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Article

The AI Readiness Gap Series: Why Normalization Is the Most Skipped, and Most Essential, Phase of AI Adoption

April 30, 2026
By Zack Kavanaugh

This is the second installment in a series on what it takes to close the gap between AI investment and tangible business impact.

In the first piece, I argued that the real barrier to AI adoption is not the technology itself. It is the human side of change. You can have the tools, investment and strategic urgency — and still fall short if your people are not ready to come with you.

A new data point from Harvard Business Review reinforces just how widespread this challenge has become. In its annual AI & Data Leadership Executive Benchmark Survey, 99% of respondents said investments in data and AI are a top organizational priority.

And yet, 93% identified human issues — culture and change management — as the key challenge to AI adoption, the highest percentage in the survey’s 15-year history.

That is the paradox organizations are facing right now. We have never been more aligned on the importance of AI, and we have never been clearer about what is standing in the way.

So, what do we do about it?

That is what this series is for. In forthcoming posts, I will go deeper into each phase of the AI adoption continuum I introduced in the first piece, starting with the one most organizations rush past: normalization.

What Normalization Means

Normalization is not a communications campaign. It is not a CEO video about the future of work. And it is not a training session scheduled before a platform goes live.

It is the deliberate, ongoing work of helping people feel safe, supported and included as they begin to make sense of AI and what it may mean for their work. It is how organizations “de-weird” the technology, create space for honest questions and begin making AI feel like something that belongs in everyday work rather than something being imposed from above.

Why Normalization Matters

Psychological safety is a critical condition for learning, experimentation and collaboration. When people don’t feel safe, they don’t ask questions, test ideas or admit what they don’t know. They comply quietly, or they quietly disengage. Neither is adoption.

The goal of normalization is to close the distance between where people are emotionally and where the organization needs them to be. Some employees will move quickly and begin experimenting right away with the tools they now have at their disposal.

Others will be unfamiliar, skeptical or unsure what this shift means for their role, their value or their future. For those employees especially, adoption does not begin with training. It begins with the feeling that engaging with AI will not make them look foolish, irrelevant or behind.

And creating that kind of readiness requires three things done well.

Three Things That Actually Work in the Normalization Phase

1. Create space – and systems – for listening.

The biggest mistake organizations make in this phase is starting with all the answers. They launch the platform, send the announcement, schedule the training – and assume those things alone will shift mindsets and change behavior.

They won’t.

What creates the conditions for readiness is being heard first. At its core, this means building an ongoing conversation about AI across the organization – one that gives employees regular, low-pressure spaces to surface questions and ideas, voice concerns and get honest responses.

That can take several forms: Office hours. Small-group sessions. Open Q&A. Pulse surveys and live polls. Not as symbolic gestures, but as mechanisms for shaping how AI gets introduced into the work people actually do.

And if you’re going to ask people to take the time to engage, you must show that what they share matters. The only thing worse than not asking employees for feedback is asking and then ignoring what you hear.

That’s why listening cannot be treated as a singular event. It has to be built into the rollout itself.

One all-hands meeting is not an AI listening strategy. Listening has to be structured, recurring and visibly tied to action. When people see their input reflected in how your AI transformation evolves, trust grows. When they don’t, skepticism hardens.

2. Coach leaders to show curiosity.

This may be the most uncomfortable shift for many leaders — and one of the most important.

We often expect leaders to project confidence during change: Here’s where we’re going. Here’s why it’s the right call. Here’s what I need you to do. In many transformations, that kind of clarity is reassuring. But AI introduces a level of uncertainty that makes a different posture more effective.

Much of this is still unfolding, and employees know that. When leaders over-index on certainty, it can unintentionally create distance. What tends to build trust instead is transparency – a willingness to share what is clear, what is still emerging and what they themselves are learning along the way.

Leaders who say, Here’s what I tried last week. Here’s where it didn’t go as expected. Here’s what I’m still figuring out, give their teams permission to approach AI the same way: openly, curiously and without needing to have everything resolved upfront. In doing so, they model the kind of learning culture this moment requires.

And this does not have to be overly formal. It can be as simple as a leader taking a few minutes in a team meeting or a 1:1 to share how they have been using AI, where it has helped, where it has fallen short and then asking whether others are seeing similar use cases or running into similar issues. Moments like that make AI feel less abstract and more like part of how the team solves problems and gets work done.

A little humility goes a long way here. Saying, We don’t have all the answers yet, but we want to understand what you’re seeing and what you need, helps build the trust and reciprocity that make people more willing to engage over time.

3. Engage both champions and skeptics.

Most AI rollouts activate champions. Fewer engage skeptics.

That’s a missed opportunity – and often a source of quiet resistance that never gets addressed.

Champions build belief. They carry peer influence, spread early momentum and make it socially safe to try.

But skeptics matter too. They ask the questions others are hesitant to raise, stress-test the strategy and identify blind spots the optimists have not yet considered.

And both groups need to be identified across the organization. The concerns people have, the language that resonates, and the use cases that feel relevant will differ by role, function, team and location. A centralized group of AI-forward employees alone will not catch those nuances.

Bring both into the process. Involve them in reviewing messaging before it goes out. Ask them to serve as ears on the ground within their teams, surfacing the quiet hesitations people may not yet be voicing openly. Invite them to curate real-world examples, flag what feels off and help co-create the evolving story – not just receive it.

When the people most likely to champion the change and the people most likely to question it both have a hand in shaping the narrative, two things happen: the strategy gets sharper, and trust grows. That makes the rollout more credible, because it starts to reflect the reality of how different parts of the organization will actually experience it.

How You Know It’s Working

Normalization isn’t a box you check. It’s a condition you build. Here are three signals that tell you the work is landing:

  • Safety and trust are growing. Survey data and anecdotal feedback show people feel comfortable asking questions about AI – even uncomfortable ones.
  • Ownership is being distributed. Champions and skeptics are in the room, giving honest input, not just nodding along.
  • Early participation is building. Attendance at office hours, demos and opt-in sessions is growing – not because it’s mandatory, but because people are curious and finding value from what you’re sharing.

These signals matter because they show people are getting more comfortable – asking questions, engaging more openly, and beginning to see where AI might fit into their work.

But that does not mean everyone is in the same place. In most organizations, some people will already be experimenting or integrating AI into parts of their workflow, while others are still making sense of what this technology means for their role, their value and their day-to-day work.

That is why normalization matters. It is not something you complete before moving on. It is the ongoing foundation that helps leaders understand where people are, how they are experiencing the change and what they need next as the work continues.

Organizations should be moving. But they need to keep listening as they do. That is what makes adoption more coherent, more durable and more likely to spread beyond the early adopters.

Article

How to Shape a Brand’s AI Visibility in 2026: Six Moves for Communications Teams

April 28, 2026
By Margaux Vega

As AI increasingly determines what gets said about brands, communications teams can rise as architects of brand visibility – driving the credibility, narratives, and signals that AI relies on. This article outlines the six strategies communicators can use to help shape whether their brand shows up in AI, and how.

PR has always been about relationships. The ones that move needles, build credibility, and shape how the world sees you. Now, the most powerful voices are AI powered assistants you’ll never meet, answering questions you’ll never hear.

When someone asks ChatGPT or other Large Language Models (LLMs) about your company, they get a synthesized answer from thousands of sources, including your website, your LinkedIn posts, your press releases and your hard-earned media coverage.  And only 8% of people consistently verify these responses. The vast majority just accept them at face value.

While these changing behaviors have become the new enemy of the C-suite, it is re-writing the playbook for you as a communicator, putting PR professionals at the most powerful intersection to solve it.

How? You influence what gets published. You shape narratives and determine consistency. You build the earned media relationships that influence these LLM systems. And you have meaningful leverage to shape how AI learns about your company.

This is not a new job. It’s the same job with a much larger stage.

Top 6 Communications Strategies to Increase Potential AI Visibility

AI is evolving quickly, but you can drive positive impact today through everyday programming you already know how to do. Here is how to get started:

1. Press releases are your AI blueprint. Despite the name, press releases are really just centralized documents that help explain your brand and your products. As AI has emerged, press releases have returned as a valuable source of truth to inform how machines understand you too. In a recent audit, FleishmanHillard found that optimized releases, on a company’s owned newsroom, can inform as much as 20% of what appears when someone asks an LLM about your company. Publish press releases and write them for machines – which luckily will make them easier for humans to read too – with clarity, facts first, no fluff. Make it crawlable and easily extractable. Make it count.

2. Depth beats reach. Vertical-specific publications that go deep on your topic can carry more weight with AI than hundreds of mentions in top-tier press. In many cases FleishmanHillard has seen single outlets contribute to 20-50% or more – of all AI responses about a single brand. Identify who these folks are for you and help them write informative and detail-rich stories. While maybe smaller in traditional reach, these key media carry huge, concentrated influence tohelp make your company visible within LLM responses.

3. Consistency wins. AI systems reward consistency. It’s one of the strongest signals of credibility. If your brand says different things across your website, press releases, bios and media coverage, it weakens trust and LLMs will turn elsewhere. Standardize 2-3 positioning statements, key topics of importance, and repeat them relentlessly. Every consistent mention reinforces the last. This is how machines learn to trust you and how you can build authority.

4. Answer the questions they’re actually asking. Identify your top 10 audience questions about your space. Better yet, ask ChatGPT what they are. Then answer them. Directly. Publish them where AI will find them. When LLMs search for expert voices in your category, yours should be cited.

5. If you want it known, it must be published: LLMs need data to crawl, so if something important lives behind a paywall or happens at a live event, it is largely invisible in AI outputs. Publish it on YouTube, LinkedIn, your blog, and your website. The content you put out is the content AI learns from. The content you gate might as well not exist.

6. Technical details matter: Metadata. Schema markups. Structured headings. You can partner with your web team on this. It can be the difference between being crawlable and creating unbranded real estate. Make sure it’s done.

The C.R.A.W.L.S. Framework to Increase AI Visibility Strategy

These six moves form a framework that can be easily remembered. Just remind yourself to create a strategy that C.R.A.W.L.S.

  • Consistency.
  • Releases.
  • Authority.
  • Written.
  • Linchpin media.
  • Structured backend.

If yes to all six, you’re not just generating coverage. You’re actively educating the systems that shape how the world understands your brand.

You Already Know How to Do This

LLM accuracy will depend heavily on the quality of input. PR teams will be able to create and optimize content that has the most potential to be visible to LLMs and help shape how their industry is defined.  

Margaux Vega width= Margaux Vega Is a FleishmanHilllard senior lead and strategist for Fortune 500 companies, driving integrated communications from strategy to shape brand perception at scale. At the forefront of new ways of communications thinking, Margaux is focused on visibility and influence in an AI-first landscape.

 

 
Article

A Corporate Communications Evolution: Strategies for the Agentic Age

April 22, 2026
By Matt Rose

Corporate Communications has long operated on a stable premise: organizations craft messages, distribute them through controlled and earned channels, and monitor how those messages are received. While tools and platforms have evolved, the underlying model has remained largely intact. At its core, the function exists to sustain visibility, build trust, and protect and enhance reputation among key stakeholders in ways that support business performance and long-term value.

Artificial intelligence challenges that model at a structural level.

The most significant shift is not faster content production or the automation of routine tasks. It is the growing role of AI as an intermediary in how information is consumed, interpreted, and acted upon. Where algorithms once filtered what audiences saw, AI now reshapes it. Organizations are no longer communicating directly with stakeholders; they are communicating through systems that filter, summarize, and reframe information before it ever reaches human audiences.

This shift extends well beyond efficiency. Historically, Corporate Communications assumed that messages, while filtered by journalists, analysts, and platforms, would remain largely intact if those filters were well understood. AI changes that dynamic. Information is no longer simply filtered; it is deconstructed and recombined with other sources to produce new outputs such as summaries, recommendations, and comparisons. Organizations are therefore not communicating discrete messages but contributing inputs into systems that determine how those messages are ultimately presented and understood. The implication is a shift from controlling the message to structuring both message and context, so that they are interpreted accurately by AI systems.

The Changing Nature of Information Consumption

Across stakeholder groups, this dynamic is already taking hold. Investors use machine-assisted tools to analyze earnings calls and identify inconsistencies. Journalists rely on AI to accelerate research and draft initial narratives. Policymakers and regulators are beginning to incorporate AI-generated summaries into their workflows. Customers and patients are turning to AI as a primary source of information and interpretation. In each case, information is no longer encountered in its original form. It is mediated.

This introduces a new layer of risk and opportunity. Errors, inconsistencies, or ambiguities can be amplified quickly. At the same time, well-structured, consistent information can be propagated more effectively than ever before. As a result, narrative control is shifting upstream, from the point of publication to the point of interpretation.

In this environment, the traditional focus on outputs is no longer sufficient. Press releases, speeches, and media engagement remain important, but they are only part of the picture. What matters is not just whether a message is distributed, but whether it is understood as intended across a range of human and machine interpreters. This requires a shift from outputs to systems.

From Outputs to Systems

An effective communications function must be capable of continuously ingesting external signals, interpreting their significance, generating aligned messaging, assessing potential risks, and executing responses in a coordinated manner. These activities must be integrated rather than siloed and must operate at a speed that reflects the pace of the external environment.

Many organizations are experimenting with discrete AI applications, such as automated content generation or enhanced media monitoring. While these efforts can deliver incremental value, they do not address the underlying structural challenge. Without integration, they risk creating a patchwork of capabilities that improves efficiency in isolated areas but does not fundamentally improve how the organization is understood or how effectively communications supports business outcomes.

The Emergence of Agentic Architectures

What is beginning to emerge instead is a more integrated, system-based model. Distinct AI capabilities perform specific roles within the communications lifecycle. Some systems monitor external signals, drawing on media, social, policy, and market data. Others synthesize this information into a structured understanding of emerging narratives and stakeholder sentiment. Additional capabilities generate content, assess potential risks, or support execution.

These elements are increasingly connected through an orchestration layer that ensures coordination across activities. The result is not a collection of tools, but a system that can sense, interpret, and respond in a continuous loop.

Importantly, this shift does not eliminate the role of human practitioners. Rather, it redefines it. As routine tasks are automated, the relative importance of judgment, context, and strategic decision-making increases. Communications leaders are required to not only craft messages, but to oversee how systems generate and deploy those messages at scale. While execution becomes more system-driven, accountability does not shift. Leaders remain responsible for the accuracy of content, the outcomes it produces, and the trust and credibility the organization maintains with its stakeholders.

Implications for Organizational Design

This evolution has implications for organizational design. Many communications functions remain structured in silos, separating media relations, social and digital, executive communications, and reputation management. While this structure provides clarity, it can lead to fragmentation in execution. Inconsistencies across channels become more visible, and the ability to respond quickly to emerging issues is constrained.

An AI-enabled model places greater emphasis on integration. Shared data layers, common intelligence frameworks, and coordinated workflows become central. The goal is not to eliminate functional expertise, but to ensure that it operates within a unified system rather than in parallel tracks. In practice, this can result in a more centralized model supported by shared capabilities.

Rethinking Measurement

Measurement must evolve as well. Traditional indicators such as volume of coverage, impressions, or engagement rates capture activity, but not whether stakeholders are interpreting the organization’s actions and positions as intended. Advances in data availability now make it possible to assess who is reached, whether priority audiences are engaged, and how messages are interpreted. Metrics such as relevant audience reach, message resonance, and narrative alignment provide a more accurate view of effectiveness in shaping stakeholder perception and supporting business outcomes.

These approaches are more complex and often more resource-intensive, but they reflect how communication actually works in an AI-mediated environment. The central question is no longer how far a message travels, but how accurately it is understood and by whom.

Implementation Considerations

Despite the sophistication of the end state, implementation does not require a comprehensive transformation from the outset. Organizations that are making progress typically begin with focused applications that address clear needs, such as executive briefing tools that synthesize external signals or systems that accelerate the drafting of media responses while maintaining consistency with approved messaging.

Efforts to modernize Corporate Communications have often been constrained by cost concerns and the perception that its impact on business outcomes is indirect. In this case, those barriers are lower. Most large organizations already have access to advanced AI capabilities through enterprise technology investments. The incremental cost of applying them within communications is relatively modest. The greater challenge lies in rethinking how the function operates and how value is defined.

The Risk of Inaction

The risk of inaction is not that organizations move too slowly internally. It is that their stakeholders move more quickly externally. As AI becomes embedded in how information is consumed and decisions are made, narratives are increasingly shaped by systems outside the organization’s control. Inconsistencies are surfaced more quickly, and misinterpretations can scale rapidly.

Addressing this risk requires more than faster response times. It requires ensuring that the organization’s information is structured, consistent, and accessible in ways that support accurate interpretation.

Conclusion

Artificial intelligence is not simply enhancing Corporate Communications. It is changing the conditions under which communication takes place. Organizations that move toward integrated, system-based approaches will be better positioned to maintain control over how they are understood, sustain trust with stakeholders, and support long-term business performance and value. Those that do not may find that control increasingly resides elsewhere.

In a world where perception is shaped as much by machines as by people, the ability to manage how information is interpreted becomes a core strategic capability.

Matt Rose width= Matt Rose is the Americas Lead for Crisis, Issues & Risk Management. An SVP & Senior Partner in New York, he brings more than 30 years’ experience in advising organizations on crisis and issues management, risk mitigation, and reputation recovery. He has guided companies through reputational crises, labor issues, regulatory challenges, ESG controversies, and high-profile litigation.

 

 
Article

Notes From the Road: RSA Conference 2026 Edition

April 1, 2026
By Scott Radcliffe

While at this year’s RSA Conference I overheard a very senior security executive at a well-known security company remark that he “came to RSA expecting a security conference and instead seemed to arrive at an AI conference.” Like many things said in jest, there was more than a little truth buried inside.

Walking through the exhibitor halls, you’re immediately struck by the nearly comprehensive inclusion of AI in nearly every offering on display—from threat detection to incident response to risk management. It seemed every vendor had either retrofitted their solution with AI or built one from scratch.

It would be easy to dismiss it all as hype, another technology cycle where marketing teams latch onto a buzzword without a lot of substance to offer under the surface. Surely at least a little is snake oil, but to dismiss everything as vaporware would be miss the dramatic and evolutionary step AI represents for the cybersecurity space.

In the short twelve months since last year’s RSA conference, we’ve witnessed countless AI experiments, implementations and innovations, and even the most experienced security minds in the world are grappling with uncertainty about what’s coming next.

The Great Shift: From “Humans in the Loop” to Autonomous Operations

At last year’s conference, most discussions around AI in security were grounded at some level on keeping “humans in the loop” of the decision-making and execution process. AI could augment, assist and accelerate actions taken by human admins and users, but the final call had to rest with a human who understood context, nuance and consequences.

That narrative has fundamentally shifted in a single year. As Wall Street Journal reporter James Rundell pointed out from his first impression of this year’s conference, the industry has undergone a philosophical change over the course of the last year. Security teams are no longer asking whether AI should act independently—they’re asking how to best, and hopefully safely, architect systems where AI must act independently and, quite often, in real-time.

This isn’t a subtle distinction. It represents a wholesale reimagining of how we defend our networks and systems. The efficiency gains of this headlong leap into AI are real, but so are the risks, and that tension is what keeps many security leaders up at night.

Identity as the New Perimeter

If autonomous AI is the emerging challenge, then identity has become an even more critical battleground. Anyone who’s paid attention to the security space recently is familiar with the popularity and continued growth of identity-based attacks that use known, often re-used credentials like usernames, email addresses, and passwords to gain access to systems. With AI systems now being granted expanding autonomy and access to sensitive data, the question of whom, or more accurately, what—should be able to access particular systems, networks, or information has taken on even greater urgency.

Early implementations of AI agents have already demonstrated the dangers of unchecked permissions. Give these systems too much access or too broad an ability to act, and they can quickly spiral into trouble. A key message that echoed through many of the talks at RSA this year make clear that guardrails aren’t optional, they’re foundational. As organizations deploy AI more widely, the ability to establish firm, granular controls around identity and access will be absolutely critical. In a world of autonomous intelligent agents, identity becomes the ultimate arbiter of what’s possible.

AI’s Dual-Use Dilemma for Security: Offensive Operators Will Have a Huge Head Start

Perhaps the most sobering insight I took away from RSA this year is how far behind defenders will be, and for how long, in the AI race. AI certainly represents an immediate force multiplier for attackers, and it will take a significant amount of time for defenders to catch up. Kevin Mandia, a veteran cybersecurity executive with decades of experience founding some of the industry’s most iconic companies, put some sobering specifics to this sentiment. In his view, AI will provide a clear advantage to offensive operations for the next two years before the defense can accumulate enough data and operational experience to train systems that keep pace.

The advantage goes beyond speed, though that’s certainly part of it. AI enables attackers to operate with precision and personalization previously unattainable at scale. Rather than deploying generic attack tactics across broad targets, AI allows threat actors to generate bespoke attack plans tailored to individual organizations—understanding their specific vulnerabilities, mimicking their communication patterns, and timing operations to maximize success. For defenders, holding the line while playing catch-up will be a daunting but necessary challenge.

The Sovereignty Conversation: A Quiet but Consequential Shift

Away from the AI spotlight, Microsoft’s CISO for AI and Technology Data, Igor Tsyganskiy, brought up a fascinating nuance to the data sovereignty trend many cloud providers are facing during a fireside chat. As organizations continue to adopt cloud architectures, where data lives—physically and jurisdictionally—has moved from a compliance checkbox to a strategic security consideration.

Different regions, regulatory frameworks and threat landscapes all create scenarios where the location and control of data become material to security architecture. This trend will likely only intensify as companies navigate an increasingly fragmented geopolitical environment. Data sovereignty has been a growing trend for a number of months at this point. The interesting point Tsyganskiy raised at the conference last week, however, was the urgent need for organizations to consider operational contingencies as well in their plans to satisfy data sovereignty requirements.  A recent airstrike that destroyed Amazon’s data center in Bahrain underscores the point: it doesn’t take a missile to disrupt operations, so organizations should be prepared as the answer may not be as easy as flipping the switch to another data center in a desired location.

For security and communications leaders, this means the conversation with the business can’t remain purely technical. It has to account for regulatory, geopolitical and strategic business considerations.

The Fundamentals Still Matter (Maybe More Than Ever)

Rob Joyce, the former director of cybersecurity at the NSA, emphasized a reality that can sometimes get lost amid the AI hype: the fundamentals of cybersecurity still remain a powerful and largely effective defense. His point is worth emphasizing, especially at a conference filled with vendors pitching the latest solutions the security industry has to offer.

Attackers, Joyce argued, continue to disproportionately target organizations that don’t execute the basics well. Though those attacks will only grow as bad actors begin to use AI as a force multiplier, organizations that prepare by adhering closely to good security fundamentals will be in a much better position to weather the coming storm. This means companies that lag in patching systems, haven’t broadly deployed multi-factor authentication, maintain inadequate logging practices, or generally fail to stay prepared are putting their systems at much greater risk.

I would argue the same applies to communications and marketing teams. Ensuring you’re prepared, properly integrated with the rest of the organization and generally ready to help your organization stay ahead of a threat environment evolving at exponential speed is more important than ever. Furthermore, I’d add that the time has come for marketing and communications teams to do their part and partner with technical teams to ensure the security conversation organizations have with their boards and business leaders isn’t dominated by buzzwords but is instead grounded in ensuring the foundational elements of security are strong enough to build upon.

It’s certainly easy to walk away from RSA 2026 with a sense of dread. But the deeper message embedded throughout the conference would be missing.

Yes, AI represents a significant challenge. Yes, attackers have a near-term advantage. Yes, data sovereignty is becoming a more complex puzzle to solve. But it’s a challenge I think we’re all up for if we’re ready.

Scott Radcliffe is FleishmanHillard’s global director of cybersecurity, leading the firm’s Cybersecurity Center of Excellence and advising clients on rising cyber risks. He recently rejoined FH from Apple, where he led cybersecurity communications and previously served as the agency’s senior global data privacy and security expert.

Article

Why Your AI Rollout Is Stalling (And What Actually Moves the Needle)

March 25, 2026
By Zack Kavanaugh

Most organizations are investing heavily in AI but seeing minimal return. The tools are rolling out. The impact isn’t landing. This article examines why adoption is stalling, what employees are really feeling and why a new model for change is essential to close the gap between investment and outcomes. 

There’s a paradox unfolding in organizations right now – and its quietly derailing AI initiatives at scale. 

Companies are pouring millions – in some cases, billions – into AI infrastructure. Platforms are deploying. Training programs are launching. And yet, most organizations report that their AI efforts aren’t delivering the results they expected.  

In fact, 95% of generative AI pilots fail to reach measurable business impact. Only 1% of organizations consider their deployments truly mature. And across the workforce, a third of employees are actively considering leaving over unclear AI expectations and lack of support. 

The investment is real. The adoption and impact is missing – and the disconnect is striking.  

So, what gives? 

What we’ve learned supporting organizations through AI transformation is this: they’re treating it like a technology problem when it’s actually a people problem. And until we acknowledge that difference, adoption – and business impact along with it – will continue to stall. 

The Emotions Nobody’s Talking About 

Walk into most organizations right now, and the conversation sounds logical. “Here’s the business case. Here’s the ROI. Here’s the productivity uplift.” But underneath that rational overlay is something messier – and infinitely more powerful: how people actually feel

The data points here are endless – and we could marshal dozens more to prove that adoption is stalling. But honestly? They don’t really matter. What does matter is whether you feel your organization is progressing at the rate you know it’s capable of.  

If not, or if you don’t know where to start to answer that question, it may be time to look closely at your adoption strategy.  

The AI Readiness Gap 

This is the mistake we see companies continuing to make: assuming a strong business case is enough to win people over. We’re treating AI adoption like a switch you flip, when it’s actually a continuous, messy, non-linear process that requires people to move through change at different speeds. 

Most organizations are still leaning on traditional change models – the kind that default to logic and expect a single launch moment to do the heavy lifting.  

But AI transformation isn’t a single moment. It’s not a product launch. It’s a fundamental shift in how people think about their work, what they value in their roles and whether they trust the organization to shepherd them through it. 

That gap – between what leaders expect and what employees experience – is the real barrier to adoption. 

A Different Path Forward 

What’s needed is a model designed for how people actually change. Not how we think they should change. How they actually do. 

The good news: that change management model exists; it features three phases and three layers of employees’ experience, and all three matter equally: 

Phase 1: Normalization – The Emotional Layer 

Normalization is about shifting mindsets – listening, building psychological safety and trust, de-weirding tools and making AI part of everyday conversation. Before anyone can adopt anything, they need to feel safe, seen and supported. This means listening before launching.  

It also means leaders modeling vulnerability, not just expertise. And it means identifying trusted voices –both champions and skeptics – and giving them visibility in shaping the journey. When you remove the mystique around AI and make it visible in how people actually talk and work, adoption becomes possible. Listening earns you permission to lead. 

Phase 2: Experimentation – The Personal Layer 

Experimentation is about shaping habits – encouraging participation and creating low-risk opportunities to try, learn, fail safely and reflect. Once people feel safe, they’re ready to connect AI to their own work and identity.  

This is where curiosity replaces skepticism. You can help replace skepticism with curiosity when you share stories from peers – not polished case studies, but real moments where someone figured something out or tried something that didn’t work. When people see themselves in the adoption story, they move from “this doesn’t apply to me” to “I see where this helps.” Experiments become personal. Habits begin to form. Failure becomes data, not judgment. 

Phase 3: Integration – The Operational Layer 

Integration is about scaling impact – building and validating use cases, measuring value, embedding AI into workflows and scaling solutions. When adoption becomes embedded in how work actually happens, impact becomes measurable and repeatable.  

Proven experiments turn into templates and workflows. Success stories become standard operating procedures. Recognition systems reward AI fluency. And AI stops feeling like the new initiative and starts feeling like “just how we work.”

The Continuum, Not the Launch 

The shift here is fundamental. Instead of treating adoption as a destination, we’re treating it as a progression.  

Instead of betting everything on a single launch moment, latest tool or new corporate mandate, we’re developing constant feedback loops. Instead of assuming readiness, we’re building it – intentionally, measurably and with employees at the center.  

Whether you’re leading an organization, a department or a team, your people will never move cleanly through one phase alone. They will move at their own pace. Some people will be experimenting while others are just beginning to normalize. And as new information emerges, they will oscillate back and forth – revisiting earlier phases to deepen their foundation before moving forward again. 

The bottom line: AI adoption accelerates only when the environment is ready – when culture, clarity and context catch up to ambition. That’s when change starts to feel real. And when people decide it’s worth leaning in.  

More to come on all this. Stay tuned.  

Article

AI is Reshaping Communications: Inside FleishmanHillard’s Enterprise-Wide Approach

February 19, 2026

In his new Forbes piece, Bernard Marr explores the breakneck pace of AI transformation in the communications landscape with Ephraim Cohen, FleishmanHillard’s global head of data and digital. Cohen reveals that unlike past technological shifts that took decades to prepare for, today’s AI evolution is happening so rapidly that even full-time experts are struggling to keep pace.

Watch Their Full Conversation Here:

Three Key Takeaways:

1. Democratizing AI Across the Organization Rather than creating an elite “AI team,” Cohen outlines empowering every employee with hands-on access to frontier models and training. This bottom-up approach has yielded more powerful, bespoke solutions because they’re built by people who intimately understand client challenges, rather than strictly technical specialists.

2. The Power of Curated Knowledge Libraries Building digitized libraries of proven case studies and best practices that feed AI agents creates more relevant, accurate outputs than relying on open internet training data. For crisis simulations and campaign work, this approach delivers precision over generic AI-generated content.

3. Keeping Humans in the Driver’s Seat Cohen emphasizes that human creativity remains paramount. AI works best as a talented assistant—helping test, refine, and optimize human ideas, not replacing them. The result: less “AI slop,” more polished, high-impact work.

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FleishmanHillard Wins 2026 Innovation Awards for Data-Driven Strategy

January 12, 2026

FleishmanHillard has won two North America 2026 SABRE Awards: Data-Driven Agency of the Year for “Democratizing Data” and Data Professional of the Year for Ines Schumacher and SAGE Synthetic Audiences.

SAGE Synthetic Audiences, built on Omnicom’s industry-leading data stack and FleishmanHillard’s audience profiling expertise, was officially introduced last spring.

The wins underscore FleishmanHillard’s operational mindset of embedding intelligence and analytics at the center of communications strategy.

The recognition follows last fall’s news of 13 AMEC Measurement and Evaluation Awards including seven Gold, four Silver and two Bronze across FleishmanHillard TRUE Global Intelligence, Methods+Mastery and Omnicom Public Relations. Those accolades included Innovation Award for New Measurement Methodologies, Best Use of New Technology in Communications Measurement and Best Use of Measurement for a Single Event or Campaign.

The wins reflect what FleishmanHillard describes as an “integrated intelligence model,” where rigorous analysis and critical thinking are baked into strategy development and execution from the start rather than applying data after the fact. The news follows the rollout of the agency’s counselor-led AI solutions suite FH Fusion last summer.

The SABRE recognition validates the investments made in building proprietary methodologies, scaling analytics capabilities across regions and training advisors agency-wide to lead with insight.

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Sponsored Content in the AI Era

December 3, 2025
By Corina Quinn, Andrea Margolin and Amanda Hampton

The landscape may be shifting, but your brand story is still getting heard.

Sponsored content is arguably one of the most powerful tools in the PR toolbox for ensuring audiences hear a brand related story in an editorial format and context that will both resonate and drive impact. We’d be hard-pressed to think of a campaign that should not have this high-impact type of storytelling as a centerpiece.  

But how does it work in today’s AI-driven environment? 

Sponsored Content in the AI Era

This era of zero-click searches has led to lots of hand-wringing on what it means for sponsored articles and performance and whether it’s still a sound strategy for an integrated campaign.

Good news on that horizon: zero-click hasn’t marked the death to sponsored content. It remains a dynamic, stable solution to reach critical audiences with rich storytelling and trusted formats that offer unique support for a brand’s business goals.

New To SponCon?

Sponsored content is a form of paid media partnership in which brands collaborate with media publishers to co-create content that aligns with the editorial standards and audience interests of the publication, while advancing the brand’s communications objectives

Unlike traditional advertising, sponsored content is integrated within the editorial environment, delivering insights, perspectives or stories that resonate with readers and offer value beyond overt promotion. This approach leverages the credibility and reach of established media outlets, allowing brands to participate authentically in conversations that matter to their target audiences, while keeping control over message integration that you can’t guarantee with earned coverage from those same outlets.

Why Sponsored Content?

Trusted Influence at Scale
By appearing within reputable editorial environments, sponsored content benefits from the publisher’s authority and audience trust—and taps its editorial expertise in content creation. This enhances message credibility and drives deeper consideration among target audiences.

Strategic Storytelling
Sponsored content enables nuanced, narrative-driven communications that go beyond product features to communicate a range of things, from big-picture brand values, new and exciting offerings for consumers, and even thought leadership or societal impact. It supports reputation, positioning, and purpose-led initiatives as well as helps drive consumer offerings, from path to purchase and other lower-funnel tactics.

Precision Audience Engagement
Partnerships with publishers provide access to highly engaged audiences with a nuanced set of metrics you don’t get with earned media. This allows for tailored content that meets specific needs, interests, or pain points, improving relevance and engagement metrics—while driving brand business goals.

Integrated Communications Impact
As part of an integrated strategy, sponsored content amplifies earned and owned messaging, bridges gaps in the customer journey, and creates additional touchpoints that reinforce key themes across channels. It’s important to remember that sponsored content goes beyond print or digital advertorial, and includes qualitative content that also taps social media and other channels, and leverages video and audio content in addition to digital.

Measurement and Optimization
With robust analytics that can include time spent, click-through rates and more, sponsored content programs can be assessed for engagement, sentiment, and conversion—providing actionable insights to refine messaging and demonstrate business impact.

Sponsored Content in the Era of AI

There is a lot of discussion about declining site traffic due to changes in how people find information (known as zero-click search due to AI-driven features) and the truth is, there remain a lot of unanswered/developing questions about how AI summaries treat sponsored content that we will continue to track. We can also imagine a world where AI may enable more targeted distribution opportunities as media platforms respond and evolve.

Here’s what we know to be true today: Sponsored content remains effective because it is distributed and amplified through intentional, multi-channel strategies and is not just reliant on organic search or publisher homepage traffic.

There are several reasons why Sponsored Content remains effective:
Unique Traffic Drivers: In general, search and AI summaries aren’t the main traffic driver for sponsored content articles. Programs lean on newsletters, social media and paid amplification to drive qualified audiences.
Publishers Are Aware and Adapting: Many publishers moved to paywalls and subscriber-based ecosystems and have grown their cross-platform channel strategy in recent years to reduce their dependency on search, even before AI summaries emerged.
Quality Over Quantity: Our success metrics go beyond page views and impressions. We emphasize time spent, engagement and lower funnel actions such as clicks to the client’s site. Even with modest traffic results, high engagement audiences drive stronger qualitative impact.
Flexible Strategies: If we do see sponsored article performance start to be impacted, we collaborate with our media partners to evolve our strategies and content mix to make up for the loss in traffic.

Sponsored Content Goes Beyond Articles

In our current sponsored content programs, diversification is a deliberate part of our strategy to ensure we’re future-proofing programs against ecosystem shifts.
• Video, newsletter and social media integrations are important players in the content space and further distribute our reach and performance.
• Publisher-branded social handles, podcasts and more provide additional opportunities to connect with audiences even if article impressions or page views fluctuate.
• Diversifying our programs makes them less susceptible to zero-click trends because distribution is intentional and audience-driven rather than search-driven.

As the digital landscape continues to shift, we will monitor changes, adapt distribution and measurement strategies, and keep clients informed—ensuring that sponsored content continues to deliver value and impact in any environment.

From Left to Right: Corina Quinn, Andrea Margolin and Amanda Hampton

Corina Quinn is a Senior Vice President who co-leads our Media Partnerships Center of Excellence. A longtime award-winning editorial director and content strategist, she spent more than a decade in digital newsrooms at places including Conde Nast Traveler and Travel + Leisure.

Andrea Margolin is a senior communications strategist with more than 20 years of experience driving integrated storytelling, executive thought leadership and digital innovation for enterprise health brands. Based in Washington, DC, Andrea partners with global healthcare leaders to translate complex science into compelling, high-impact narratives that resonate across audiences and platforms.

Amanda Hampton is a Vice President based in Washington D.C. where she leads integrated sponsored content programs informed by audience insight and editorial excellence. She collaborates with clients and top consumer media partners to create high-impact storytelling that drives engagement and strengthens brand performance.

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Predictions for the Year Ahead: 3 Shifts in Internal and Change Comms in the Age of AI 

November 20, 2025
By Zack Kavanaugh

AI is reshaping how work gets done – and the field of communications is no exception. 

The fundamentals haven’t changed: people still need clarity, context and connection to make sense of change. What’s changing is how communication is created, delivered and received. 

Here are three shifts we expect to see in the year ahead – and what leaders can do now to prepare. 

Prediction 1: Content Will Keep Scaling. Attention Will Keep Shrinking. 

Stat to watch: 83% of knowledge workers say they are trapped in a communication maze of scattered emails and chats, where vital information often gets lost. 

What it means: Information overload is already a challenge, and AI-generated content is likely to add to the volume. The risk isn’t that employees won’t get enough information – it’s that they’ll disengage or find that messages actively obstruct their ability to focus on meaningful work.  

Communication teams must shift from output to impact, producing fewer, more intentional messages that protect attention and create value. 

How to prepare: Treat communication like scarce real estate. Ask: Is this message necessary? Who really needs it? Run small tests and trials, such as A/B tests, to see what captures attention, then scale your solution based on those insights. In a world of abundant content, relevance is what earns attention, provides value and builds trust. 

Prediction 2: Signals Will Get Louder. Understanding Will Stay Quiet. 

Stat to watch: 57% of employees say their company has a generative AI strategy in place, compared with 89% of executives who say it does. 

What it means: Leaders may assume their messages are landing simply because they were sent or because dashboards show clicks or activity. However, the gap between executives and employees in understanding AI strategy shows how misleading that assumption can be.  

How to prepare: Go beyond superficial metrics. Don’t just track clicks or usage. Use methods like focus groups, pulse surveys and informal conversations to assess true understanding. Ask: Do people understand the strategy? and Can they explain what it means for their role? Don’t settle for more data. Seek deeper, actionable insight that drives understanding and adoption. 

Prediction 3: Managers Will Carry More of the Message – and More of the Risk. 

Stat to watch: Only 27% of managers are engaged at work, and over half have never received formal training – including communication and people-leadership skills.  

What it means: As AI tools take on more of a team’s drafting and editing responsibilities, managers play an increasingly important role in the final stage of communication – ensuring messages are delivered in a way people understand and trust.  

Employees already look to them first for clarity, but many managers don’t feel equipped for the role. Without support, important messages risk being diluted or distorted – and organizational alignment can weaken. 

How to prepare: Support managers as the human bridge between the organization’s strategy and those who will implement it in their day-to-day responsibilities. Provide hands-on training to build confidence and give them opportunities to practice effective communications.  

In an AI-driven workplace, managers need more than digital tools. They need targeted coaching and ongoing, real-time support to communicate change. 

What AI Can’t Replace 

These shifts point to a future where AI does more of the producing, but people remain responsible for the meaning. 

More than ever, communication won’t just be about what gets said. It will be about what gets understood, internalized and acted on. And as machine-made content becomes more common, the messages employees will trust most are the ones that feel human. 

The role of the Internal Communications function is evolving – not to create more, but to help organizations make sense of more.  

Leaders who plan for this now will be better equipped to earn attention, maintain alignment and guide their people through the changes AI is accelerating. 

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Understanding the GLP-1 Consumer: Pairing AI and Consumer Behavior Research to Map Potential Impact on Food, Nutrition and Innovation 

October 29, 2025
By Allison Koch

Obesity medications have created a new type of consumer with unique needs. These consumers are not only spending their money differently but also spending less on groceries while still figuring out how to integrate their new diet into their homes and social lives.  

Food companies, as well as health professionals and dietitians like me, are seeking to better understand the GLP-1 user and how best to support them, especially as the medications become more affordable and accessible.  

Consumer research is already showing us where there are opportunities to support GLP-1 users. For example: 

GLP-1 users are tech-savvy, diverse and often rely on online communities – underscoring a shift in how Americans get health advice.

Moving beyond the numbers with AI 

But how do we really get behind the statistics and inside the mind of a GLP-1 user?  

We created a synthetic audience—an AI-driven amalgamation of many users based on all of the research we could put into the tool—to explore their thoughts and use them as a springboard for discussion and inspiration. Our proprietary tool unveiled potentially unintended consequences medication users’ decisions may have, including how their dietary habits and behaviors could influence how and what their family eats. More broadly, their habits and decisions will drive how product innovation happens and how the food supply chain is impacted.

And our synthetic audience showed us clearly that:  

  1. One size fits none: the most effective engagement – whether clinical or product – starts with understanding and targeting micro-segments.  
  1. Rethink education with reach: health care professionals (HCPs) – preferably led by registered dietitians (RDNs) who are experts in connecting the food and healthcare sectors – as well as the broader healthcare and food industries need to embed in GLP-1 users’ ecosystems as most build health knowledge outside traditional channels (on YouTube, Reddit, TikTok and with peer groups). 
  1. Anticipate ripple effects: HCPs (and the industry where appropriate) need to help patients navigate this cascade with empathy, flexibility and real-world solutions beyond just nutrition effects.  

What industry leaders are saying 

With these insights in hand, earlier this month I challenged three industry professionals to apply our findings to their work in front of a crowded room at the recent Academy of Nutrition and Dietetics annual Food and Nutrition Conference & Expo (FNCE). Each panelist brought a unique perspective to the table, discussing how they work with and reach GLP-1 medication users as well as key considerations and implications for practice and the broader healthcare, food and beverage community. 

How far does the GLP-1 impact reach? My colleague and Audience Strategy and Data Innovation expert Amanda Patterson said, “The rise in GLP-1 medications is fundamentally reshaping not just how people eat, but what and how much they buy at the grocery store. Beyond the individual, these changes ripple out to families and social circles. Many users say their household food routines (grocery lists, meal prep, holiday or social meals) are being reworked to accommodate their new eating patterns.” 

How should the food industry respond? For long term implications if this trend continues, community nutrition dietitian and GLP-1 user Summer Kessel shared, “I’m hopeful we are course correcting from the days of massive portion sizes and novelty products over nutrition. However, I’m a little worried that if people rely too heavily on ‘low-calorie’ processed foods instead of balanced meals, they risk missing out on essential nutrients.” 

Can the right nutrition messages get through the marketing hype? Founder of the Better Nutrition Program and RDN Ashley Koff shared, “We can use awareness of GLP-1 medications to introduce the public to weight-health hormones and how they regulate numerous functions in the body known collectively as ‘weight health.’ In doing this, dietitians can expand the reach of GLP-1, GIP beyond medications and help people learn to assess and as indicated, optimize their own hormones – whether they ever use a medication or not.” 

Rethinking food and health communications 

As GLP-1s continue to change daily routines and expectations, helping consumers make the right decisions to stay healthy but also being present with family and friends at meals and other food-based activities will test how we communicate about food and health.  

Combining insights from AI, research and lived experience allows us to reach solutions faster and understand not just what works, but why.  

For more information on these insights and other key learnings from FNCE, contact Allison at [email protected]

Allison koch width= Allison Koch MS, RD, CSSD, LDN is a vice president in FleishmanHillard’s Chicago office, where she provides nutrition communications counsel for clients. A registered dietitian with more than 20 years of experience, she’s passionate about helping brands connect science and storytelling to inspire healthier choices and stronger consumer trust.