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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

Why Trust is the Real Competitive Advantage in Ag Tech  

April 2, 2026
By Vanessa Sapino, Kristin Hollins and Shelly Kessen

At this year’s World Agri-Tech Summit in San Francisco, several key insights cut through all the AI, robotics and data ecosystems discussions with one clear stand out: Trust and relationships form the foundation of successful technology adoption and meaningful connections in agriculture.

We took away from the conversations that the future of successful ag tech isn’t built in boardrooms. It starts at the farm level, with credible voices, practical solutions and farmers who see themselves as partners in innovation. It’s shaped by people who deliver not only technology, but who understand the market, the mission and the opportunity for real change.

As a proud co-sponsor with Western Growers within the California Delegation of the Ag Tech Alliance, FleishmanHillard was on the ground hearing directly from farmers, food leaders, agribusinesses and tech innovators, along with global policy, industry and academic leaders about what’s working and what’s not.

What we heard repeatedly was striking. While innovation and disruption drive today’s ag tech conversation, farmers still rely most heavily on word of mouth, recommendations from trusted advisors, and partnerships built over years. From the tech company perspective, the conversation centered on differentiation and how to stand out in a crowded market while competing for limited investment and customer attention.

This creates a fundamental challenge: While ag tech companies seek to differentiate in an oversaturated market, farmers seek clarity amid piecemeal options. As one farmer panel pointed out, there is no “Good Housekeeping seal of approval” for ag tech. Farmers face a bewildering array of options, each claiming to solve different pieces of the puzzle. The result? Adoption stalls.

Farmers need holistic solutions that work immediately, reliably, practically, and profitably. That demand for certainty is where trust becomes currency. Without a credible source vouching for a solution, many farmers find themselves in analysis paralysis. But trust shortens the decision cycle. When a farmer trusts a source, they can move faster.

Relationships Are Infrastructure

In agriculture, relationships aren’t soft. They’re structural. A trusted agronomist, equipment dealer or financial advisory team becomes part of operational infrastructure because that person understands the farm’s specific challenges, geographic weather patterns, soil conditions, financial constraints and business goals.

New technology that arrives without relationship context is just noise. Conversely, technology that arrives with a trusted recommendation becomes an asset.

To keep that infrastructure intact, farmers, food companies, agribusinesses and investors across every panel kept emphasizing the same characteristics for technology that actually gets adopted: practical, reliable, immediate ROI, user-friendly, easy to operate, and easy to service. The key takeaway: functional innovation earns credibility.

The Communications Parallel: Moving Forward

The same principle that governs farmer tech adoption also governs communications strategy. Just as farmers need advisors and relationships from day one, organizations across every industry — from scrappy startups to established enterprises — need a trusted communications partner embedded in their growth journey from the beginning to help craft their narrative.

When an organization partners with a communications advisor from day one rather than after launch or when they need crisis response, something powerful can happen. As the ag tech ecosystem faces a challenging commercialization gap, the answer isn’t just deeper partnership with farmers. It’s recognizing that breakthrough ideas only scale when translated into stories that farmers, investors and the entire market can understand, believe in and ultimately adopt. That translation work happens early, or it doesn’t happen at all.

That’s how you build understanding and credibility. That’s how you scale. And in agriculture — as in communications — trust is everything.

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

Get the Report: Inside China’s 2026 Two Sessions

March 24, 2026

China just locked in its economic roadmap for the next four years with a 4.5–5% growth target. Here’s what matters: The 2026 Two Sessions formally endorse a pivot toward innovation-driven growth, economic resilience and calibrated openness that reshapes how global companies operate, partner and communicate across markets.

Our latest analysis cuts through the noise to explain what actually matters for your organization in 2026. Based on observation and conversations with leaders across sectors and regions, it examines the strategic context, the trade-offs China is managing and what corporate communications professionals need to know to navigate influence and opportunity in this environment.

Article

The Patient Engagement Gap Your Competitors Are Closing

February 26, 2026
By Barry Sudbeck

Here’s a question more pharma executives are asking: Does patient engagement move the needle, or is it just good optics?

It’s a fair question.

In an era where pharmaceutical innovation must prove its value not only through clinical efficacy but also through demonstrated patient relevance, the question is no longer ‘whether’ to engage patients—it’s whether that engagement translates into an advantage.

New research from FleishmanHillard’s Global Health & Life Sciences group found it might. Released in recognition of Rare Disease Day, The Patient Engagement Premium: Defining the Strategic Value of Patient Input in Drug Development examines FDA submissions for rare disease therapies approved between 2018 and 2024 and finds directional associations between documented patient input and regulatory outcomes.

From Philosophy to Evidence

The shift from transactional patient engagement to embedded patient evidence isn’t new thinking though, but it is accelerating practice. And as regulatory scrutiny of traditional DTC channels intensifies and Health Technology Assessment bodies increasingly consult patient advocacy organizations, companies face a choice: embed patient evidence directly into development processes, or risk losing ground to those who do.

But let’s be honest, executive decision-makers demand more than anecdote. This research represents a crucial step toward establishing a measurable evidence base for patient engagement as a strategic investment, not just a values statement.

A Rigorous Approach to a Complex Question

The analysis examined 179 rare disease drug approvals that included Patient Experience Data (PED) tables, a requirement formalized following the 21st Century Cures Act. Each product was assigned a ‘Patient Engagement Score’ based on six distinct engagement activities, from patient advisory committee insights to patient-reported outcomes (PROs) and clinical outcome assessments (COAs).

Here’s what we found:

  • Patient input is increasingly embedded in regulatory submissions. Nearly nine in ten submissions in 2023-2024 explicitly cited at least one patient engagement activity, up markedly from earlier in the study period. PRO and COA data have become the most common form of patient input, signaling that companies may be integrating patient insights systematically and earlier in development.
  • Higher engagement scores trended with patient-centered labeling. Products with label claims tied to patient input averaged 1.4 documented engagement categories versus 1.0 for those without, a modest but directional association that could confer commercial advantage.
  • Company size isn’t a barrier. Mid-cap sponsors engaged in patient-centered activities nearly as frequently as large pharmaceutical companies. Translation? The potential benefits of patient engagement appear accessible across the competitive landscape.

What Happens to Companies That Don’t Move?

Let’s be clear: the evidence base is still developing, and these associations are directional rather than conclusive. But the implications are hard to ignore.

Patient engagement is evolving from ethical consideration to strategic necessity. Companies are prioritizing structured, quantifiable patient data, particularly PROs and COAs, for FDA submissions. Yet many underutilize other pathways, including patient organization partnerships and patient preference studies. That suggests that comprehensive investment in the full spectrum of patient evidence could be an untapped competitive edge.

For smaller companies not yet systematically integrating patient perspectives, the takeaway is encouraging, structured engagement may level the playing field. For larger companies that under-invest in patient input, the risk is equally clear, patient-centered rivals may be building advantages that compound over time.

Looking Ahead

As the evidence base expands and sponsors document patient engagement more comprehensively, clearer patterns will likely emerge. But the direction of travel is already obvious: regulators, payers, and patients themselves are reshaping how innovation is valued. Companies that embed patient engagement as foundational, not peripheral, will compound advantage across regulatory, payer and reputation landscapes. The infrastructure to do it exists. The question is in the execution.

Our approach combines regulatory expertise with data science and AI tools to help clients operationalize patient input across the product lifecycle, ensuring innovation is positioned as both evidence-driven and human-centered.

The pharmaceutical industry is at an inflection point. The companies that treat patient engagement as foundational—not peripheral—will define what comes next.

To access the full report or discuss how strategic patient engagement can create value for your organization, visit fleishmanhillard.com or contact Barry Sudbeck and Laura Musgrave, Patient Engagement Specialists with FleishmanHillard’s Global Health & Life Sciences group.

Click the image to download our Global Health & Life Sciences patient engagement analysis

Article

From the Super Bowl to the World Stage: What Health’s Cultural Moment Means Now

February 11, 2026
By Jacob Porpossian

Super Bowl LX offered more than a strong showing for health and pharma brands. It signaled something more durable: health is no longer a category that waits for cultural permission. It’s actively shaping culture on the world’s biggest stages. That shift matters.

Roughly seven major health and pharma campaigns aired during the big game. But what stood out wasn’t volume. It was confidence. These brands claimed space alongside beer, cars, and tech.

More importantly, this moment shouldn’t be viewed in isolation. As we look ahead to the Olympics and the World Cup, we’re entering an extraordinary run of global tentpole moments where health, science, performance, access, and equity will increasingly intersect with culture at scale. Super Bowl LX was an early proof point.

What the strongest work got right

Across categories and geographies, the most effective campaigns shared three defining moves.

1. Stigma reduction through entertainment
Novartis used NFL tight ends to make prostate screening feel approachable, even funny, rather than fearful. Boehringer Ingelheim reframed early-detection testing as a “mission,” turning anxiety into agency. Humor and storytelling didn’t trivialize health; they unlocked attention, relatability, and permission.

2. Normalizing everyday health decisions
GLP-1 related ads put the focus on being human and positioned their treatment options as support and empowerment for patients, not intervention. These brands met people where they are. Not where the healthcare system wishes they were.

3. Cultural clarity
Simple metaphors. Human voices. Ideas that survived the post-game social conversation and shaped Monday-morning dialogue. The work that traveled didn’t over-explain science; it translated it.

The bigger signal

Industry and media reaction underscored a structural shift playing out globally: health is no longer purely clinical. It’s lifestyle-adjacent, values-driven, and culturally expressive. The brands that resonated weren’t just marketing products. They functioned as cultural facilitators, translating science into relevance, credibility, and permission. For trust-based, highly regulated categories, this is the difference between building confidence and eroding it.

Why this matters now

As health brands look ahead to the world’s next major cultural moments, the opportunity (and responsibility) is clear.

Cultural strategy and creative as core health capabilities
Brands need to enter culture without trivializing health, balancing regulatory rigor with entertainment and emotion. This requires earned-first thinking that travels across markets and moments.

Integrated moment marketing
Impact now lives across broadcast, social, earned media, influencers, and executive voice working together. The most effective programs build always-on platforms that culminate in tentpole moments, rather than relying on one-off activations.

Prevention and behavior-change storytelling
As healthcare moves upstream toward screening, early detection, and access, brands must reduce fear and inertia. That demands new creative frameworks that motivate action without alarm.

Corporate narrative alignment
Many of these campaigns carried implicit corporate messages around innovation, access, and equity. As scrutiny around healthcare ethics and pricing intensifies, alignment between brand, corporate, and leadership narratives becomes essential; not optional.

Looking ahead

Health is moving faster than many organizations are prepared to follow. The brands that succeed will be those that show up credibly, responsibly, and creatively. Not just during one event, but consistently across the world’s biggest cultural stages.

With the next Super Bowl less than a year away and the Olympics and World Cup on the horizon the window to plan thoughtfully is already open.

Jacob Popossian width= Jacob Porpossian is the Global Executive Creative Director for FleishmanHillard’s Health & Life Sciences practice, where he builds and leads creative and storytelling capabilities for major health brands. With a background spanning creative strategy, digital marketing, communications, and production, he advises integrated teams across healthcare, CPG, corporate, and technology sectors, while also championing diversity and inclusion initiatives across the agency and industry.

 
Article

Sustaining AI Adoption on Your Team: Moving from Launch to Long-Haul Momentum 

December 19, 2025
By Zack Kavanaugh

Your organization launched the tools. Ran the trainings. Clarified the policies. Maybe even branded your AI initiative to rally employees and excite stakeholders.  

Now what? 

Three Brutal Truths About AI Adoption 

  1. For many organizations, AI remains more of a talking point than a true driver of change in daily work, employee experience or customer service. 
  1. With a thoughtful, risk-aware approach, adoption may not be straightforward or fast
  1. Employees will always be at different stages – some experimenting, some integrating AI into workflows, some skeptical or uncertain, and many shifting between these states as priorities and information evolve. 

Your Role as a Leader 

That’s where leaders – C-suite members, team leads and managers alike – come in. With AI adoption – a business transformation that carries emotional baggage, operational challenges and even existential questions – leaders have a responsibility to guide their people through the hype and toward something practical that drives business value. 

What You Should Get from This Article 

This piece closes out our 2025 series on AI adoption. The first article mapped out readiness across culture, leadership, knowledge and infrastructure. The second examined why adoption stalls, unpacking hesitations at the enterprise, team and individual levels. The third highlighted risks when communication and leadership lag behind technology. 

Those pieces focused on the big picture and the organizational must-haves. This one assumes those foundations are in place. It gets more tactical – outlining what leaders can do with their teams to move from launch to long-haul momentum. 

Ultimately, sustaining adoption comes down to three things: reinforcement, relevance and reflection. 

1. Reinforcement: Make AI Part of Everyday Routines 

After rollout, leaders must embed AI into daily routines, not treat it as a one-off initiative.  

Practical ways leaders can reinforce AI: 

  • Build in five minutes during team meetings for questions, concerns and hesitations related to AI use. Consider launching a dedicated channel, email thread or chat on your company’s collaboration platform so team members can share resources and ideas in real time. Funnel what you hear to the cross-functional team responsible for driving adoption. 
  • Identify and empower an AI champion – ideally, someone curious, willing to advocate and experiment, and who is influential on the team. Position this role as a professional development opportunity.  
  • Integrate AI into performance conversations and onboarding so it’s part of every team member’s role, not an optional add-on. Encourage people to rethink their work – and how that work gets done – in ways that push your team’s objectives forward. 

If reinforcement isn’t visible in everyday conversations, adoption will stall. Leaders should pay attention to whether AI is being treated as optional – and redirect if it’s not yet treated as an expectation. 

2. Relevance: Tie AI Directly to the Work People Do 

Adoption won’t stick if AI feels abstract or disconnected. It has to feel useful in the context of actual work. 

Practical ways leaders can make AI relevant: 

  • Share your own AI examples regularly – where it saved time, where it added value and, equally importantly, where it didn’t and why. Use existing channels – chat, email, 1:1s with direct reports and team meetings – to socialize your learnings. 
  • Engage the team in solving challenges and capitalizing on opportunities together. For example, run bi-weekly brainstorming sessions where team members bring problems and explore whether AI can help address them. 
  • Recognize small wins so adoption feels attainable – and do the same with failures so the team can learn from what didn’t work. Spotlight and reward team members who solve customer challenges, improve processes or identify new use cases. 

Relevance ensures employees see AI as a tool for them – not just for the company. Leaders should surface challenges, encourage collaboration and keep examples concrete and tied to team goals. 

3. Reflection: Measure What Actually Matters 

Tracking logins shows activity – but not necessarily maturity. Leaders need to move beyond superficial usage metrics and measure whether adoption is building confidence, capability and alignment with business objectives. 

Practical ways leaders can reflect on adoption: 

  • Run short (potentially anonymous) monthly pulse surveys with two or three questions that gauge clarity of your company’s AI strategy, how it connects to employees’ work, and confidence in using the tools to solve business problems. Include at least one open-ended question for crowd-sourced ideas and opportunities. 
  • Work with your AI champion to surface issues employees may hesitate to raise directly with you. Encourage them to set weekly office hours or meet 1:1 with team members to collect insights, and report back to you. 
  • Check often whether AI efforts are aligned with team objectives. If your priority is expanding your customer base, do you have the use cases to support it – or are you drifting into experimentation that doesn’t advance your goals? Consider setting time with your AI champion each month to reflect on whether you’re driving the value you set out to. 

Reflection helps separate meaningful progress from surface activity. Pairing usage data with comprehension metrics gives leaders a sharper view of where adoption stands and where support is most needed. 

The Final Test: Is Your Team Living It? 

At the start of this series, we asked what readiness looked like at the organizational level. Now the question is more immediate: Is your team living it? 

Use this scorecard to check your progress: 

This isn’t a one-time exercise. Revisit it monthly – and at a minimum, quarterly. Consider having your AI champion fill it out too, to guard against blind spots.

The Bottom Line

The biggest challenge of AI transformation in 2026 isn’t speed – it’s staying power. The organizations and teams that succeed will be the ones that take the actions above now and treat adoption as an ongoing process, not a one-time push.

Article

When the World Gets Noisy, Great Storytelling Breaks Through 

December 3, 2025
By Trine Hindklev

In a world where disruption feels like the norm, clarity can feel out of reach. Our President and CEO, J.J. Carter, put it well at a recent PR Decoded discussion: “Clarity often comes from chaos for those who are bold enough to seek it.” That’s the mindset driving how we approach communications today. For us, it’s our call to action for how we think about storytelling. 

That perspective came through in our recent discussion with Chrissy Farr, editor-in-chief of Second Opinion, former tech and health reporter at CNBC and author of “The Storyteller’s Advantage: How Powerful Narratives Make Businesses Thrive.” Chrissy’s research and real-world experience show that the most effective leaders strategically use compelling narratives as a springboard to craft stories that connect, persuade and inspire.  

When storytelling is left to chance, companies lose control of the narrative. And no one wants to lose their narrative. Today, there’s a genuine need for trusted counselors to help leaders put communications at the center of their business strategy, not as a last-minute fix or a siloed function cast off to the side. 

Complexity isn’t going away. It’ll likely to get, well… more complex, but the ability to cut through it with authentic, sharp, well-crafted narratives is the key to thriving. Here’s how: 

Building resilience through narrative 

Organizations that invest in their narrative build real equity. It’s more than a safety net for tough times, Chrissy said. It’s a foundation of trust, engagement, and clarity that holds up in any environment. Leaders who go beyond facts and figures, sharing vivid, relevant and relatable stories, build solid reputations and relationships that last.  

The lesson? Invest in your narrative before you need it.  

That translates to mapping out your narrative assets, finding gaps in credibility, and creating processes for authentic engagement and rapid response. Resilience isn’t just about weathering storms; it’s about being known and trusted, no matter what comes your way. 

Moving beyond spin with courage  

Let’s be honest, being clear and human isn’t easy. It feels too risky. Too vulnerable. But the bigger risk is being boring and forgettable. People crave something real. Something bold and relatable. The risk of irrelevance is greater than the risk of saying something fresh.  

This holds true whether you’re B2B or B2C. Broad, generic messaging just gets lost. Clarity and specificity cut through. Every time. That’s why it’s so important to help leaders tap into their authentic voice, not just the safe, polished version, Chrissy shared. There’s a need to coach leaders to lean into what makes them different and set up the right guardrails to navigate complex issues with confidence. This isn’t about spin. It’s about showing up as real humans and letting that drive real connection.  

Reach without the reaching 

Metrics, dashboards and percentages dominate most conversations at the top. But honestly, who remembers a stat sheet? 

It’s stories that stick. Share the right story, and suddenly your message is making the rounds in rooms you’ve never entered, and your message travels further than any paid campaign could. That’s the kind of reach every communicator dreams of. 

But too often organizations chase impressions and views, Chrissy said. Impressions don’t impress if they don’t move people, and most don’t. The real impact of storytelling shows in influence: your message shapes industry conversations, earns trust and opens doors that numbers alone never could.  

In a nutshell, it’s time to move beyond those vanity metrics. So, look for measurement models that track the full picture, from traditional reach to narrative traction and influence mapping. Build dashboards that look at what counts, such as stakeholder sentiment, leadership invitations and the conversations you’re sparking across your sector. 

Chrissy’s advice is simple: focus on quality engagement. Nurture the audiences that matter. Invest in content people value. Rethink channel strategy, prioritizing depth over breadth and building real communities.  

Move away from chasing numbers to building lasting influence, think more modular content + smart thought leadership + executive visibility all working together as part of an integrated approach.  

Be a trusted partner  

Storytelling carries risks. But so does playing it safe. As Chrissy and J.J. put it, leaders and brands who stand out are the ones willing to show up with clarity and courage, sharing the moments that matter even when they aren’t perfect. That’s how impact grows. 

As a modern communications agency and trusted partners to many of the world’s most vibrant brands, we believe crafting a narrative isn’t just a tactic, but a strategic asset that drives measurable impact. The organizations that treat storytelling as a line item or an afterthought will get left behind. The ones who invest, with purpose, will lead. 

Do you need help being human, specific and bold? Be brave. Let’s talk.  

Trine HindklevTrine Hindklev is a senior partner and FleishmanHillard’s Global Strategic Media Relations Lead. She is part cultural anthropologist, part media strategist, part creative storyteller and all-in change-maker..