When marketers need answers and insights to make the most well-informed decisions, they turn to their marketing dashboards. But are their dashboards truly working for them? Increasingly, the answer is “no.” Marketers deserve better – and through advanced AI, “better” has become a reality.  

In a car, the dashboard is where you monitor speed and fuel levels and see service alerts – all key indicators of the driver’s overall performance. In the dashboard of a marketing tool, marketers can monitor campaign performance – traffic, engagement, conversion metrics, and more, ideally at a glance. 

Of course, any modern marketers will tell you their dashboards fall far short of that ideal. Dashboards require marketers to search and search to discover the right insights for the moment. Marketers need solutions that deliver those insights. And AI is making this transformation to a new model possible. AI’s potential here is so great that it’s allowing marketers to move beyond dashboards – and to put the power of a decision engine in their own hands. 

AI Is Heralding the Next Generation in Marketing Decision-Making 

So what changes in a marketer’s day-to-day life when they adopt a decision engine model? In short, they gain agility, efficiency, and focus. More broadly, marketing teams are given an opportunity to revamp their operations overall. Dashboards are built to monitor. Decision engines are built to guide the marketer, enabling them to be proactive and customize their approach through intelligent and responsive systems.  

With the continuous expansion of marketing channels to monitor comes immense volumes of data. Marketers need to leverage that data to decide what actions to take next. But in a traditional dashboard, that’s often an awkward and time-consuming proposition. The interface often feels both static and old-fashioned. These are your charts, but which are the right charts? These are your metrics, but what patterns do they suggest? More to the point, what should we do next to succeed? 

Also, “dashboard fatigue” is real. A recent Gartner study revealed only 38% of CMOs believe their dashboards actually empower them to make better decisions. Teams commonly abandon dashboards, too. BI and dashboard tools have an adoption rate of only 20%, according to BARC. 

Don’t Think Charts. Think Partnership 

Marketers today need more than charts – they need a decision–making partnership. Next-generation AI tools can provide that partnership – answers to questions, relevant alerts, and more. Here’s what an AI-powered decision engine does for the marketing team: 

• Delivers insights to you directly. Marketers receive timely alerts and updates about new trending patterns across KPIs, rather than needing to search manually. 

• Speaks to you like a human. Rather than clicking and filtering to get highly specific insights, marketers can simply ask generative AI a question in conversational language and get a clear answer right away. 

• Delivers insights within your business’s workflow and communications tools. Instead of logging in, marketers can now embed analytics into their workflow by choosing to have them delivered straight to email, Slack, project boards, and other tools where marketers do their work. 

• Forecasts future trends and recommends decisions. Dashboards tell marketers what already happened. Decision engines illuminate what will happen next and how to best meet those trends. 

How the CMO of Real Estate Giant JLL Put AI to Work – and Succeeded 

We’re seeing leading businesses adapt to today’s business realities, innovating and investing for the future, and succeeding in the marketplace. Case in point: Commercial real estate giant JLL needed to reassess the effectiveness of their dashboards in 2023. Global CMO Siddharth Taparia recognized the limitations in the older dashboard models JLL was relying on. His proposed solution? A proprietary large language model, which JLL would custom-develop to serve the company’s specific needs.  

The LLM they built, JLL GPT, totally reimagined their marketing operations. New efficiencies abounded. For example, where the process of drafting partnership memoranda used to take four to six weeks to complete, now it took less than five hours. The difference between their previous methods and new methods? Their old dashboards served more as reporting tools, but the new tools acted more like decision engines. The efficiency and effectiveness of JLL’s innovations inspired 400 of their marketing professionals to adopt AI and begin reaping the benefits of intelligent systems.  

Modern Marketers Need to Demand Better – and Define “Better” 

Now is the time for marketers to take action to find the tech they need to thrive today and into the future. Here’s how to start moving away from dashboards and toward AI-powered decision-making: 

Conduct a full dashboard audit. Ask your team and any other relevant stakeholders: Which of these current tools do you actively use? Which are important to your decision-making? Which help you understand real ROI? And for those that are not delivering value, how can they be cut or consolidated? 

Test agentic tools. Gartner forecasts that 30% of new applications will use AI for proactive decision recommendations by 2026. Now is the time to test AI tools and evaluate whether they’re helping the marketing team. Marketing leaders should choose an AI assistant, alerting system, or conversational analytics tool to test. It’s an important part of keeping a competitive edge. 

Demand better from your tech partners. McKinsey research tells us businesses that embed AI in their decision-making process are more than 2x more likely to “outperform peers in marketing effectiveness.” The imperative for marketers is to seek AI-powered solutions that uncover and deliver insights that impact their own goals and objectives, and that recommend next steps. 

Connect insights to real actions. Harvard Business Review research shows that turning data analytics into action is a challenge for 72% of companies. Any AI-powered decision engine must provide measurement to fill in that gap, understand the real value of the tools, and improve performance meaningfully. 

In today’s marketing landscape, the growth and the momentum is in tools that enable and even encourage deeply informed decision-making, agility, flexibility – and in the end, success for the marketing team that boosts the whole business’s bottom line. AI is making this finally happen – and demonstrating marketers really have outgrown the old dashboard model. To find out more about how and where Mod Op sees AI rising to the occasion and supercharging B2B marketing, reach out to us today. 

The 2025 Cannes Lions Festival proved once again to be a hub for the latest thinking about innovation, technology and the future of advertising.  

Early at the show, EVP of PR, Chris Harihar, shared some of the shifts happening in the adtech industry that were prevalent at Cannes. But the points Chris highlighted weren’t the only advertising industry conversation that happened during the show.  

Several of Mod Op’s advertising industry clients had a presence at Cannes Lions this year, including GumGum, Yahoo and Vantage. Here are some of their takeaways from the event: 

Media and Commerce Converge  

While at Cannes, Drew Cashmore, Chief Strategy Officer for retail and commerce media orchestration platform, Vantage, observed additional signs that media and commerce are converging. 

As he shared with Performance Marketing World: “At Cannes, there was a noticeable shift as traditional digital media companies like Meta, Reddit and Google explored how to collaborate with retailers in the retail media space. This convergence signals a growing recognition that commerce and media are no longer separate disciplines. As these players look to coexist and compete, the lines between brand advertising and retail activation are blurring.” 

Read the full article: “Why in-store media is a sleeping giant for marketers” 

(You can also catch Drew on Mod Op’s Leader Generation podcast, where he shares additional insights from Cannes Lions.)   

Contextual Signals Win 

GumGum co-hosted an Adweek House Cannes Lions Group Chat where industry leaders explored how contextual signals can outperform behavioral and demographic targeting and drive performance.  

As Adweek’s Michael Keenan summarized in his piece on the session: “Panelists agreed that effective targeting begins with what a person is doing at the moment, not who they are on paper. But it can’t be done without the right technology.” 

As GumGum’s Chief Marketing Officer, Kerel Cooper, shared, “We believe there’s a world where we can move beyond demographic information to help brands connect with who they want to connect with and provide a great experience for them.” 

Read the full article: “Contextual Signals Win as Data Accelerates Marketing” 

Advertisers are Exercising Caution  

As Yahoo’s Chief Revenue Officer, Rob Wilk, shared with the New York Post, “If you look at what you read, it seems way gloomier than what I experience day to day.”   

Rob noted that many advertisers are taking a strategic “wait-and-see” approach and those “holding on to dry powder” and waiting to spend versus “slashing budgets and pulling back.” Rob is also seeing that same caution reflected in dealmaking, due in part to the tariff conversation. 

Read the full article: “Media, ad execs and celebs return to Cannes Lions” 

Cannes Lions 2025 made one thing clear: the advertising industry isn’t standing still – it’s recalibrating. As media and commerce continue to collide, contextual targeting gains traction, and brands tread carefully amid economic uncertainty, the road ahead demands both innovation and intention. For marketers, that means moving beyond the buzz and leaning into strategies that are data-smart, audience-focused, and ready for what’s next. 

This year, Cannes Lions isn’t just a celebration of creativity and marketing. For better or worse, it’s a stress test for an industry in flux. From agency restructurings to economic uncertainty, advertising is being reshaped in real time.  

And in ad tech — which underpins it all — the pace of change is even faster, fueled by AI, shifting channels, and growing complexity around data. Amid the plentiful rosé and sunshine, here are the three themes I expect to dominate ad tech conversations this week. 

How Real Is Your Agent? 

AI dominated Cannes last year, but the conversation is evolving. It has shifted from generic AI hype to Agentic AI, the systems that don’t just generate responses but actually take action and complete tasks on your behalf, autonomously and at scale. In theory, this should be a watershed moment. But in ad tech? It’s mostly noise. Companies are slapping the term “agentic” on any product with a basic prompt-response interface. That’s not agentic AI. 

What we’re seeing is a wave of opportunistic rebranding as companies chase fresh funding. Expect “agentic” to be one of the most abused buzzwords at Cannes this year. True agentic systems are pre-trained and actually intelligent, capable of making decisions and executing workflows with minimal human input. Most ad tech companies still don’t have a credible AI story, let alone real agentic capabilities. But that won’t stop them from pretending they do.

CTV Wants SMBs, and It Shows 

Cannes isn’t exactly known for championing small businesses. But 2025 has brought a growing push to get SMBs and DTC brands more meaningfully involved in connected TV, and the buzz around it will likely continue along the Croisette this week. CTV can’t scale forever without broader advertiser participation. To unlock the next phase of growth, platforms need to open up programmatic access and tools and make “performance TV” feel real. They also have to make it accessible — both in cost and in complexity — to businesses without Super Bowl budgets or major agency support.  

This explains why companies like MNTN are showing up at Cannes ready to make a splash. Roku and Comcast are also leaning into SMBs, offering lower entry points and self-serve tools designed for smaller advertisers. The message may not be tailored to the Cannes elite, but the opportunity is clear: CTV needs a middle class.

Retail Media Is Dead. Long Live Media Networks 

Retail media is undergoing an identity shift. In fact, it’s no longer just about retailers. Marriott, Western Union, and even Chuck E. Cheese are launching media networks. The term “retail” can’t contain what this has become. At this point, any brand with scaled first-party data and media assets can spin up a media network and, increasingly, they are.  (This is a drum I’ve been beating for some time, and the IAB recently endorsed that thinking.) More importantly, the focus is moving well beyond owned-and-operated properties to include off-site extension. Initially, retail media was prized for its proximity to purchase, with ads running on retailer-owned sites near checkout. But now, the value increasingly lies in using that data across the open web. 

Marriott’s media network, for example, isn’t just about marriott.com or Marriott in-app inventory. It’s about activating traveler intent data across the broader internet. The same goes for financial and entertainment brands, as well as any other media networks. Expect to hear less about “retail” and more about “commerce” or simply “media networks” — and a lot more emphasis on off-site activation, which has quietly become the fastest-growing offering. 

Some of these discussions may sound semantic, but in reality, they’re structural. We need to align on what truly qualifies as agentic AI and stop mislabeling basic automation of Gen AI. We need to democratize CTV by making performance TV real in terms of accessibility and measurability, so it’s more than just a buzzword. And we need to move beyond the limitations of “retail media” and embrace the broader, more scalable future of “commerce media.” Cannes is a good sandbox for these conversations, as it’s reshaping what’s to come in the second half and beyond. 

“I Quit Google Search for AI—and I’m Not Going Back” 

Headlines like this, which appeared in The Wall Street Journal last month, reflect a notable change in user habits: Many of us are turning to AI-powered tools like ChatGPT, Gemini, Grok and Perplexity to look up things we would have previously searched in Google. (It’s even spawned a new marketing acronym: GEO or Generative Engine Optimization).  

While I’m not ready to shelve “Googling” as a verb, I do agree with many of my peers that it’s time to consider what the move to large language models (LLMs) means for marketers. What happens to search engine optimization (SEO) as users begin to shift away from “search engines”? What happens to PR and media relations when readers start their journey on ChatGPT and not The Wall Street Journal? 

I have an answer – and it might surprise you: With the increasing prominence of generative AI and LLM, SEO and PR are more important than ever.  

In this blog we explore how generative AI and LLMs are using earned media coverage and what media publications tend to be indexed most frequently.  

How LLMs are Using Earned Media Coverage 

At Mod Op, we’ve launched a multidisciplinary team of PR professionals, SEO specialists and members of our AI and innovation team to explore, understand, and use AI tools to strategize improved brand presence in AI-generated content.  

One of our early findings: ChatGPT tends to cite well-known, authoritative media sources. In other words, mainstream news sites with strong reputations and visibility, like The New York Times (NYT), CNN and Forbes, are heavily represented in ChatGPT’s citations.  

How do brands secure mentions in publications like The New York Times (NYT), CNN and Forbes? By effective use of PR and, more specifically, media relations. In fact, MOZ founder Rand Fiskin even cites LLMs and AI indexing as one of the reasons that public relations is the future of marketing:

“Language models are governed by the ‘trusted sources’ they crawl, index, and build up their text corpora,” explains Rand, “Three guesses what influences change in those models. Yup, it’s PR-Influenced Content. The ability to influence how people write about, talk about, and publish about you on the web directly impacts how AI tools respond to questions about your brand, your field, and whether they include you when prompters ask about the problems you solve.” 

What Media Outlets is AI Indexing? 

To better understand what media outlets are being indexed by LLMs, we decided to look at one of the most popular: ChatGPT. We used ChatGPT4.5 + Deep Research to complete an analysis of what publications are cited most often in ChatGPT, focusing on tech and consumer goods.  

Major Media Outlets 

According to our analysis, major U.S. outlets like The New York Times (NYT), The Wall Street Journal (WSJ), and CNN frequently appear as cited sources due to their reputation and high search visibility (DeepSeek vs. ChatGPT vs. AI Overviews: YMYL Research Study).   

In PR — as well as SEO — we often use metrics like UVM (unique visits per month), which estimates an outlet’s viewership (the modern “circulation”), Domain Authority and Spam Score to judge an outlet’s reputation and search visibility.   

News agencies such as Reuters are also commonly referenced (DeepSeek vs. ChatGPT vs. AI Overviews: YMYL Research Study). Like the major U.S. outlets mentioned above, Reuters has a relatively high UVM (50.6k), very strong Domain Authority (94) and low spam score (3%). 

Industry-Specific Publications 

Alongside general news, ChatGPT cites industry or sector-focused journals and trade publications when addressing industry-specific queries.  

In the technology sector, leading tech news sites like TechCrunch, Wired, The Verge, Engadget, and CNET are frequently referenced (Top 100 Tech News Websites for Technology Enthusiasts in 2025). These outlets specialize in tech coverage and often rank highly for tech queries. Again, if we look at common PR and SEO metrics, we see a pattern of relatively high UVM, very strong Domain Authority and low spam score. 

For consumer goods and retail topics, trade publications such as Retail Dive and Consumer Goods Technology are often cited for industry trends and analysis 

(Retail Dive: Retail News and Trends) (Consumer Goods Technology: Consumer Goods Industry News …). Similarly, Consumer Reports (for product reviews) and Chain Store Age (for retail news) may appear in citations due to their domain expertise, according to our ChatGPT4.5 + Deep Research analysis.  

For these more niche publications, UVM becomes less clearly indicative of reputation. We also begin to see Domain Authority dip below excellent, but it’s within the range of what is considered “good” (50 – 60). The spam score remains low.   

Wikipedia  

One interesting source that emerged from our ChatGPT4.5 + Deep Research analysis: Wikipedia. While not a media outlet, Wikipedia is one of the most cited sources across many topics (DeepSeek vs. ChatGPT vs. AI Overviews: YMYL Research Study). This is particularly interesting when you consider the connection between Wikipedia and earned media coverage. 

If you’ve ever tried to make a change on Wikipedia, only to have it quickly scrapped by Wikipedia editors, you’ve likely already discovered the importance of sources.  

Earned media coverage acts as a powerful citation engine for Wikipedia pages. When authoritative media outlets write about a topic, person or an organization, these articles serve as independent, verifiable sources that Wikipedia editors can use to support claims.  

Publications Most-Cited by ChatGPT  

Developed using ChatGPT4.5 + Deep Research, the list below ranks some of the most-cited publications in ChatGPT’s outputs for tech and consumer goods topics (U.S. focus), based on their observed citation frequency in ChatGPT’s answers. 

1. Reuters 
2. The New York Times 
3. CNN 
4. Forbes 
5. TechCrunch 
6. Wired 
7. The Verge 
8. CNET 
9. Retail Dive 

While it’s important to note that ChatGPT4.5 – even with DeepResearch – and make mistakes, this is a great starting point.  

As you’re looking to PR and media relations to further brand awareness, and how you may be able to use earned media to support AI indexing and AI search visibility, I recommend using a media database like MuckRack to review the UVM, Domain Authority and Spam Score for various target publications. After all, LLMs index publications with smaller UVMs and lower Domain Authority, especially when it’s around niche topics — for example, Consumer Goods Technology, with a UVM of under 100k and Domain Authority in the “good” range (50 – 60).   

With all that in mind, here are ten publications I’d recommend exploring: 

Some final thoughts: consumer search behavior is changing. With users shifting from search engines to AI-powered tools like ChatGPT and Gemini, the role of earned media has never been more critical. For those with established PR programs, refining media targets by integrating metrics like UVM, Domain Authority, and Spam Score can improve the quality and credibility of earned media placements, increasing the likelihood coverage will be cited by AI models like ChatGPT. For those without a PR program – it’s time to start building.  

When it comes to getting discovered online, it’s no longer just about search engine rankings. It’s about being the answer – not just to search engines, but to AI-driven queries as well. Welcome to the new frontier: GEO, or Generative Engine Optimization. 

What Is GEO and How Is It Different from SEO? 

Search Engine Optimization (SEO) is the long-established practice of enhancing your website to improve visibility in search engine results pages (SERPs). It involves strategic keyword usage, quality content, backlink building, technical improvements, and more – all aimed at pleasing algorithms like Google’s or Bing’s. 

Generative Engine Optimization (GEO), on the other hand, is about making your content AI-friendly. Instead of optimizing for ranked lists, GEO is designed to make your content usable by large language models (LLMs) like ChatGPT or Google’s Gemini. The goal is to have your content referenced directly in AI-generated responses. 

Traditional SEO: The Foundation of Visibility 

SEO is built around matching user search intent with valuable content. Techniques include: 

• Keyword optimization 
• Content quality and relevance 
• Mobile usability and speed 
• Internal linking and crawlability 
• Authority through backlinks 

The objective? To appear on page one – preferably at the top – so users click through to your site. 

GEO: The Future of Content Optimization 

Unlike SEO, GEO doesn’t focus on rankings. Instead, it emphasizes: 

• Clarity and depth of content 
• Answer-focused structuring 
• Use of statistics, citations, and industry-specific terms 
• Intent-based writing, whether informational, navigational, or transactional 

The goal here is to provide content that AI can easily understand, extract, and rephrase into helpful, conversational answers. 

Comparison Chart: SEO vs GEO 

Why Helpful, Targeted Content Matters More Than Ever 

With Google’s AI Overviews and ChatGPT’s real-time browsing capabilities, traditional content optimization is evolving. Modern algorithms – and AI models – now look beyond keywords. They analyze the semantic meaning of content and evaluate how well it answers real-world questions. 

GEO thrives in this space. It encourages creators to: 

• Focus on specific questions or problems 
• Provide concise, high-value content 
• Use domain-specific language, data, and citations 

By doing so, you’re not just hoping to be seen – you’re positioning your content to be used and trusted by AI systems. 

How to Optimize for GEO and AI Discovery 

Here are actionable strategies to boost your content’s potential for being selected in generative responses: 

Content Creation 

• Research what questions your target audience is asking to understand the most important topics to address 
• Include clear summaries or key takeaways at the top 
• Use structured content: headers, bullet points, and short paragraphs 
• Answer specific questions clearly and directly 

Pro Tip: Start by auditing your existing content for AI-readiness. Ask yourself, “Would this answer be helpful if read aloud by an AI assistant?” 

Content Enhancement 

• Cite reliable sources and include statistics 
• Incorporate industry-specific terms and authoritative tone 
• Address various search intents: informational, navigational, transactional 

Technical & Distribution Strategies 

• Use structured data markup (e.g., FAQ schema) 
• Reindex updated content via Google Search Console 
• Distribute on platforms like Reddit, Quora, and social media 
• Use multimedia formats like infographics or videos 

Final Thought: It’s Not SEO Versus GEO – It’s SEO Plus GEO 

Think of SEO as the way to be found. Think of GEO as the way to be chosen. They are not opposing tactics — they are complementary strategies. When used together, they allow your content to perform in both search results and AI-generated conversations. 

So don’t choose between them. Master both. 

At Mod Op, we’re no strangers to tight deadlines, evolving client needs, and the constant demand for fresh, brand-aligned storytelling. As a PR professional, staying ahead of the curve often means digging deeply into brand materials, mastering a client’s voice, and translating business objectives into meaningful narratives that resonate with media and audiences alike.  

That’s why I was excited (and admittedly a little skeptical) – when I first started using Brand Agent, our in-house AI-powered tool. 

Spoiler alert: It’s quickly become one of the most valuable resources in my toolkit. 

Using Brand Agent in PR 

PR teams juggle a wide range of tasks – many of which require a deep understanding of a brand’s identity and strategic goals. Brand Agent helps us accomplish more with less stress and greater accuracy.  

1. Faster Media Preparation 

For a product launch where we need to develop a full media kit, including talking points, executive bios, and supporting data, Brand Agent can be instrumental. Normally, this would require pulling from multiple sources, including past press releases, internal briefs, meeting notes, and more. 

With Brand Agent, we simply upload the newest product documents, merge them with existing client data, and use natural language prompts to extract the exact insights needed. It can help PR teams create clear, concise messaging that reflects the brand’s voice and strategy without spending hours digging through past files. For example, we recently needed to combine two old bios for our client as well as new messaging to create a fresh bio that also highlighted a bit about the value proposition of the company. We were able to easily upload all the documents and Brand Agent provided a new bio that reflected the new messaging as well. 

2. Real-Time Reactive Pitching 

One of the more demanding aspects of media relations work in PR is reacting quickly to breaking news and trending topics. When an unexpected industry shift occurs, we often need to craft a quick POV for clients – something that is tied to the trend while staying aligned with their positioning. 

Using Brand Agent, PR teams can quickly review how the client had previously spoken about similar topics and match it with current campaign messaging. Within minutes, the tool can help generate an on-brand response that is timely and relevant. And because Brand Agent provides access to historical talking points and tone guidance, we don’t need to pause to double-check phrasing or approvals before hitting send. For clients, using Brand Agent can help us turn around POVs three to four times faster than before. 

3. Smarter Content Creation 

Any PR person knows that drafting press releases or blog posts can be time-consuming. What Brand Agent offers is a starting point that reflects past content, brand tone, and messaging frameworks. For client thought leadership pieces, it can help repurpose existing research into a first draft that hits the right points and tone. Of course, editorial judgment and human polish is still needed, but it can drastically cut down writing time for PR pros. 

Why Brand Agent is a Game-changer for PR 

It Speeds Up Onboarding 

Onboarding new clients is one of the most critical parts of agency life. In the past, it could take weeks to internalize a brand’s voice, its PR goals and competitive landscape. With Brand Agent, that process becomes dramatically more efficient. The PR team can ask questions like, “What are the client’s core brand pillars?” or “How did they position their product in the last campaign?” and get immediate, brand-informed answers. Using Brand Agent, junior team members can ramp up faster on client accounts while senior team members have more time to focus on strategic execution. 

It Reduces Siloed Information 

In any agency, knowledge can get stuck in silos – especially when it comes to old documents or even teams transitioning off accounts and losing that institutional knowledge base. Brand Agent democratizes access to brand knowledge across teams. PR teams no longer have to wait for an account lead or strategist to send over background on a campaign. This information can be easily accessed via Brand Agent, which understands the context and provides brand-aligned information. 

It Enhances Quality and Consistency 

Consistency is essential in PR, especially when we’re working with multiple teams and touchpoints. With Brand Agent, we know that client messaging aligns with what the creative team is producing, what the strategist is planning, and what the client expects. It ensures that every piece of content produced reinforces the same brand narrative. 

Security and Trust Built In 

One of the most common concerns about AI tools in our industry is data security. Clients are cautious about how and where their proprietary information is being used. With Brand Agent, we can assure clients that their data is secure. In this closed LLM, no client information is fed into open models. One of Brand Agent’s biggest differentiators is that it doesn’t train public systems, so access can be tightly controlled within our team.  

Brand Agent hasn’t replaced the creative thinking, storytelling skills, or media instincts that are essential to PR, but it has elevated them. It has freed up time for deeper thinking and it can make PR work faster and more accurate, and our teams more confident in their output. And most importantly, Brand Agent will help PR pros deliver more consistent value to clients. 

Recently, I had the opportunity to help test a new AI platform at Mod Op called Brand Agent. Brand Agent is a large language model (LLM) that is extensively trained on client data and information to act as an information retrieval portal when asked pointed questions. 

It was an interesting experience – not just because I got an inside look at how the platform functions, but because I played a role in coordinating the testing process, gathering insights, and helping shape how we might use Brand Agent internally in the future. 

Getting the Right People Involved 

When we kicked off testing, one of the first steps was ensuring we had a well-rounded group of testers. Since Brand Agent is designed to support various aspects of our work, we needed input from people across different parts of the agency – PR, strategy, creative, and more. Each of these teams interact with client data in unique ways, so it was important to have a diverse set of perspectives to get the full picture of what Brand Agent could do. 

Once we identified our key testers, we made sure they understood what we were looking for in their feedback. We didn’t want the testing to feel like just another task to check off: we wanted it to be a meaningful experience that would actually help improve the platform. That meant setting clear expectations and making sure everyone was logging their findings consistently. 

Watching the System in Action 

One of the most fascinating parts of the process was seeing how Brand Agent handles data. Once testers selected which client they wanted to work with, we loaded the platform with relevant publicly accessible data. Seeing this in action made it clear just how much potential AI has in our industry, especially when it comes to making sense of large amounts of data quickly. 

But, as with any new tool, Brand Agent needed refining. Some testers found certain functions intuitive, while others ran into roadblocks that highlighted areas for improvement. The feedback we gathered helped pinpoint what was working well and what needed to be tweaked to make Brand Agent more effective across different teams. 

Turning Feedback into Action 

At the end of the testing phase, I compiled all the insights into a wrap-up report. This wasn’t just about listing what people liked or didn’t like, it was about identifying patterns, surfacing key takeaways, and outlining next steps for making Brand Agent a more valuable internal tool. 

One of the most valuable things we learned was how different teams approached the platform in their own ways. Creative teams, for example, were focused on brand guidelines and messaging insights, while strategists wanted a deeper dive into audience behavior. Understanding these nuances will be key in shaping how we refine Brand Agent moving forward. 

Looking Ahead 

Overall, this was a great experience – not just because I got to see an AI platform evolve in real time, but because it gave me a new perspective on how different teams within the agency interact with technology. The testing phase was just the beginning, and I’m excited to see how Brand Agent develops from here. There’s a lot of potential for AI to enhance the way we work, and I’m looking forward to seeing where we take Brand Agent next. 

This year at Mod Op, something interesting happened. Without much fanfare, we quietly introduced over 60 specialized AI teammates into our daily work. We call them Brand Agents – custom-built, brand-aware digital collaborators that help our teams do better work. No press releases, no grand proclamations about “revolutionizing” anything. Just steady progress toward making our clients unforgettable. Here’s the story. 

What’s a Brand Agent, anyway? 

Picture this: an AI assistant that knows everything about your brand – the guidelines, the history, the competitive landscape, all of it. Not just surface-level stuff, but the deep context that usually takes years to absorb. Unlike generic AI tools that know a little about everything, each Brand Agent is purpose-built for a specific client, steeped in their voice, visual identity, and strategic goals. 

It’s like having a brand strategist, creative director, and data analyst rolled into one digital teammate – one that never sleeps, never gets tired of double-checking details, and can process mountains of information in seconds. And because security matters, each Brand Agent lives in our secure environment. No client data wandering off into public AI training sets. 

Brand Agents learn and evolve alongside the brands they serve. As campaigns roll out, markets shift, and strategies pivot, BAs adapt, keeping their knowledge fresh and relevant. And unlike static brand guidelines that quickly become outdated, Brand Agents can be continuously updated with the latest campaign results, consumer feedback, and market research – ensuring everyone in the organization is working with the most current brand intelligence. 

To be clear: Brand Agents aren’t here to replace human creativity or strategic thinking. They’re here to amplify it. By handling the heavy lifting of information retrieval, pattern recognition, and initial content generation, they free our teams to focus on what humans do best: the creative leaps and strategic insights that make brands unforgettable. 

Context is Everything 

We do a lot of exploring of new AI apps as part of our commitment to understanding and innovating agency work. And here’s one thing we’ve noticed: AI tools, backed by well-curated contextual information, are becoming a genuinely new way of getting good work done. And in advertising, when an AI really understands a brand’s history, voice, and goals, it transforms from a generic chatbot into something much more interesting – a specialized collaborator that can actually help create brand-aligned work. 

This is an insight that grew out of Mod Op’s AI Playground. Through that initiative, we’ve been experimenting with tools like Google’s NotebookLM and Cursor (which, fun fact, is the fastest growing SaaS application ever). Through that experimentation, we saw an opportunity: what if we could collect all our brand knowledge and let our teams access it through simple chat? It could democratize brand expertise across our entire organization in ways that weren’t possible before. 

And working this way wouldn’t just boost efficiency – it could elevate quality across the board. A junior copywriter could tap into brand understanding that used to take years to develop. A strategist could quickly explore multiple approaches grounded in the brand’s established positioning and pre-test it with a simulated focus group based on real audience data. A creative director can iterate rapidly while staying perfectly aligned with brand guidelines. 

But doing this at an agency scale? That’s trickier. We needed something that could handle dozens of brand identities, provide secure access to team members with differing degrees of access – and fit seamlessly into our existing workflow. 

So, we built it. 

The Challenge 

Here’s the thing about enterprise AI adoption: the biggest hurdle isn’t the technology itself, or even getting the data organized – it’s security. When we talked to our clients about AI, this came up again and again. And they’re right to worry. 

Take the popular AI tools like ChatGPT and Claude. They operate on a simple deal: you feed them data, they use it to get smarter. Every conversation, every bit of proprietary information potentially becomes part of their training set. For agencies handling sensitive client information – unreleased products, marketing strategies, competitive analyses – that’s a non-starter. 

Then there’s the hallucination problem. When an AI confidently presents fiction as fact, it’s more than just annoying – it’s dangerous. One wrong detail in a client presentation, one misquoted statistic, and you’ve got a mess on your hands. Generic AI tools, for all their capabilities, just don’t have the specialized knowledge to reliably support good agency work. 

We needed something better: AI assistance without the security nightmares and reliability issues. We looked around, but couldn’t find it, so we built it ourselves. Enter Brand Agents. 

Starting Fresh 

Talk about “democratizing” access to information has become one of those empty tech buzzwords. But here’s where Brand Agent actually makes good on that promise: it fundamentally changes how knowledge flows through our agency, from day one of any client relationship. After all, when is solid intel on a new client brand more valuable than when we’re all getting to know each other? 

Before we have our first presentation, we create a Brand Agent. We feed it a mix of publicly sourced research and agency insights, giving it a deep understanding of the client’s brand and a bit of our own POV. By the time we get together to brief the team, they can explore the client’s industry, competitors, and audience through natural conversation, getting answers that would normally require weeks of research or months of experience. 

The result? First client meetings that feel like fifth meetings – focused on solutions rather than basic fact-finding. Skip the awkward getting-to-know-you phase and dive straight into the good stuff. And it just compounds from there. 

AI Collaboration That Actually Works 

In agency life, “I need more time to think” is generally met with “there isn’t any.” Now, there’s hope. Brand Agents can create breathing room that agency teams desperately need. 

When a designer hits a wall, Brand Agent can suggest new directions based on brand history and audience insights. When a copywriter needs that perfect phrase, it can offer variations that keep the brand voice while exploring new territory. When an account manager gets that urgent client email at 5 PM, they can pull together a document in minutes instead of hours. 

But here’s where it gets really interesting: Brand Agents break down traditional agency silos –  the invisible walls between strategy, creative, media, tech, and account teams. Instead of islands of expertise connected by bridges of miscommunication, we have a central source of brand knowledge everyone can tap into. Junior team members don’t have to wait years to develop sophisticated strategic perspectives. Creative teams can explore consumer behaviors without waiting for formal research or needing to reach out to the media team for performance insights. Account managers can answer client questions that once required a round of internal emails. This isn’t just efficiency, it’s empowerment. 

AI that Actually Delivers Value 

Brand Agent represents something new in client-agency partnerships: AI that actually  delivers value. By handling time-consuming research, tactical exploration and initial content development, we can focus more human energy on the creative and strategic work that will only matter more in 2025 and beyond. By aligning behind brand priorities and strategic objectives in everything we do, we deliver more value. In other words, we’re combining human creativity with technology to create that “unfair advantage” our clients come to us for. 

And we’re just getting started. 

Learn more about Brand Agents.