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Article

Black Hat 2026: A Conference and Industry Transformed

August 10, 2026
By Scott Radcliffe
Black Hat 2026

I walked into Black Hat this year with a simple observation that kept getting reinforced the more I saw and heard: this isn’t your father’s Black Hat anymore.

There was a time when Black Hat was the hacker conference, the kind of place where attendees would famously hijack hotel WiFi and billing systems just to prove they could. It was edgy, even a bit gleefully irreverent. That energy hasn’t disappeared entirely, but it has definitely evolved.

I asked a number of people at the conference, from CISOs to others in communications, if they felt the following sentiment seemed true to them. That RSA has become its own thing; DefCon has become the old Black Hat and Black Hat has become the old RSA. As if to reinforce the point, a CEO of a mid-sized security company told me at his company’s large and well-staffed Black Hat booth he doesn’t even reserve booth space at RSA anymore because his customers, CISOs, system admins, directors of security and so on, are no longer there. They are, however, at Black Hat. Though many have great feelings of nostalgia for the old Black Hat, it is now undeniably a security conference for business and research. Still technically focused, but with a fundamentally different vibe and evolving focus.

The scale of the transformation became impossible to ignore when I saw there was an entire healthcare track at this year’s conference. To me, it tells you everything about how the conference has matured and professionalized. What was once a place where the hacker subculture would convene is now a sprawling conference center addressing security challenges across entire industries. With that growth comes responsibility, but also opportunity.

The Tone in the Security Community Has Shifted Ever so Slightly and It’s Turning Optimistic

In terms of substance, the tone of talks, general discourse and even solutions and products offered on the floor shifted a bit in the months since RSA, particularly around AI security.

At RSA, which many may remember occurred just before the limited release of Anthropic’s Mythos model, people were overwhelmingly sounding the alarm about AI’s offensive cyber capabilities. That discussion certainly didn’t disappear, but it’s no longer dominating the discourse around security or even security in AI. The most common question asked at the conference was generally, “How do we secure the AI models running in our own environments?” Axios’s Sam Sabin covered this very effectively in her overview of the conference as well. But even that generally was solutions-focused and underscored the shift. What I saw and heard at Black Hat was something slightly more optimistic: tangible solutions and exciting innovations for defenders.

This isn’t just a tonal shift; it reflects real progress. The best minds in security are now effectively chipping away at a very real and present problem presented by the quickly growing and evolving threat environment that has only accelerated thanks in large part to AI. The message we heard a few months ago was “defenders won’t be able to catch up for a couple of years” and has now begun to turn to “here’s what we’ve come up with to map and mitigate your vulnerabilities.”

Innovation at Scale: From Companies Large and Particularly Small

As you may expect, though some of this progress came from established companies in the space, the most exciting developments came from startups. A CISO I spoke with underscored this point in how he approaches building his environment. He said he leaves “the large muscle movements” like endpoint detection and response (EDR) to the big companies and relies on startups and smaller firms for innovation. His reasoning seemed pretty sound to me as I worked my way through the conference hall, sat through some talks and attended some events.

The runway for innovation is wide and long, especially today. You’ve got sustained growth in the security market overall, a universally recognized threat environment that’s expanding everywhere that touches the internet and now the ability to rapidly develop and deploy new tools using AI. This has all established a dynamic where smaller, nimble companies can move fast enough to actually solve critical problems, often in incremental but important ways.

I watched this play out across multiple startup competitions. The winners weren’t necessarily the ones with the most complex solutions. They were the companies that could clearly and efficiently articulate the problem they were solving and how they were solving it.

Story Matters. A Lot.

At these competitions it was obvious that a clear and simple solution set resonated with the intended audience. It was also evident that a focused and concise story told about those products won out as well. In a space defined by technical complexity, nuance and countless interdependencies, clear storytelling wins.

Yes, the technical details matter. Yes, the nuance is critical. But people, decision-makers, investors and security teams, respond to a clear, well-told story about what you’re trying to accomplish. The most successful startups at Black Hat weren’t necessarily the most technically sophisticated. They were the ones who could translate their innovation into a human narrative.

The author mingles at Black Hat 2026.

Don’t Forget the Fundamentals—And That’s Where Communications Lives

Even as AI turbocharges everything, security experts seemed united on one point they seem eager to tell everyone who will listen: get the fundamentals of security right. An often-forgotten element of that common piece of advice is that executing the fundamentals of cybersecurity isn’t purely a technical problem.

Consider what’s emerging as a real AI security risk: credential theft. Certainly not a new concept for security, but it impacts threats that result in everything from the compromise of a business email account to the unauthorized use of internal models from individuals outside your organization. Threats like this and the business use of unsanctioned AI models aren’t firewall problems. They’re employee behavior and awareness problems. They’re exactly the kind of issues where communications can directly contribute to security outcomes.

Making sure your organization is following security policies, engaging employees on best practices and maintaining data governance, these aren’t just IT concerns anymore. They’re enterprise-wide challenges that require a coordinated communications and engagement campaign.

Black Hat itself this year felt like a conference, and a conference serving an industry, finding an important stage in its evolution in response to genuine disruption. There’s real optimism about what’s possible when you combine human expertise with AI tools, when you focus on innovation in the margins where smaller companies thrive and when you remember that beneath all the sophistication, security success still depends on clear thinking and clear communication.

Scott Radcliffe width= 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

The AI Readiness Gap Series: How to Build a Culture of Experimentation 

June 22, 2026
By Zack Kavanaugh

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

In the first two pieces, I argued that AI adoption is fundamentally a people challenge – not a technology one. And I walked through why normalization – that initial work of building psychological safety and making AI feel less like a mandate from above – is where adoption begins.

But normalization is foundational, not sufficient for scaling and sustaining a transformation.

Listening builds trust, transparency creates permission and leaders modeling curiosity make space for people to be curious too.

Yet at a certain point, readiness must turn into action, and trust must move into experimentation – and that’s where things often stall for organizations.

What experimentation entails

Experimentation is not a single workshop or training session where people learn AI in theory and hope to apply it later.

Experimentation is the deliberate work of creating low-stakes opportunities for people to try AI with daily tasks, see what happens, share what they learn and let those discoveries shape how their team and the broader organization evolves.

Why experimentation stalls

Many organizations are making the mistake of treating experimentation like compliance.

They may schedule training, mandate it or expect it to happen in a structured, predictable way. Then they’re surprised when adoption remains stuck at the edges – concentrated among early adopters while the rest of the organization watches, wondering why this whole “AI thing” isn’t for them.

What tends to move people from curiosity to confidence is relevance. When people see how AI fits into their work, adoption stops feeling like a mandate being forced upon them and starts feeling like a tool they’re better off with than without.

Who influences experimentation

Experimentation doesn’t happen without active support – and managers’ involvement is often what determines whether it takes root or trails off.

  • Microsoft’s 2026 Work Trend Index found that when managers visibly use AI themselves – not just endorse it – employees report a 17-point lift in AI value, a 22-point lift in critical thinking and a 30-point lift in trust in AI tools.  

Manager visibility and support are the prerequisites for experimentation to become embedded in how work gets done.

And beyond that non-negotiable support, companies can deploy three strategies to accelerate experimentation:

1. Design low-stakes opportunities to try, not mandatory programs.

The difference between training and experimentation is permission to fail.

Experiments are designed to surface discovery. The expectation is learning – which includes failure. The point is to uncover insights that shape what comes next, not to prove mastery.

This changes how people engage. Instead of one-size-fits-all training, create role-specific challenges.

These don’t need to follow a single format.

  • Some organizations run week-long sprints where teams tackle a specific workflow problem with AI.  
  • Others build 15-minute “AI challenges” into team meetings – quick, low-pressure moments where teams tackle something together and debrief in real time.  
  • At FleishmanHillard, we’ve deployed “try this” email campaigns that highlight role-specific tips and best practices – paired with reinforcement in team meetings – and structured, cross-functional hackathons and competitions where groups solve real workflow problems with AI.  

Format matters, but less than the regularity with which you encourage and provide opportunities for your people to try something with their work, see what happens and reflect on it – moments where stakes stay low, the learning gets documented and momentum builds as people see peers discovering things that work.

2. Spread learning through peer voices and stories, not polished case studies.

Your AI wins will get turned into case studies – charts, metrics and messaging locked in to prove ROI. While these matter for leadership dashboards, they often read less like something a peer figured out and more like something the company or an expert achieved.

Peer stories work differently. They come from someone familiar and in a similar position. They’re messier, they show what someone was really thinking when they tried something and they make room for context – “Here’s where I am, here’s what I tried, here’s what happened and here’s what I’d do differently.”

That messiness is what makes them powerful. It signals that perfection isn’t the bar – and if someone who thinks like you and works like you figured something out – suddenly that same experiment feels possible for you too.

Those stories create permission in ways polished case studies never do – which is why leaders should find ways to share these.

This could take several forms.

  • Create internal campaigns where teams share what they tried that week and what they learned – misfires included – via Slack threads or Teams channels.  
  • Host show-and-tell sessions where someone walks through how they solved a problem, where they got stuck, what went wrong – and invites the room to help troubleshoot next steps.  
  • Or establish dedicated architect and ambassador roles – like we’ve done at FleishmanHillard – where builders and super users experiment alongside their teams, share what’s working and what isn’t, and create permission for others to do the same. 

And with peer stories, tone is everything. “I tried this and it didn’t work, but here’s what I learned” is infinitely more relatable than, “Here’s how our company is transforming productivity and driving efficiency.” One invites personal experimentation – and the other signals compliance.

3. Build informal peer-to-peer momentum instead of formal training.

Training is periodic and linear. Peer-to-peer learning is fluid, constant and evolves as your organization does.

Both are valuable, and you should deploy each as needed, but peer-to-peer learning builds adaptive capacity – the kind that compounds and grows alongside your culture.

When you create simple mechanisms for ongoing peer-to-peer learning – “What I learned this week” rituals in team meetings, Teams threads where people drop quick tips, debrief huddles where someone walks through how they applied AI to a real use case, side conversations where a peer shares a shortcut – learning stops being something that happens to people and starts being something they do together.

At FleishmanHillard, we’ve made this easier by building off-the-shelf training resources and templates that any role can use or adapt for their teams – simple scaffolding that removes friction and makes peer sharing more accessible.

We’ve witnessed firsthand that those moments compound, and they reshape how your organization thinks about discovery and experimentation.

They also serve as a continuous feedback loop. You may learn more about what people care about, what confuses them and what would help them in a few weeks of informal conversations than from your annual survey.

On top of all that, assuming you’re following through on what you hear, your people will feel like their voices shaped what comes next – because they did.

How you know experimentation is becoming part of your culture

Experimentation is a continuous, messy, non-linear process – not a single moment. Here are three signals you’re heading in the right direction:

  • AI is being applied to everyday work. Analytics and team check-ins show employees testing AI with real tasks.  
  • Learnings are being shared. Examples, wins and failures surface in meetings and peer showcases. The conversation has shifted from, “how do I use this?” to, “here’s what I tried and here’s what I learned.”  
  • Confidence is building. The tone in surveys shifts from, “I’m not sure where to start” to, “I’m figuring out where this could help.” People are getting more and more curious and taking small, concentrated risks because they feel safe doing so. 

Building the cultural conditions for experimentation

Right now, organizational culture is roughly twice as powerful as individual mindset in determining whether AI delivers value.

The organizations accelerating adoption are the ones making room for people to learn and figure out what this technology means for their work – where people feel safe trying them, failure is a learning opportunity, peer discoveries shape strategy and use becomes personal enough to stick.

So, the question for leaders right now is less about the technology itself and more about whether you’ve created the conditions for everyone to use it. And if you haven’t normalized this shift and built experimentation into how your organization operates, the answer will always be no – no matter how good the tools are.

Article

From Buzzword to Backbone: What ALM 2026 Signals for AI and Advertising

February 11, 2026
By Matthew Caldecutt

Each year, the Interactive Advertising Bureau (IAB) Annual Leadership Meeting (ALM) offers a snapshot of where digital advertising stands and where it is headed. In 2026, that picture sharpened quickly. AI is no longer an emerging trend. It has become the industry’s operational substrate, shaping how decisions are made, campaigns are executed, and value is measured.

At ALM 2026, AI was not treated as a standalone topic or future experiment. Under the banner “It Starts Here,” it emerged as connective tissue across conversations on measurement, identity, commerce, and privacy. Rather than signaling a mandate for any single stakeholder, discussions reflected a shared reorientation toward common operating principles. The question shifted from whether AI will shape advertising to how decisively organizations are prepared to rebuild their foundations.

In prior years, AI discussions often focused on pilots or novel demonstrations. This year marked a clear shift toward what many described as industrial plumbing. AI is increasingly central to planning, activation, and optimization in 2026. According to the IAB’s 2026 Outlook Study, digital ad spend is projected to grow 9.5%, with growth shaped in part by AI-enabled targeting and automation. It now powers how media is bought, optimized, and valued.

Across sessions, a consistent theme emerged. Organizations that delay embedding AI into foundational workflows risk falling out of step with a market rapidly reorganizing around automation and intelligence. AI is not simply accelerating existing processes. It is redefining how planning decisions are made and how performance is evaluated in real time.

The emphasis on measurement made this shift especially clear. With the launch of Project Eidos, the IAB elevated a challenge the industry has grappled with for years: the erosion of trust in an opaque, AI-driven environment.

As signal loss continues to erode traditional tracking, the risk is not just fragmentation, but reduced transparency across platforms. The IAB’s 2026 State of Data report highlights a sobering reality: most buy-side respondents believe current AI-powered measurement falls short on rigor and trust. When platforms define metrics in isolation, brands are left comparing results that are difficult to audit or reconcile.

Discussions at ALM reflected broad alignment that this is not a failure of intent, but a structural challenge. Shared definitions, interoperability, and consistent frameworks are required to restore confidence. Project Eidos represents a coordinated effort to move beyond today’s patchwork of channel-based metrics toward an interoperable approach built on shared constructs, framed as a multi-year foundation effort to ensure AI delivers confidence alongside automation.

Commerce and retail media were positioned as the most immediate proving grounds for AI, combining rich first-party data with clearer paths to closed-loop measurement. However, the conversation was not solely about efficiency. Two friction points emerged.

The first was publisher sustainability. IAB President David Cohen introduced the AI Accountability for Publishers Act to address concerns surrounding large-scale AI data use and publisher compensation. Discussions reflected ongoing debate across the ecosystem about how value is created and shared in an AI-driven market. The focus was on exploring guardrails that balance innovation with the long-term viability of journalism and creator ecosystems.

The second was the AI ad gap. While 82% of ad executives believe Gen Z and Millennial consumers feel positive about AI-generated ads, only 45% of those consumers report feeling the same, according to recent IAB and Sonata Insights research. This gap highlights a disconnect between advertiser assumptions and audience sentiment. The findings suggest that clearer, more consistent disclosure of AI use, supported by the AI Transparency and Disclosure Framework, can help rebuild trust and improve resonance without undermining long-term brand equity.

A more understated challenge was the human element. While technical foundations are being put in place, many organizations are still building the expertise required to manage increasingly complex, agentic systems. The shift toward AI as infrastructure is driving demand for deeper AI fluency across strategy, creative, measurement, and governance.

Taken together, the conversations at ALM 2026 suggest that AI’s role in advertising has fundamentally shifted. The question is no longer how quickly the industry can adopt new capabilities, but how deliberately it can operationalize them. Measurement standards, disclosure frameworks, publisher protections, and talent development are no longer secondary considerations. They are the scaffolding required to make AI durable at scale. The shift from buzzword to backbone is already underway. What comes next will depend on whether the industry treats AI as a shortcut or as shared infrastructure that must be governed and trusted over time.

Matt Caldecutt is a Senior Vice President in the Technology Practice at FleishmanHillard, with extensive experience in advertising technology and digital media. He advises companies across the ad tech ecosystem on media relations, emerging trends, and industry issues, helping translate complex developments into clear, strategic communications.

Article

From Chaos to Clarity: Why AI is the Communications Industry’s Strategic Imperative

May 2, 2025
By Matt Groch and Caitlin Teahan

In a world where every headline seems louder than the last, communications professionals are being asked to do, and prove, more than ever. What was once a function centered on messaging and media relations has evolved into a high-stakes, high-visibility discipline responsible for protecting reputations, navigating cultural complexity, and driving business outcomes.

As technology further shapes the media landscape and economic pressures continue to mount, one thing has become clear: the role of communications is more complex and thus more critical than ever. But with challenge comes opportunity, and today, that opportunity is being fueled by the strategic integration of artificial intelligence (AI).

The Challenge: It’s Bonkers Out There

If you’re a communications leader, you’re likely feeling the pressure and for good reason. The landscape is chaotic.

Crises move at lightning speed, supercharged by social media and a disparate constellation of stakeholders with conflicting expectations. Add to that a hyper-polarized climate where nearly every issue becomes politicized, and it’s easy to see why navigating reputation risk has become exponentially more difficult.

Meanwhile, the media landscape is in flux. Traditional outlets are losing authority, misinformation spreads like wildfire, and trust is no longer centralized; it’s fragmented across a dizzying array of influencers, platforms, and niche communities. For brands, that means it’s harder than ever to shape consistent, credible narratives.

And that’s not all. Economic uncertainty continues to rattle both the public and private sectors. From rolling layoffs and market volatility to tariff threats and escalating trade tensions, the pressure is on, especially when it comes to protecting corporate revenues and justifying communications budgets.

The Opportunity: AI-Powered, Data-Driven Communications

Despite all the noise, there’s a bright spot — one that could help communications professionals not just survive but thrive.

For years, we’ve used tech tools like media monitoring platforms and databases. But until recently, they had only a marginal impact on how we worked or the business outcomes we could drive.

Enter large language models and generative AI.

These tools represent a true inflection point. They’re already enabling communications teams to move beyond reactive, manual approaches and toward more proactive, insight-led strategies that improve outcomes, not just optics.

With AI, we’re now equipped to analyze coverage, conversation, and cultural trends at scale — instantly and intelligently. That opens the door for more effective, real-time media intelligence, smarter issues management, and faster, more informed responses when a crisis hits.

FleishmanHillard’s Ephraim Cohen moderates a conversation with Business Insider CTO Harry Hope during The Briefing Room: Teams of the Future in NYC — a dynamic discussion on how curiosity, integrity, and quiet persistence can guide teams through constant transformation.

The Future Is Now: Comms, Collaboration, and Leadership

What once took days and armies of analysts, AI can do in minutes.

Teams can tap into virtual audiences (or synthetic personas) to test messages and develop campaign hypotheses quickly and affordably. These techniques are also changing the game behind the scenes, with agencies using them to streamline business development and accelerate RFP workflows. Of course, care must be taken, especially when targeting niche or harder-to-reach audiences, to avoid misleading outputs or poor strategy based on bad data.

Creative production is also getting a tech-powered boost. AI tools can now generate brand-aligned visuals, edit videos, draft headlines, and even create compelling campaign concepts — all faster, cheaper, and at scale.

But here’s the catch: all this potential only matters if teams are equipped and empowered to use it.

That’s where leadership comes in. Communications leaders must play an active role in fostering a culture of innovation. That means encouraging experimentation, offering training, and helping teams develop fluency with AI tools. In today’s environment, AI isn’t just a productivity enhancer, it’s a strategic lever.

And as communications teams are increasingly expected to demonstrate measurable impact, AI is enabling a new era of outcomes-focused storytelling. From real-time analytics to advanced attribution modeling, we’re more strongly positioned to tie comms efforts to business outcomes in a way that makes sense to the C-suite.

Bottom Line: A Strategic Imperative, Not Just a Tool

AI isn’t just here to optimize how we write press releases or track media hits. It’s reshaping the very foundation of the communications function.

The leaders who will thrive in this new landscape are the ones who see AI not as a threat, but as a strategic enabler. By embracing AI capabilities across insights, content, issues management, and measurement, communications professionals can elevate their strategic value and align more closely with business goals.

The challenge is real, but so is the opportunity.

Now’s the time to lead with clarity, act with urgency, and build teams that are ready for what’s next.