sameness is a strategy problem

When every invited person films the same entrance, holds the same drink, photographs the same sandwich and poses against the same recognizable backdrop, the result may produce enormous volume without adding much meaning.

That is promotional sameness: different people producing versions of the same advertisement.

The creators may be doing exactly what they were invited to do. If the brief rewards recognizable repetition, access display and immediate posting, it is strange to blame them when the output feels repetitive, transactional and detached from the event.

The strategic question is not whether creators belong at the US Open. It is whether the program gives them a reason to notice, understand and contribute something an ordinary sponsorship placement cannot.

Access changes the meaning of content

Scarcity changes the calculation. When fans see the best seats and hospitality spaces as expensive or out of reach, creator access is not judged as content alone. It becomes a story about fairness, status and who the institution appears to value.

A courtside photo is no longer only a courtside photo. It becomes evidence of who got in, who did not and whether the person with access seems to understand what that access was for.

Brands often treat access as a distribution asset. Audiences experience it as a social signal.

That gap is where resentment grows.

We often apply a different test to celebrities

Two years earlier, Travis Kelce arrived at the US Open in head-to-toe Gucci, generated headlines about his clothes and later acknowledged watching football on his phone during the match. He was certainly part of the spectacle. Yet celebrity performance is often treated as charming, while creator performance is treated as proof of cultural decline.

Why? Is celebrity distraction more legitimate because the fame came first? Or did the same behavior look charming because it fit the celebrity we already thought we knew?

I do not raise this to criticize Kelce. I raise it because “influencers ruin everything” often disguises a less comfortable conversation about status. We tolerate performance from people whose access already feels culturally authorized. We resent it from people whose access feels newly manufactured.

What a better program would do

A stronger creator or hospitality strategy begins before anyone enters the suite.

Define the guest’s role. Are they there to explain the sport, interpret its culture, reach a new audience, document hospitality or merely produce recognizable proof that the event is desirable? Those are different assignments.

Brief for difference. Give people access to angles, experts and stories that allow them to make something other than the same post everyone else is making.

Make the norms explicit. If silence during play matters, explain why, reinforce it in the environment and make hosts responsible for the conduct of invited guests. Etiquette cannot remain an assumption when audience expansion is the strategy.

Measure beyond reach. Did new audiences understand more about tennis? Did the activation improve affinity? Did it attract people the sport wants to retain? Did fans feel invited into the experience, or reminded that the best parts were reserved for someone else?

Millions of impressions cannot answer those questions by themselves.

Reach can grow while trust shrinks

The US Open did not create influencer fatigue. It gave the problem a stadium.

The answer is not fewer creators by default. It is better judgment about access, context, difference and the audience on the other side of the post.

Attention can expand while trust contracts. A campaign can travel farther while making the institution feel smaller. A marketing program can hit its numbers while quietly teaching people to resent the people carrying the message.

The ring light was the easiest thing to blame. The strategy deserves the harder look.

Before you add more attention

If your organization is planning a creator program, sponsorship, high-visibility launch or public repositioning, start with the Strategic Advisory Diagnostic.

If the issue is larger than a campaign and touches positioning, reputation or stakeholder trust, explore Strategic Advisory.

Sources and further reading

In an AI-Saturated Summer, Judgment Became the Most Important Marketing Skill

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This summer, I taught digital marketing in a classroom where AI was already everywhere.

Not theoretically: Students were using it. Marketers were using it. Organizations were trying to figure out what it meant for search, content, social media, customer behavior and their own jobs.

Before the course began, I asked students what they wanted to understand.

The questions came quickly:

  • How should marketers use AI?
  • How is it changing search?
  • How do you know whether the information it produces is accurate?
  • How much content is too much content?
  • What makes people trust a brand now?
  • Which platforms still matter?
  • How do you know whether something actually worked?

The questions looked like they were about technology.

By the end of the term, I thought many of them were about something else: Judgment.

AI made producing an answer easier

That is the obvious change.

A marketer can now generate an audience profile, content calendar, competitive analysis, list of keywords, campaign concept or draft strategy before lunch. Tasks that once required hours of searching and synthesis can happen in minutes.

That is not trivial. AI’s extraordinary advantage is compression.

It compresses the distance between a question and something that looks like an answer.

But the classroom made the other side of that equation impossible to ignore.

The ability to produce something quickly does not tell you whether the thing is right.

  • Or useful.
  • Or based on credible evidence.
  • Or appropriate for this customer.
  • Or strategically connected to the business problem.
  • Or even solving the problem you thought you had.

Speed without verification just accelerates mistakes.

The work changed when the questions changed

One of the most interesting parts of teaching marketing is watching what happens when students stop asking: What should we do?

And start asking: Why would we do it?

A weaker strategy starts with tactics.

  • We need TikTok.
  • We need SEO.
  • We should use influencers.
  • We need an AI strategy.
  • We should run paid search.

A stronger strategy asks:

  • Who exactly are we trying to reach?
  • What are they trying to accomplish?
  • What do they already believe?
  • Where are they getting information?
  • What could make them hesitate?
  • What evidence tells us this is the actual problem?
  • What would have to change for the strategy to work?
  • What would we measure to find out whether we were right?

Those questions are considerably less glamorous than prompting an AI tool. They are also where most of the value lives.

A buyer persona can now be wrong faster

Buyer personas provided a particularly useful example.

Give an AI tool a company name and some context and it can produce an impressive-looking persona almost instantly.

  • Name.
  • Age.
  • Occupation.
  • Goals.
  • Frustrations.
  • Media habits.
  • Maybe even a stock image and a quote.

It looks “complete.” But that is precisely the problem.

A polished artifact can create the impression that a question has been answered when the underlying assumptions have never been tested.

The persona may be plausible, but plausible is not the same as true.

The better student work moved beyond demographic decoration to examining the decision itself.

  • What is this person actually trying to accomplish?
  • What creates uncertainty?
  • What information do they need?
  • What do they trust?
  • What alternatives are they considering?
  • What prevents action?
  • What changes their mind?

AI did not make the buyer persona obsolete, it made unverified personas easier to manufacture.

That raises the standard for the marketer.

The same thing happened with customer journeys

Funnels look wonderfully orderly in a presentation.

Awareness ➡️ Consideration ➡️ Conversion

The real people inside them are considerably less cooperative.

  • They search.
  • They leave.
  • They ask someone.
  • They watch something.
  • They forget.
  • They come back.
  • They read reviews.
  • They ask AI.
  • They compare.
  • They abandon the process.
  • They return three weeks later from another device.

My students repeatedly confronted the difference between the journey they wanted customers to follow and the one customers were likely to follow.

That distinction matters because marketing plans become much stronger when students stop designing around a diagram and start designing around behavior.

The marketer’s job is not to force people neatly through a funnel.

It is to understand enough about the decision to remove unnecessary friction along the way.

Trust kept showing up where I wasn’t explicitly teaching “trust”

This happened throughout the course.

  • We would be discussing search and end up talking about credibility.
  • We would discuss social media and end up talking about reputation.
  • We would discuss customer journeys and encounter uncertainty.
  • We would discuss AI and end up asking whether the answer deserved to be believed.

By the time we reached privacy, security, ethics and reputation, the pattern was difficult to miss.

Trust was not another topic in the curriculum, it was running through nearly all of them. That led to a question I kept returning to: What would make an audience hesitate to trust an organization online—even when the marketing appears polished?

It is an uncomfortable question, but also incredibly useful because so much of marketing today looks excellent—but still creates doubt.

  • A beautifully designed healthcare site can make claims that are technically accurate but difficult for a patient to interpret.
  • A polished social presence can generate attention without providing evidence.
  • An AI-generated explanation can sound authoritative while flattening important distinctions.

A company can be visible everywhere and credible nowhere.

Trust is not the decorative layer we add after the marketing strategy, it fundamentally changes whether the strategy works.

AI clarity and human clarity are mostly the same discipline

One of the stranger developments this year is the growing industry around making organizations understandable to AI.

There is real work to do there.

Entities need to be clear. Expertise needs to be attributable. Information needs structure. Relationships between people, organizations, topics and evidence need to be legible across systems.

But teaching this summer reinforced something more fundamental for me. Many organizations do not primarily have an AI-clarity problem. They just have a clarity problem.

  • Who are you?
  • Who is this for?
  • What problem do you solve?
  • What makes your expertise different?
  • What evidence supports your claims?
  • Where can someone verify them?
  • What should the person do next?

Those are questions a human needs answered, and they are increasingly questions a machine needs answered, too. We should absolutely learn how emerging discovery systems work. But there is a danger in applying sophisticated optimization to fundamentally unclear positioning. Sometimes the best thing you can do for AI visibility is make the organization easier for everyone to understand.

My students also reminded me that tools are seductive

Marketing has always had this problem.

Every generation gets a new tool that appears capable of solving the discipline.

  • Email.
  • Search.
  • Social.
  • Marketing automation.
  • Influencer marketing.
  • Data.
  • Now AI.

The tool changes, but he temptation remains: we mistake capability for strategy. One of the harder things to teach—and one of the harder things to practice professionally—is restraint. A marketer who understands a new tool can tell you what it can do. A strategist also needs to know when it is irrelevant. That distinction becomes much more valuable when the cost of creating something approaches zero.

We can now make more.

  • More copy.
  • More images.
  • More analysis.
  • More campaigns.
  • More variations.
  • More content than audiences could reasonably consume.

The strategic question is no longer merely whether we can make something, it is whether creating it changes anything that matters.

The scarce skill may be moving

For a long time, access to information was scarce, then information became abundant. AI is making synthesis abundant, too and that changes where human value sits.

I do not think the answer is that humans must simply become more creative than machines. That is too easy.

The more important advantage may be discernment.

  • Knowing which question matters.
  • Knowing when the data is weak.
  • Knowing what assumption an answer depends on.
  • Knowing when a customer description is generic.
  • Knowing what evidence would change your conclusion.
  • Knowing when a tactic is fashionable but strategically irrelevant.
  • Knowing when a beautifully produced answer should make you more skeptical, not less.

AI can help enormously with the work, but it does not relieve us of responsibility for the judgment.

Teaching is becoming a form of market intelligence

This is one reason I continue to value the classroom: teaching forces precision. Students notice when an explanation does not hold together. They ask questions practitioners sometimes stop asking because an industry convention has become familiar.

And each class provides a live view into what people are struggling to understand as the market changes.

This summer, AI generated many of the questions, but underneath them I kept hearing older, harder ones:

  • How do I know what matters?
  • Who should I believe?
  • How do I tell whether this is working?
  • What is the customer actually doing?
  • What should I do with all this information?

Technology changes the surface of those questions, but it does not make them less important.

What I am taking into the fall

I started the summer thinking I would spend a considerable amount of time helping students understand new tools.

I did.

But the more important work became helping them interrogate the output.

  • Ask a better question.
  • Examine the evidence.
  • Identify the assumption.
  • Understand the audience.
  • Connect a tactic to an actual decision.
  • Decide what not to do.

Those are not anti-AI skills, they are the skills that make AI useful. And they are becoming more valuable precisely because producing a plausible answer has become so easy.

The students did not convince me that marketers need to become less technical. They convinced me of something harder: we need to become better judges.

Because in an environment where almost anyone can generate an answer, the competitive advantage may belong to the people who know which answers deserve to survive.

Gamification Was Never Just Points. But Did It Move Us Closer to Social Media Addiction?

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A 2010 gamification framework helps explain how visible points, badges and progress mechanics evolved into today’s harder debate over platform addiction, device dependence, youth safety and trust.

Who this is forMarketing, product, healthcare, education and policy leaders responsible for engagement systems that shape consequential behavior.
When it mattersWhen retention, participation or time spent is being treated as proof that a platform, device or experience is creating value.

In 2010, gamification sounded like a clever digital trick.

Add points. Add badges. Add levels. Add progress. Make the boring thing feel a little more like a game.

That was never the whole story.

I am not writing this as someone who hated gamification. I am writing this because I believed in it.

The better version of the gamification argument was always about influence. Digital systems could shape behavior by making progress visible, feedback immediate, status legible and participation more rewarding. That insight helped explain why people would return to apps, communities, learning systems, wellness trackers, workplace tools and customer programs even when no one was forcing them to.

Bunchball, Inc.’s 2010 paper, Gamification 101: An Introduction to the Use of Game Dynamics to Influence Behavior, captured an important early moment in that shift. It helped popularize the idea that game mechanics could be applied outside games to influence behavior in business and digital products. In my own archive, that paper belongs in the same historical context as my interview with Kevin Spier of Bunchball. It was part of the promotional and intellectual material around that conversation, and it should be treated that way: a third-party historical source connected to an interview, not something I wrote or own.

This article does not reproduce the Bunchball paper, its tables or its diagrams. It cites the paper as a historical artifact and uses the renewed attention around gamification to ask a more current question: what should leaders reconsider now that behavior design is everywhere?

The answer is trust.

And in 2026, that question is no longer theoretical.

The Debate Has Moved From Engagement to Addiction

For years, marketers and product teams treated engagement as a clean word. Engagement meant attention. Participation. Return visits. Usage. Time spent. A sign that people cared.

But the public debate has moved. The question now is not simply whether platforms can increase engagement. The question is whether platforms and devices can become addictive, especially for children, teenagers and people already under stress.

That distinction matters. Engagement can be healthy. A patient portal that reminds someone to complete a form can reduce friction. A learning app that shows progress can help a student keep going. A fitness tracker that gives feedback can make behavior change feel more visible. A community platform can help people feel less alone.

Research on gamified learning reinforces this distinction. Adding game elements can support participation, but outcomes depend on design choices, context, autonomy, age, the task and the user experience. A digital game layer is not automatic evidence of better learning or healthier engagement.

But engagement can also become a trap. A system can be engaging because it is useful, meaningful and trusted. It can also be engaging because it is hard to stop.

That is where the old gamification conversation begins to look less quaint. Points, badges, levels and leaderboards were visible. You could see the game layer. You could recognize the device being used to motivate you.

Today’s systems are less obvious. The phone is in your hand. The notifications arrive before you decide to look. The feed refreshes before you know what you came for. The next video starts without asking. The metric tells you how you performed socially. The platform learns what keeps you there. The device becomes the delivery system for a behavioral loop that can follow you from bed to school, work, waiting rooms, caregiving, commuting and the middle of the night.

That is not just engagement architecture. It is platform and device dependence as a business model.

Why the Meta Settlement Belongs in a Gamification History

On August 26, 2026, Meta and a bipartisan coalition of state attorneys general announced a proposed settlement of a federal child-safety case involving allegations that Facebook and Instagram were designed in ways that contributed to addictive or compulsive use by children. Meta denied wrongdoing. Meta updated its announcement on August 27 to say the judge had approved the agreement, while separate litigation involving social-media harms continued.

The financial descriptions differ by source and condition. The California Attorney General described a payment of up to $17 billion over ten years. Meta described the agreement as approximately $18 billion, with part of the payment tied to whether YouTube and TikTok adopt comparable requirements. Those figures should not be flattened into one unsupported claim, and a settlement is not an admission.

The terms matter because they are not only about content moderation. They are about product design.

The announced teen protections include default daily time limits, overnight blocking, school-hour notification limits, hidden like and reaction counts, a non-algorithmic feed option, autoplay controls, usage prompts, enhanced parental supervision, age assurance and independent oversight.

That list is the story.

It is not a list of objectionable posts. It is a list of behavioral levers: time, sleep, notification, reward, feed structure, personalization, automatic continuation, social comparison and age assurance.

In other words, the legal system is now paying close attention to the same basic question gamification raised early: how do designed feedback systems shape behavior?

But the answer has changed.

What began as visible game mechanics has become a much larger debate about addiction to platforms and devices.

Gamification Was Not Born Only as Manipulation

At the time, one of the lazy assumptions around gaming was that men loved games and women somehow did not. Some of us knew better. What felt exciting about gamification was not simply that it borrowed from gaming. It was that it suggested digital design could understand human behavior in a more textured way.

Around the same period, Jane McGonigal was making a broader public argument that games could help people collaborate, solve problems and imagine different futures, an argument she carried into her 2010 TED talk and her 2011 book, Reality Is Broken.

That origin story matters because it prevents the current debate from becoming too simple.

Gamification was also born from curiosity, community design, learning theory, motivation, play and the hope that digital systems could help people participate more fully.

I do not think gamification was inherently manipulative, and I do not think every engagement feature is harmful. The harder question is what happened as the field moved from visible game mechanics toward systems optimized for attention, habit, personalization, social comparison, device dependence and prolonged use.

When Motivation Becomes Extraction

Gamification is not inherently addiction. A progress bar is not a lawsuit. A badge is not a diagnosis. A streak is not automatically manipulation. A leaderboard can motivate healthy competition. A reminder can support someone trying to build a habit. Feedback can make learning, care, community and behavior change easier.

Not every engagement feature is harmful.

The present question is more precise: when does design meant to motivate participation become a system optimized to prolong use, especially for minors, patients, students, caregivers, workers or people already under stress?

That is the line the industry keeps trying to rename.

The old gamification conversation was about adding visible mechanics: points, badges, levels, leaderboards, progress, missions, status and rewards.

The current system is broader and often harder to see. It can include variable rewards, autoplay, streaks, quantified reactions, infinite feeds, algorithmic personalization, push notifications, usage prompts, social comparison, creator incentives and data-driven recommendations that adapt faster than the user can consciously evaluate them.

A badge tells you what game you are playing.

An algorithmic feed may not.

A phone may not feel like a game at all. It may feel like a calendar, a classroom, a support system, a work tool, a family connection, a news source, a medical portal, a marketplace, a camera, a map and a lifeline. That is part of the problem. The device does not simply host the behavior loop. It makes the loop ambient.

The question is no longer whether game-like feedback can increase participation.

The question is whether participation is still serving the person.

A progress bar was never the danger. The danger was building a world where progress, approval, interruption and social belonging all fit in the same pocket.

From Game Mechanics to Device Dependence

Bunchball’s 2010 framework still matters because it recognized something many leaders were only beginning to understand: digital environments do not merely communicate with people. They train behavior.

People respond when they can see progress. They respond when a system gives feedback. They respond when effort has a visible path. They respond when participation creates status, belonging, mastery, curiosity or a sense that a next step is within reach.

That was useful then, and it remains useful now.

In healthcare, education, customer experience, workplace learning, fitness, financial wellness and community design, leaders still need to understand how motivation works. A system that gives no feedback, no progress, no context and no reason to continue is usually asking people to succeed on willpower alone.

That is bad design.

But the problem in 2026 is not that leaders forgot motivation. It is that many systems learned motivation too well.

The U.S. Surgeon General’s 2023 advisory on social media and youth mental health notes both potential benefits and harms, as well as substantial evidence gaps. It also identifies push notifications, autoplay, infinite scroll, quantified popularity and data-driven recommendations as examples of features that can maximize engagement.

That is the shift.

The problem is not only what people see on platforms. It is how platforms and devices shape attention, mood, identity, sleep, social comparison, impulse and return behavior.

When a system is designed to pull people back again and again, the boundary between engagement and dependence becomes the ethical question.

Why This Matters in Healthcare and High-Trust Environments

The same logic does not stay inside Instagram.

Healthcare, fertility, mental health, education, financial wellness and professional-service brands all want people to take useful action. Complete the intake form. Schedule the consult. Follow the care plan. Understand the options. Return to the portal. Track a symptom. Ask a better question. Stay engaged long enough to make a decision.

Those goals are not inherently manipulative. In many cases, they are necessary.

But high-trust environments cannot borrow the logic of platform addiction and call it patient engagement.

A fertility clinic, mental-health organization, healthcare startup or professional advisory firm cannot afford to treat attention as proof of trust. In those contexts, people are often anxious, overloaded, vulnerable, hopeful, ashamed, skeptical or trying to make consequential decisions with incomplete information. They may need clarity, timing, boundaries and dignity more than they need another prompt.

The same design feature can mean different things depending on the relationship.

A reminder can be care. A reminder can also be pressure.

A progress tracker can support someone. It can also make someone feel surveilled, behind, defective or trapped.

A streak can motivate a healthy habit. It can also turn a person’s self-worth into a metric.

That is why behavior design belongs inside trust strategy. The question is not simply whether a feature increases action. The question is whether the action remains aligned with the person’s interest, capacity, consent and understanding.

The Personal Line I Watch Closely

I also watch these questions closely because of my caregiving role for a sibling living with schizoaffective disorder.

That personal context is not evidence that social media or devices cause serious mental illness. I would not make that claim. It does, however, make me more alert to how digital systems can affect people whose attention, perception, stress level, sleep, social connection and sense of reality may already be fragile.

When you love someone navigating that kind of vulnerability, you notice design differently. You notice whether a system calms or agitates. Whether it clarifies or confuses. Whether it supports connection or feeds obsession. Whether it helps someone step away, or keeps pulling them back into a loop they did not consciously choose.

That is not a reason to panic. It is a reason to pay attention.

The first honest question is whether the system helps someone leave.

What Leaders Should Reconsider Now

For marketers, the lesson is not to stop caring about engagement. It is to stop treating engagement as proof of value. A system can be engaging because it is trusted. It can also be engaging because it is difficult to leave.

For product leaders, retention mechanics are no longer only growth tools. They are risk surfaces. If a feature changes behavior, it deserves an ethical review, not only an A/B test.

For healthcare and wellness leaders, patient engagement should not be modeled on platform dependence. The aim should be informed action, not endless interaction.

For educators, digital literacy cannot stop at content evaluation. Students also need to understand interface design, social proof, metrics, personalization, recommendation systems, notification design and the business models behind attention.

For caregivers, the lesson is not to monitor every click. It is to recognize that design details matter: sleep boundaries, notification defaults, visibility of social reward, algorithmic feeds, autoplay, friction to leave and whether a person can stop without losing connection.

For policymakers, design remedies may matter as much as disclosure rules. The architecture of participation is part of the policy problem.

The executive takeaway

Gamification was an early warning that digital environments do not merely communicate with people. They train people, reward people, sort people and shape what people believe is worth doing next. The strongest question is no longer, “How do we get people to engage?” It is, “What kind of relationship are we training people to have with us, our platforms and the devices that carry us everywhere?”

Historical Source and Rights Note

This article cites, summarizes, discusses and critiques Bunchball, Inc.’s 2010 white paper Gamification 101: An Introduction to the Use of Game Dynamics to Influence Behavior as a third-party historical source provided around the Kevin Spier interview. Jennifer Neeley and JND Global are not identified as the author, copyright owner, commissioner or license holder for that paper. Its continued availability online is context, not evidence of ownership or licensing. This article is Jennifer Neeley’s original current analysis of gamification, influence, platform and device dependence, health behavior, ethics and trust.

Bibliographic citation: Bunchball, Inc. (2010). Gamification 101: An Introduction to the Use of Game Dynamics to Influence Behavior. Bunchball, Inc.

Evaluate the Influence System Before It Becomes a Reputation Problem

If your organization is designing prompts, dashboards, notifications, streaks, AI workflows, patient portals or community loops, do not wait for a trust problem to tell you the system worked too well. Jennifer helps leaders evaluate influence systems before they become reputation problems.

Explore Strategic Advisory   |   Review options and availability

Continue with Healthcare Advisory, Insights, The Influence Project and Generationisms.

Sources and further reading

AI Can Find the Pattern. It Still Can’t Tell You What Matters.

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Ten years ago, I called the useful output of marketing data contextual intelligence. The phrase came from a frustration I kept encountering in client work: organizations had more numbers than they knew what to do with, yet the numbers rarely answered the question leadership was actually asking.

That problem has not disappeared. We have simply made it faster.

AI can now summarize customer conversations, classify sentiment, surface anomalies, compare competitors and generate a plausible explanation before a team has finished its first cup of coffee. That is useful. It is also easy to mistake speed for understanding.

AI can find a pattern. It cannot decide, by itself, whether the pattern matters to your business.

The dashboard is not the decision

A dashboard can show that engagement rose. It cannot tell you whether the audience became more qualified, whether the message attracted the wrong expectations or whether the activity moved anyone closer to a consequential decision.

A sentiment model can label a conversation positive. It may not recognize that the praise is sarcastic, that the loudest voices are not representative or that the same words carry different stakes in healthcare, financial services and entertainment.

A generative AI system can produce a confident narrative from incomplete evidence. Confidence is part of the interface. It is not proof that the explanation is correct.

This is why current guidance from the National Institute of Standards and Technology treats trustworthy AI as an organizational risk-management problem, not merely a model-performance problem. The people deploying a system still have to define the context, evaluate the output and decide what consequences they are willing to accept.

Marketing needs the same discipline.

What contextual intelligence actually requires

Contextual intelligence begins before the analysis. It starts with a sharper question.

  • What decision are we trying to make? A report built to defend last quarter’s activity is different from an analysis designed to determine what should change next.
  • Whose behavior matters? More attention from an irrelevant audience can make a dashboard look healthier while weakening the business case.
  • What is the comparison? A rising metric can still represent lost ground if the market, competitor or cost moved faster.
  • What is missing? Customer-support records, sales objections, referral patterns and frontline observations may explain what platform data cannot.
  • What would change our mind? If no plausible evidence could alter the recommendation, the analysis may be decoration for a decision already made.

This is the point where professional judgment enters. Judgment does not replace evidence. It determines how evidence should be interpreted when definitions are imperfect, incentives conflict and the consequences are not evenly distributed.

Three places marketing analysis still loses the plot

1. Share of voice without share of relevance

In the original 2016 article, I described using social-listening and competitive-analysis tools to compare share of voice. That work was valuable, but the metric was never sufficient on its own.

A brand can dominate conversation because it is trusted, because it is controversial or because it has become the subject of a joke. The volume is real. The strategic meaning is not contained in the volume.

The useful questions are more specific: Who is talking? What do they believe? Which claims are traveling? Where is the evidence coming from? Does the conversation affect consideration, confidence or action?

2. Influencer reach without decision influence

The same distinction applies to influencer analysis. Follower count and engagement can describe visibility. They do not establish whether the creator changes a decision that matters to the organization.

An influence map should connect the person, the audience, the subject, the stage of the decision and the evidence of movement. Otherwise, it is a popularity map wearing business clothes.

This is the gap I continue to examine through The Influence Project: attention is visible, but the change produced by attention is often harder to see.

3. Crisis signals without operating context

Monitoring systems can surface a spike in negative conversation. They cannot know whether the organization should respond publicly, correct an operational failure, contact a small affected group or wait for an inaccurate story to lose momentum.

The wrong intervention can amplify the very narrative a team hoped to contain. Context includes the source of the claim, the credibility of the people carrying it, the organization’s evidence, the affected stakeholders and the cost of acting too quickly or too slowly.

AI raises the value of judgment

The more efficiently technology produces analysis, the more important it becomes to know what the analysis is for.

That is not an argument against AI. I use AI because it can reduce administrative work, help organize large bodies of material and make patterns easier to inspect. But I do not ask it to assume responsibility for the decision. It has no client history, institutional memory or personal stake in the outcome unless people deliberately provide and govern that context.

The competitive advantage is not simply having more data or a newer model. It is building an organization that can connect evidence to a real decision without losing sight of the people, incentives and risks behind the numbers.

What leaders should ask before accepting an AI-generated answer

  • What data was included, and what important evidence was unavailable?
  • Which definitions or proxies are doing the most work in this conclusion?
  • Is the system describing correlation, or are we treating it as an explanation?
  • Who could be misread or disadvantaged by this interpretation?
  • What human observation supports or contradicts the output?
  • What decision will this answer change?

In 2016, I argued that content without context wastes time and resources. In 2026, the warning applies to intelligence itself.

More output is not more understanding. The work is turning evidence into judgment people can act on.

Sources

Originally published February 29, 2016. Materially updated August 31, 2026.

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Related: Your Metrics Say It’s Working. Your Business Says It’s Not. Here’s Why. | Fractional & Interim Marketing Leadership | About Jennifer Neeley

When Influence Outpaces Authority: The Trust Gap Holding Expert-Led Brands Back

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In The Quiet Merger of Power and Influence, I wrote about how attention is no longer neutral terrain. What we see, read, and react to is shaped before most of us consciously choose it.

There’s another shift unfolding beneath that one.

Influence now moves faster than authority can respond, and judgments often form before expertise has a chance to enter the room.

Authority comes from role, training, institution. Influence forms through perception—through tone, visibility, repetition, and the subtle cues that signal credibility within a community. When authority and influence align, decisions feel stable. When influence outruns authority, interpretation hardens early, and correction becomes harder.

I’ve seen this across industries that rarely think of themselves as connected.

In fertility healthcare, physicians with decades of clinical experience often meet patients who arrive confident in conclusions shaped by TikTok creators or online forums. The doctor’s authority hasn’t changed. But the patient’s judgment has already been formed. The consultation begins not with diagnosis, but with reframing.

In growth-stage tech companies, founders assume product strength will establish credibility. But investors and customers respond as much to narrative coherence as to technical merit. A company can have strong data and still struggle if leadership signals feel misaligned. Decisions are rarely made on facts alone; they are made on perceived trust.

In consumer brands, I’ve been brought in when influencer programs were generating reach but not results. On paper, everything looked successful. In practice, audiences sensed inauthenticity. The mismatch was subtle – values didn’t quite align, messaging drifted, the partnership felt transactional. Engagement numbers rose while credibility slipped.

Even in media and podcast building, influence compounds through consistency more than volume. A show doesn’t grow because of a single viral moment. It grows because listeners begin to trust how the host thinks – not just what they say.

These are not isolated marketing missteps. They reflect something larger: influence behaves like a form of currency, but its value depends on shared perception. It builds through reliability and erodes through inconsistency. Visibility can accelerate it, but visibility cannot manufacture it.

Part of this shift is generational. Those who grew up before digital saturation were trained to look for institutional authority. Younger cohorts are more likely to evaluate credibility through networks, creators, and the signals surfaced in their feeds. This isn’t about attention spans. It’s about where judgment forms.

In conversations about my developing concept of Generationisms – including on the Empathy Unbound podcast—I describe this as a difference in how cohorts learn to assess trust. Generations aren’t simply divided by age; they’re shaped by the information systems that trained their early instincts. What reads as expertise to one group may read as branding to another. What feels transparent to one may feel curated to someone else.

 

When organizations don’t recognize that influence is moving ahead of them, they miscalculate. They rely on title when perception has already settled. They communicate from authority while influence circulates elsewhere, shaping how that authority will be received.

Influence without authority destabilizes expertise. Authority without influence leaves expertise unheard.

The challenge now is not simply to communicate more clearly. It is to understand how credibility is judged before authority ever enters the frame.

And increasingly, that judgment is shaped by systems as much as by people.

Before an audience encounters a message, it has already been filtered, ranked, and prioritized. Some signals surface. Others quietly fade. What feels like organic visibility is often the result of invisible sorting.

That shift matters.

If influence is the currency, artificial intelligence is becoming the screening layer. It determines which signals appear coherent, consistent, and credible enough to surface at scale. By the time human trust forms, a preliminary judgment may already have been made.

Which raises a harder question: when machines participate in deciding what counts as credible, do they recognize the kind of authority we believe we hold?

That question sits at the center of another piece of mine, AI Is the Gatekeeper. It Screens for Credibility. Does It Recognize Yours? Because if influence now moves faster than authority – and AI increasingly shapes which influence is seen – then judgment itself is changing.

 

And that shift is not theoretical. It is structural. It’s up to each of us to get ahead of it.

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Jennifer helps leaders diagnose the gap between attention, authority, trust and the decisions people actually make.

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