By Carey Madsen, VP and CMO, The Fletcher Group

94% of B2B buyers now use AI during the buying process, and most marketers are working hard to insert their brands into those buyer recommendations. But you’re probably making it harder than you need to.  

Here’s a scenario that plays out every day in B2B: a company earns a strong media placement in a respected trade publication. The story is sharp, well-positioned, and reaches the right audience. Then it disappears. Posted once on LinkedIn, shared internally, and forgotten. Sales never sees it. The website never references it. No one writes a follow-up post that builds on the insight. The executives who could have amplified it don’t.

This is what happens when PR and marketing operate in silos. Coverage and content don’t travel far  and in 2026, that has consequences that go beyond missed amplification. It affects how often your brand appears in AI-generated answers.

The way B2B buyers research and evaluate vendors has changed fundamentally over the past two years. Buyers no longer follow a neat funnel. They may read a trade article, which prompts a question, so they ask ChatGPT or Claude. The answer frames their next steps, which might include a visit to your website to read an FAQ or case study, an industry report, or to a competitor’s site instead.

If your messaging isn’t aligned and repeated across these channels, you haven’t made your brand known; and it’s difficult for buyers to find you, because they don’t know what you solve for. In a nutshell, vague messaging gets skipped, while consistent messages gets cited.

How Do B2B Buyers Research Vendors in 2026?

Forrester’s 2026 State of Business Buying report shows that purchasing is more collaborative, and dependent on validation from trusted sources than in previous search eras. Buyers rely on what Forrester calls a “buying network”  internal stakeholders plus analysts, peers, and earned media — to validate what they learn from any single channel, including AI tools.

The Forrester data paints a clear picture of just how early these decisions are forming:

·       92% of B2B buyers enter the process with at least one vendor in mind, and 70% of the journey happens before sales engagement 

·       9 out of 10 C-suite decision makers say they are more receptive to thought leadership than traditional marketing materials

·       94% of buyers use generative AI during the buying process, but 20% report inaccuracies—leading them to validate AI outputs against third-party sources 

Buyers use AI as a data point, then confirm what they find through media, analysts, LinkedIn, and your owned content. If your brand shows up in only one of those places, you’re missing other essential validation opportunities.

Why Do LLMs Favor Brands with Multi-Channel Presence?

This is where buyer behavior and AI visibility intersect. LLMs pull from media coverage, brand content, social conversations, and third-party validation to shape the answers buyers see. Brands that appear across more source types tend to be cited more often and with more context.

The rules of AI-fueled search are evolving in real time, but several patterns are already clear enough to act on:

·       Earned media drives the majority of AI citations. Muck Rack found that 82% of citations come from earned sources

·       Brand search volume is a stronger predictor of AI citation than traditional SEO authority like backlinks 

·       LLMs do not share the same resource pools, so, appearing on a wide range of relevant channels—owned, paid and earned—is necessary to be cited by all the most popular LLMs

In practice, this means disconnected or incomplete efforts across PR and marketing teams create visibility gaps that competitors can fill. When PR, content, and executive visibility aren’t aligned, you reduce the number of trusted signals AI systems rely on.

How Does One Asset Become Five?

The real value of integration is making one success work four times harder. This helps large companies absolutely dominate their space and lets smaller firms punch above their weight through efficient use of resources.

Here’s what that looks like in practice. Take a single starting point: your company releases original data or research on a trend that matters to your buyers.

•      Earned: The research is pitched to key trade publications and tier 1 business outlets. Stories are published, your CEO is quoted with a distinctive point of view.

•      Owned: The research becomes an un-gated blog post and report on your website, structured with clear headers, FAQ sections, and schema markup so both Google and LLMs can parse it effectively. Key data points are formatted as standalone, citable claims that start showing up in other earned media.

•      Shared: Your CEO and other executives post their own take on LinkedIn — not identical reshares, but distinct perspectives that create multiple entry points for key audiences. The company page amplifies with a summary post linking to the blog.

•      Third-Party/Paid: A LinkedIn sponsored content campaign targets decision-makers in your key verticals. An analyst briefing results in an informed industry expert that validates the narrative for media and prospect inquiries. The research serves as the foundation of a presentation or webinar at an industry event.

Does Integrated PR and Marketing Require a Large Budget?

No. In fact, smaller teams are often better positioned to do this well from day one, because they can’t afford to be spread too thin. Even some larger brands can’t activate all channels at scale, and trying to do everything at a surface level is worse than doing two things well. But whatever you do invest in, do it well, and set your campaigns up to compound across channels rather than exist in isolation. 

A single earned media placement that nobody amplifies, repurposes, or references on your website is a missed opportunity — and that’s true whether your budget is $50,000 or $500,000. A blog post that answers a question your buyers are asking but never gets shared by an executive or promoted to a targeted audience is content that only works in one way, instead of four or five.

Integration is a mindset about how assets get used, not a mandate to spend more. Start with what you have. Make each piece of content and each media win work across every channel you can reach. 

The Outcome: Consistent Presence Where Buyers Look

The B2B buyer’s journey is no longer a path you control. It is now made up of a network of sources — and increasingly, a network that AI tools reference on their behalf.

When PR, content, social, and paid efforts work together, your brand appears more consistently across those sources. That consistency builds consensus  and ultimately, trust.

 Yashaswi Mudumbai, Senior Director of Solutions Engineering, APAC, JFrog

Q1: JFrog has announced a new integration with NVIDIA around agentic AI. What problem is this solving and why is it becoming critical now?

At the core, this solution closes a growing trust gap. As AI evolves from copilots to autonomous agents that can access systems, data, and tools, they require stronger governance than traditional software pipelines can provide. The risk is real, just as a malicious software package can compromise an application, an unvetted skill can guide an agent to perform harmful actions.

In an agentic environment, it is now about governing skills, models, MCP services, and other agentic assets that can directly influence how AI behaves in production. 

This is critical because AI agents are moving from experimentation into real enterprise workflows. JFrog’s new Agent Skills Registry, with early integration with NVIDIA, is designed to provide the missing trust layer required for autonomous AI workforces to operate safely at enterprise speed and scale.

By serving as a secure system of record for skills, models, MCPs, and agentic binary assets, JFrog serves as a secure, single source of truth for rigorously scanning and governing all agentic binary assets, which NVIDIA’s NemoClaw then executes in highly isolated sandboxes with zero initial permissions. This ensures every skill is approved and safe for use at enterprise scale.

Enterprises cannot rely on blind trust, they need a way to verify which agents and assets are being used, where they come from, and whether they comply with internal policies before agents can operate at scale. 

Q2: Many Australian organisations struggle to move AI projects from pilot to production due to security and compliance concerns. How does this joint solution with NVIDIA help bridge that gap? 

One of the biggest barriers to scaling AI is that innovation often outpaces governance. Teams build pilots and test models, but when it comes to deploying them into production, questions around security, compliance, and accountability slow everything down. 

The partnership  between JFrog and NVIDIA helps put structure around that process, giving organisations a centralised way to manage all the components that power AI agents, from models to connectors to reusable skills, while ensuring they meet enterprise standards before they are deployed. 

Instead of relying on fragmented tools or manual approvals, organisations can automate checks, enforce policies, and maintain visibility across the entire lifecycle. That makes it much easier to move from experimentation to production without introducing unmanaged risk. 

Q3: As AI adoption accelerates globally, how is the concept of an “AI Supply Chain” evolving compared to traditional software pipelines, and how is Australia responding?

The AI supply chain is fundamentally different from traditional software delivery. In the past, organisations were managing relatively static components like code and packages. Now they are dealing with dynamic elements such as models, datasets, prompts, and agent behaviours. 

With AI systems now adapting and acting independently, this means organisations need to track  not only what goes into an application but also how it behaves once deployed. In Australia, we’re seeing a strong emphasis on governance and accountability as part of this shift, particularly as organisations align with the Australian Government’s AI in Government Policy and broader responsible AI frameworks that emphasise transparency, accountability, and safe deployment.

Enterprises are recognising that adopting AI at scale requires visibility, traceability, and control, particularly in an increasingly regulated marketplace.  

Q4: Australia is seeing growing enterprise investment in AI, particularly across sectors like financial services and government. What specific risks or opportunities do you see for Australian organisations adopting agentic AI?

When agents are given access to internal systems, data, and workflows, any gap in oversight can lead to serious consequences, from data exposure to compliance breaches. There is also a growing concern around ‘shadow AI,' where teams adopt tools or models outside of approved processes. This creates blind spots for security and governance teams, making it difficult to understand what is actually running inside the organisation. 

For Australian enterprises, especially those operating in regulated environments, the priority is to ensure that innovation is matched with strong controls from the outset. Those that get this balance right have a clear opportunity to build a trusted AI and software supply chain that not only reduces risk, but also accelerates speed to market by giving teams the confidence to scale AI safely and consistently. 

Q5: Trust and governance are emerging as major concerns for enterprises deploying AI agents. How does JFrog’s new Agent Skills Registry address these challenges in practical terms?

JFrog’s Agent Skills Registry is designed to bring order to what is otherwise a highly fragmented landscape. It acts as a central point where organisations can manage the different components that AI agents rely on. 

This means every skill or asset can be inspected, validated, and approved before it is made available for use. It also allows organisations to define who can access what and under what conditions, ensuring that agents operate within clearly defined boundaries.

Importantly, it creates an audit trail, enabling organisations to track where assets came from, how they were used, and whether they meet compliance requirements. That level of visibility is essential for building trust in systems that are becoming autonomous. 

On the execution side, NVIDIA’s NemoClaw then runs each agent in an isolated, virtual environment, sandboxed with zero initial permissions. Thus, even if a skill were to behave unexpectedly, it can not affect broader systems or trigger network-level risk. 

Q6: For developers and engineering teams in Australia, how can they balance strong governance with the need to innovate quickly when building and deploying AI agents?

The goal is to embed governance into the workflow rather than treat it as a separate step. If security and compliance rely on manual reviews, they will always slow teams down. 

Instead, organisatons should focus on automating these controls. By providing developers with access to pre-approved, trusted components, they can move quickly without needing to navigate complex approval processes each time. 

This approach allows teams to maintain speed while ensuring that everything they use has already been vetted. For Australian organisations, particularly those under regular pressure, this balance between agility and control is critical to scaling AI successfully.

1. 
   They're building a proper identity foundation. They’re not just collecting emails. They’re actually resolving those signals across web, mobile, CTV, and audio into a unified identity graph that makes the data actionable at scale.
2. They're investing in the infrastructure to enrich and activate that data in real time. They’re moving away from manual processes because the programmatic market rewards speed and precision.
3. They're putting AI agents to work. They're replacing manual processes by deploying agents to query their first-party assets, build custom audiences, and push them to activation platforms in minutes. They’re ready for the buyer agents that are already searching and evaluating their inventory.
   
   

• Grief — Sailing with Angels
• Grit — Sailing with Alligators
• Growth — Sailing with Anglers

• Pay attention to what has stayed with you over time 
• Trust that your skills translate 
• And give yourself permission to try