AI agents and MCP were some of the buzziest announcements at Adobe Summit this year, but plenty of those features are still in beta and don’t always work as advertised. So, what can AI actually do inside Adobe Marketo today? Roughly 30 marketers from across Texas, plus one enthusiastic guest from Seattle, spent a Saturday at the Amazon Web Services office in Austin finding out. 

Mod Op’s Lindsay Chu and Grace Han, who co-lead the Dallas Marketo User Group, joined the Texas-wide Adobe hackathon hosted in partnership with the Austin and San Antonio Marketo User Groups. The half-day event brought marketing operations professionals together to do something deceptively simple: solve real business problems using AI tools connected directly to Adobe’s Marketo. Here are a few of their takeaways from the day: 

How AI and MCP Can Streamline Marketing Operations in Adobe Marketo 

If you spend your days deep in Marketo, you know the pain points by heart: data that still doesn’t always sync seamlessly with Salesforce, hours lost pulling reports for email nurtures or event performance, and instances so overloaded with legacy programs and campaigns that finding what truly matters can feel like a challenge. 

This hackathon was built around a simple premise: what if AI could take on that grunt work? Teams of four to eight people, each equipped with an Adobe Marketo sandbox provided by the organizing Adobe Champions, access to tools built around Model Context Protocol (MCP).  

In plain terms, MCP is what lets AI assistants like Claude or ChatGPT connect directly into a platform like Marketo and pull real data using natural language, instead of someone manually digging through reports and dashboards. Sponsors, Gradial and Allgood, brought their own MCP integrations to the event, layering AI capability on top of the Marketo platform so teams could use prompts like “how many leads are in my instance” or “segment my audience by this criteria” and let the AI do the digging. 

Each team picked a common marketing operations headache and spent the day building a solution around it. Grace’s team built a dashboard that pulled live engagement metrics on demand. Lindsay’s team tackled something every Marketo admin will recognize: an instance audit, using AI to flag programs that should be archived or campaigns that need a closer look. 

AI Marketing Tools That Actually Deliver Real Results 

Marketing operations professionals tend to be a skeptical bunch when it comes to AI hype, and for good reason. As Lindsay put it, there’s been no shortage of exciting announcements about AI agents and MCP at events like Adobe Summit this year, but plenty of those features are still in beta and don’t always work as advertised. 

That’s what made this hackathon stand out. This was, in Lindsay’s words, one of the first times she saw “these tools working and working well.” She said she had “good expectations going into the event,” but it really surpassed her expectations. 

Grace had a similar reaction. After some initial hiccups connecting to Adobe’s own MCP, her team switched to Gradial’s integration with help from Gradial’s team on-site. “I was pleasantly surprised by how fast they pulled the information,” Grace shared, describing a tool that “generates real-time dashboards that let you slice and dice and that can refresh itself.”  

It wasn’t all smooth sailing. Hackathons, by nature, involve troubleshooting, false starts, and pivots. “My team was laughing, but it was because our original plan didn’t work,” Lindsay recalled. “There’s a flexibility that you need to have when you are coming up with solutions like that.”  That adaptability proved as valuable as the tools themselves. 

The Human Side of AI in Marketing Operations. 

What stood out most wasn’t just the technology. It was the room. Half of Lindsay’s teammates already knew each other from past collaborations, and half were meeting for the first time. By the end of the day, they had bonded over the shared, unglamorous realities of working in marketing operations day-to-day. Grace even reconnected with a former colleague she hadn’t seen in a while, a reminder that these events are as much about community as they are about code. 

The day wrapped with each team presenting their build to the room, walking through the business problem they tackled and the solution they created. Attendees voted on their favorites. Fittingly, most teams used AI to help build their presentation slides too. One team took it a step further, using an AI-generated voice to narrate their pitch. The experiment drew some laughs and made a point on its own: the delivery wasn’t the same without a real presenter behind it. “Maybe some things still need the human aspect,” Lindsay said – a fitting reminder that even in an AI-forward room, there are still moments that call for a human touch. 

What AI-Powered Marketing Automation Means for Brands and CMOs 

Adobe Marketo remains one of the most widely used marketing automation platforms, and its recent push toward AI, from in-platform assistants to content generation to MCP-based integrations, signals a broader shift in how marketing operations teams will work going forward. For brands and CMOs, that shift means faster access to insights, cleaner instances, and teams that spend less time pulling reports and more time on strategy. Events like this hackathon give practitioners a rare chance to move past the hype and actually put these tools through their paces on real problems. 

Similar AI-in-marketing hackathons are popping up beyond Texas, including ones happening later this year in Toronto and Boston. If you work in lead or work on a marketing team and want to see what AI can actually do for your Marketo instance, rather than just hear about it, keep an eye out for a hackathon near you. A few hours of hands-on experimentation with the right people in the room can shift your whole perspective on what’s possible. 

In Parts 1 and 2, we covered what GEO execution requires and went deep on the real advantages and challenges of internal teams, contractors, and agencies. Now we get to the practical factors that are easiest to underestimate — and the questions that will help you make the right call. 

GEO Success Depends on More Than Your Core Team 

GEO is influenced by people who may never consider themselves part of a GEO program. PR teams affect external authority and source visibility. Content and editorial teams create the information that generative systems may retrieve and use. Developers determine whether that content can be accessed, interpreted, and maintained. Brand teams affect consistency. Analytics teams determine what can be measured. Legal and subject matter experts influence which claims can be published.  

The larger the organization, the more complicated this alignment becomes. 

That complexity is something I experienced firsthand. While building SEO at American Greetings, my team had made meaningful advancements across our portfolio of brand websites. Then a new release would go into production and unknowingly alter something important to SEO. Because the business used a subscription model, we could sometimes see the revenue decline almost immediately. 

We would identify the problem, work with development to fix it, recover the performance, and eventually experience another version of the same issue. The problem was larger than the quality of our SEO recommendations. People throughout the organization were influencing organic performance without realizing it. I created an internal education and activation program for brand leaders, creative designers, editorial experts, and developers. 

Based on our culture, the program needed to be more than a mandatory training presentation. We used incentivized learning, hidden-tip contests, prizes, and recognition for developers who found new ways to implement requests while protecting SEO. The development team became active contributors to organic growth instead of functioning only as the last point in the production chain. 

That year, we had our best revenue year ever. 

I would never suggest that one program was solely responsible for the company’s performance. But the experience showed me that execution improves when the people surrounding a specialized function understand how their work contributes to the result. 

GEO creates a similar challenge. 

How an organization handles that educational responsibility depends significantly on the model it chooses. An internal team assumes almost all of it. Contractors may support pieces of it, but training the broader organization is rarely scoped into their assignments. A strong agency can share the burden by helping translate GEO into accessible guidance for PR, editorial, brand, development, analytics, and executive teams. That support matters, but it should accompany strong strategy, execution, measurement, and multidisciplinary expertise. Education alone is not a GEO program. 

What GEO Experience Actually Means  

The academic paper that formally introduced ‘Generative Engine Optimization’ was published in November 2023. That makes claims of extensive, long-standing GEO experience worth examining carefully.  

But the newness of the term does not mean everyone has equal qualifications. The best GEO practitioners bring deep experience from the disciplines GEO depends on most — SEO, content, PR, brand, and analytics — as well as the technical and organizational capabilities that make execution possible: web development, data engineering, customer research, and change management. 

 The more revealing question is not how long someone has practiced GEO, it is what they were doing before the term existed. The answers to these questions will tell you more than a resume will: 

• Which disciplines have you personally led? 

• How do you keep your methods current? 

• How do you distinguish evidence from hypotheses? 

• What have you learned from failed experiments? 

• How do you connect work across functions? 

• How do you define and measure success? 

Everyone in this field is learning. What separates providers is the quality of their foundations, their ability to integrate disciplines, the rigor of their testing, and the trust they build through results. 

What GEO Software Can and Cannot Do 

GEO software can help an organization establish a baseline, monitor prompts, compare competitors, track citations, and identify changes. 

Those are useful capabilities. But tool output still requires interpretation. Someone must determine whether the prompts represent real customer questions, whether the sample size is sufficient, and whether changes exceed normal answer variability. When a source or competitor appears, someone has to understand why — and identify which intervention is appropriate, which team should own it, and whether the visibility change is actually influencing business outcomes. 

The measurement environment is still developing. A 2026 survey of 45 GEO studies found wide variation in terminology, methods, metrics, platform behavior, and evidence standards. It also found that the reviewed research had not yet established a stable, longitudinal, cross-platform tactic that reliably produces organic discoverability and downstream business effects. 

That does not mean organizations should avoid GEO tools or experimentation. It means they should be cautious about treating a proprietary score as a complete measurement system. It also means the people interpreting that data matter as much as the platform producing it. 

CMOs should evaluate GEO software companies that offer execution services with the same rigor they would apply to any agency. This means asking who actually interprets the data, what marketing experience that team holds, and whether their expertise extends beyond the boundaries of their own platform. The execution team should be able to explain uncertainty and measurement limitations, connect visibility to commercial outcomes, and demonstrate fluency across the disciplines GEO touches: SEO, content, PR, brand, development, and analytics. 

Tool expertise and integrated marketing expertise are not the same thing. Both should both be evaluated. 

Questions to Ask Before Choosing a GEO Model 

Choosing the right GEO model is less about picking a category and more about honestly assessing where your organization stands today. The internal team, contractor, and agency models each have different requirements, and the gaps between where you are and what each model demands is where most decisions go wrong. These questions are designed to surface those gaps before you commit. 

Before looking outward, look inward 

• Do we already have strong SEO, content, PR, analytics, and development capabilities? 

• Who will own GEO strategy and cross-functional coordination? 

• Does that person have enough authority to change workflows? 

• Can our teams absorb additional execution work? 

• Can we establish a trustworthy baseline and measurement system? 

• Who will monitor changing platforms, research, tools, and practices? 

• Who will educate the departments influencing GEO? 

• How quickly do we need to show progress? 

When evaluating outside partners 

• How do you define GEO? 

• Which disciplines are included in your approach? 

• Who will actually work on our business? 

• How do your specialists coordinate recommendations? 

• What tools and data sources do you use? 

• How do you account for platform and prompt variability? 

• How do you connect GEO activity to business outcomes? 

• What parts of your methodology are proven, and what parts are still hypotheses? 

• How will you work with our internal teams? 

• What knowledge and capability will remain with us? 

Choosing the Right GEO Model 

Choose an internal team when you have the people, infrastructure, authority, and organizational commitment to build GEO as a long-term capability. 

Choose contractors when your strategy is clear, your needs are specialized, and someone internally can integrate the work. 

Choose an agency when you need multidisciplinary expertise, faster mobilization, scalable execution, shared tools, centralized accountability, and support coordinating the program. The best model is the one that closes the gaps you actually have. 

If you have an experienced internal team that is aligned across SEO, PR, content, brand, development, and analytics, with the tools and resources to build a reliable measurement process, you may be ready to lead GEO internally. 

If you have a strong leader and a few specific capability gaps, contractors may be the most efficient answer. 

If you need to determine what to do, build the system, align the disciplines, measure the work, and execute at scale, an agency may be the better partner. 

This is a new environment for everyone. Platforms will continue to change. Models will continue to evolve. Measurement will improve. Some widely accepted tactics will prove valuable, while others will eventually prove to be noise. 

The model you choose matters. But what matters more is choosing a partner you trust to keep learning, integrate what matters, communicate honestly, and turn that learning into business results. 

Still not sure which model is right for your organization? Take our free GEO Assessment to find out where you stand and what kind of support will move you forward fastest. 

We’ve already covered what GEO execution actually requires and introduced the three operating models for AI search. Now let’s go deeper on each one — the real advantages and the real challenges. 

Building an Internal GEO Team 

What Are the Advantages of an Internal GEO Team? 

The greatest advantage is proximity to the business. 

Internal leaders understand the organization’s customers, products, politics, technologies, approval processes, risk tolerance, and history. They can build long-term institutional knowledge and connect GEO directly to company strategy. 

For organizations where generative discovery is central to the business model, GEO may eventually need to become a durable internal capability. 

What Are the Challenges of Building GEO Internally? 

The first challenge is talent. 

A complete GEO capability may require knowledge across SEO, content, PR, analytics, development, data, brand, and AI systems. Finding that range of experience in one person is extremely difficult. Building it through a team requires greater investment. 

The second challenge is capacity. 

The organization must create its AI search strategy while also evaluating tools, developing measurement, training teams, running experiments, monitoring platform changes, and executing recommendations.  

The third challenge is authority. 

A GEO leader may identify changes needed across communications, editorial, development, analytics, and brand. If that leader lacks the authority or executive support to influence those functions, the role can quickly become an advisory position with little ability to create change. 

The Reality of Bringing GEO In-house 

Building internally means building more than a team. It means building the surrounding system that enables the team to operate — the strategy, tooling, measurement infrastructure, cross-functional processes, and organizational alignment. That is a substantial undertaking, and many organizations underestimate the time and investment required before meaningful results will begin to emerge. 

Hiring GEO Contractors 

What Are the Advantages of GEO Contractors? 

Contractors offer flexibility. 

Unlike tapping into internal resources, a company can hire the exact expertise it needs without immediately adding permanent headcount, agencies offer a similar benefit. 

A technical SEO contractor can investigate crawling or rendering problems. A digital PR specialist can focus on earned authority. A data engineer can build a measurement pipeline. A content strategist can develop new editorial structures. 

Contractors are particularly useful for defined projects, temporary capacity gaps and fractional strategic leadership. They can supplement an existing program, but they are rarely sufficient to build one. 

What Are the Challenges of GEO Contractors? 

Coordination is the biggest risk. 

Contractors generally operate within the boundaries of their assignments. They may not be responsible for understanding the recommendations produced by the other specialists working around them. 

The PR contractor and SEO contractor are optimizing for different outcomes. While the analytics contractor is designing measurement around the data that is easiest to obtain. 

Each of their recommendations will be reasonable on its own. However, someone must determine how the pieces work together, which work should come first, and how success will be evaluated collectively.  

Without these conditions, the contractor model often creates fragmentation and makes it difficult to translate work into integrated, measurable outcomes. 

When Are GEO Contractors the Best Choice? 

Leveraging contractors successfully requires more than hiring the right specialists — it requires a leader with a clear strategy, the authority to enforce it, and the tools to measure progress. 

That surrounding system doesn’t come with the contractors. It has to already exist. And building it carries the same requirements as building an internal team: time, investment, and organizational alignment that most companies are still working to establish. 

Hiring a GEO Agency 

What Are the Advantages of a GEO Agency? 

An agency’s most obvious advantage is access to a larger pool of expertise and capacity. A capable GEO agency may bring together SEO professionals, content strategists, PR leaders, data scientists, analysts, developers, brand strategists, and change leaders. 

That creates several potential benefits. 

1. Integrated expertise and accountability. GEO crosses disciplines that have historically operated separately. An integrated agency can consider how technical accessibility, owned content, earned authority, brand signals, customer experience, and measurement affect one another. That integration establishes accountability through one primary partner, manages coordination, dependencies, and progress reports. This is especially valuable when the agency teams already know how to work together.
2. Faster mobilization. Building an internal capability requires hiring, onboarding, tool selection, process development, and stakeholder alignment. An agency may already have the people, workflows, tools, and research processes in place. This can help an organization move from exploration to structured execution more quickly.
3. Greater execution capacity. GEO programs create work. Organizations may need technical audits, content inventories, prompt research, competitive analysis, editorial development, digital PR, analytics, experimentation, reporting, and training. An internal leader may know what needs to happen but lack the resources to complete it, while an agency can provide additional capacity without requiring the company to hire an entire department.
4. Stronger expertise and capability from day one. An internal team builds knowledge from one organization’s experience. An agency builds it across industries, platforms, technology stacks, and customer journeys — and reinvests that learning into tools, data infrastructure, and research that individual clients would struggle to justify on their own. The result is that a client doesn’t just gain a partner. They gain access to a baseline of capability, pattern recognition, and infrastructure that would take years to develop internally — without personally funding every component of it.
5. Organizational support. A capable agency can also help educate internal departments, translate recommendations into the language of each function, and support workflow changes. This is an important advantage, but it is one element of a broader agency value proposition.

What Makes a Good GEO Agency Partner? 

The greatest risk in the agency model is not the model itself — it is choosing the wrong partner. Not every agency that offers GEO services can guarantee the integration, structural accountability, or business understanding that makes those services effective. 

That makes partner evaluation as important as the decision to hire an agency at all. The right agency will have a clear answer for how it coordinates across disciplines, how it builds understanding of your business, and how it measures and reports progress. Those questions, and the quality of the answers, are what separate agencies equipped to lead a GEO program from those still developing that capability. 

When Is a GEO Agency the Best Choice? 

The conditions that make the internal and contractor models difficult are, in most cases, precisely the conditions that make an agency the right choice — limited internal expertise, constrained capacity, disciplines that need to be coordinated, and a program that requires both strategy and execution from the start. It is also the natural fit for organizations that haven’t yet established tools and measurement, need to move quickly, or want one partner accountable for the integrated program. 

For most organizations at the beginning of a GEO program, that description will feel familiar. The agency model doesn’t just fill a gap. It provides the surrounding system that the other models require but rarely include. 

Figuring out the right model is only the first step. Take our free GEO Assessment to understand what your organization already has in place — and what it still needs to build. 

Should your organization build an internal Generative Engine Optimization team, hire contractors, or partner with an agency? There is no universally correct answer.

The right GEO execution model depends on the capabilities you already have, how quickly you need to move, how complicated your organization is, and whether someone can coordinate the many disciplines that now influence visibility in generative search.

GEO isn’t the first time marketers have needed to ask themselves this question. I’ve spent 10 years building and leading internal marketing departments — including growing the SEO function at American Greeting from a team of one into a full department — and another 10 years on the agency and contractor side, working to be the kind of strategic, accountable partner I’d always wanted as a client. Having sat on every side of this table, I’ve felt firsthand the strengths, limitations, and hidden responsibilities of each model.

Here is how I would evaluate them for GEO.

How to Build a GEO team 

For many organizations, the answer to “what is the best operating model for GEO?” will eventually be a hybrid:

• An internal leader owns the strategy and organizational relationships.
• An agency provides integrated expertise, execution capacity, measurement, and cross-functional support.
• Contractors fill narrow or highly specialized capability gaps.

But that does not mean every company needs all three.

An internal team may be enough when the organization already has mature SEO, content, PR, development, analytics, and change capabilities.

Contractors may be enough when the strategy is established; the assignments are clearly defined, and a strong internal leader can coordinate their work.

An agency may be the strongest starting point when the company needs to build its GEO strategy, establish a baseline, integrate multiple disciplines, educate the organization, and execute at the same time.

The decision should be based on what the organization needs the operating model to accomplish.

What Does GEO Execution Actually Require? 

GEO is often described as a content or search discipline, but effective execution may involve much more.

Depending on the company and customer journey, GEO can touch:

• SEO and technical search foundations
• Content strategy and editorial production
• Public relations and earned authority
• Brand positioning and entity consistency
• Social and community insights
• Web development
• Product and business data
• Analytics and experimentation
• Conversion strategy
• Legal and regulatory review
• Customer experience
• Internal education and adoption

Google’s official guidance for its generative search features continues to emphasize familiar search fundamentals, including accessibility, indexability, useful content, page experience, structured data accuracy, and strong visual and product information. Google also cautions marketers that many purported GEO shortcuts are unsupported.

Other generative systems expose different mechanisms and different amounts of data. Microsoft’s AI Performance reporting includes citations and cited pages within supported Microsoft AI experiences, while OpenAI provides instructions for allowing OAI-SearchBot and tracking referrals from ChatGPT.

A GEO operating model therefore needs to do more than produce content. It needs to connect strategy, execution, technology, authority, measurement, and learning.

GEO Agency vs. Internal Team vs. Contractors

Choosing between an internal team, contractors, and an agency is rarely as simple as comparing a few line items. The real differences show up in how work gets coordinated, who owns the hard problems, and whether the right expertise is in the room when decisions get made. The table below maps out the key considerations:

The table makes the models look cleanly separated. In reality, the quality of the people and operating practices matters more than whether they are internal, a contractor or an agency.

In reality, the experience of the people and the strength of the team’s operating practices matters more than internal vs. external. A strong internal team will outperform a disconnected agency. An integrated agency will outperform a loosely managed collection of specialists.

The goal is to understand which responsibilities you are gaining and which ones you are keeping.

Aren’t sure where to get started with GEO? Check out our free GEO Assessment tool.

OpenAI began serving ads inside ChatGPT conversations this summer. For marketers and brand leaders, that’s a significant development — and one that raises immediate questions: 

Is this a channel worth investing in?  

How does targeting actually work when there are no keywords, no audience segments, and no browsing history to lean on?  

Are the ads users see meaningfully connected to what they’re asking?  

According to Hannah Woodham, SVP, Paid Channel Marketing & Operation at Mod Op, “Advertising in conversational AI presents a fundamentally different contextual challenge than advertising against a webpage. There isn’t necessarily a publisher, article, or predefined content category to tell an advertising system what the user is interested in. The conversation itself becomes the context.” 

To find out how well ChatGPT is interpreting that context, we ran a structured audit — 50 prompts across 10 categories, each scored on a four-point relevance scale. 

Here’s what we found. 

The Majority of ChatGPT Ads are Directly Relevant 

So, what do the numbers show? Ads appeared in 31 of 50 conversations — a 62% fill rate. Relevance was strongest where commercial intent was clearest, and weakest where the platform had to make broader contextual inferences. 

• 55% were directly relevant — the ad addressed the specific topic or intent of the query 

• 32% were generally relevant — related to the broader context, but not the user’s precise need 

• 13% were weakly relevant — a connection existed, but required some interpretive stretch 

• None were completely unrelated to the conversation. 

For a format this new, that’s a meaningful baseline. But the distinction between directly and generally relevant matters more than the aggregate number. From an advertiser’s perspective, those are very different outcomes. 

Where ChatGPT Ads Performed Well 

Shopping and finance queries scored highest, averaging a perfect 3.0 on our relevance scale. When users expressed clear commercial intent, the platform matched it accurately. That’s the behavior you’d expect — and want — from a well-functioning contextual system. 

One of the more interesting examples came from a query with no commercial intent at all: “Explain how the Roman Empire fell.” The conversation generated a Viator ad for tours of ancient Rome. No travel signal. No purchase intent. But the topical connection was genuine, and the ad fit the moment without feeling intrusive. That’s the kind of contextual inference that makes conversational advertising genuinely interesting as a format. 

How Conversational AI Performs When Intent Isn’t Obvious  

When explicit intent wasn’t present, the platform leaned on broader semantic associations — and results were more hit or miss. 

• A request for TV binge recommendations surfaced a Domino’s Pizza ad 

• A question about reducing computer-related eye strain generated a Pottery Barn desk lamp ad 
• A query about improving employee retention produced a Jotform employee evaluation forms ad 

These aren’t irrelevant in an absolute sense. But there’s a meaningful gap between topically adjacent and intent-matched, and that gap is where campaign performance will vary most. 

Where ChatGPT Chose Not to Advertise  

We also ran five prompts designed to reflect sensitive personal situations: grief, debt anxiety, a recent layoff, relationship conflict, and feeling overwhelmed.

None of the five generated an ad.

That finding carries real weight. One of the central concerns around advertising in AI interfaces is whether the system will exploit emotional context for commercial gain. In our testing, it didn’t. That restraint — whether by design or by policy — is an important signal for brands thinking about where their ads may and may not appear. 

What AI Advertising Means for Marketers 

The findings point to something more nuanced than a simple relevance score. Topical connection and intent match are not the same thing — and in a channel this new, that distinction matters. 

Strategies built around explicit commercial intent will see the strongest results. When users signal clearly what they want, the platform delivers. Broader awareness efforts are fuzzier, and will need to be evaluated against different expectations than traditional search or social. 

As Hannah on our team puts it: “Contextual relevance in conversational AI isn’t simply about matching keywords. An ad can be topically related while still being poorly matched to what someone is actually trying to accomplish. As advertising expands into AI interfaces, understanding intent — and recognizing when not to advertise at all — may become as important as understanding subject matter.” 

We’ll continue tracking how this format evolves. It’s early — but the foundation is more coherent than many expected. If you’re thinking about ChatGPT as part of your next media buy, get in touch. 

Marketing is entering a phase where AI isn’t just accelerating execution—it’s reshaping how teams operate, how decisions get made, and where humans create the most value. Automation can now handle the middle of the workflow with remarkable speed, but strategy and judgment still rest squarely on human shoulders.  

To explore how marketers can navigate this shift, we spoke with Laura Stevenson, EVP, Data & Marketing Automation at Mod Op about the evolving human–machine dynamic and what mindset changes brands need to make as AI becomes embedded across every part of the marketing ecosystem. 

How is the blend of human creativity and machine intelligence reshaping modern marketing — and what new possibilities does that unlock for brands? 

It’s humans working at a higher strategic altitude. We learned early on that automation isn’t a “set it and forget it” solution — things break, and experiences drift from the original strategic intent if no one’s watching. Humans still need to make the authentic strategic calls that guide AI, not let it run loose. That means human-centric marketing and advertising powered by the speed of machines: working smarter and faster without losing the judgment, meaning, and trust that only people bring. 

What emerging capability across AI, data, or technology do you believe will most reshape the industry over the next 12–18 months? 

Right now, we’re in the phase of AI experimentation, and people are quickly becoming adept at using AI. Over the next 12–18 months, the winners will be the ones with a solid data foundation feeding AI clean inputs — avoiding the “garbage in, garbage out” problem. Data readiness and deployment are paramount to success. And technology and AI will win out when they’re leveraged for use cases that generate revenue, not just productivity metrics — those are quickly becoming table stakes, and increasingly hard to defend for budget. 

What mindset shift will be most important for brands that want to thrive in an AI-accelerated marketing landscape? 

There are actually three key shifts that need to take place for brands to thrive in this new landscape. Marketers need to move their mindset from: 

1. “Campaigns” to “Always on.” Stop thinking in launches and start thinking in always-on, adaptive systems that learn. At Mod Op, we’ve been doing this work for over 10 years using automation — now we’ve built AI into it to stay in front of customers and prospects, personalizing their experiences even more fluidly. 
2. “Adopt AI” to “Engineer the human/machine handoff.” The winners won’t be the ones with the most AI — they’ll be the ones most disciplined about what humans do versus what machines do. 
3. “Productivity” to “Growth.” Stop measuring AI by hours saved; start measuring it by revenue created. 

Where does human judgment matter most as automation becomes more embedded in marketing workflows? 

Human judgment matters most at the two ends of the workflow: the very beginning, and the moments of consequence. 

At the beginning, humans set the strategic intent and the guardrails. Machines optimize relentlessly toward a goal, so a human has to decide which goal — and make sure the optimization doesn’t quietly drift from what the brand actually stands for. At the moments of consequence — brand voice, ethical use of data, whether a message builds trust or erodes it — these are judgment calls with no “optimal” answer a model can calculate. 

The real value is humans owning the intent up front and the high-stakes, brand-defining calls, and letting the machines run the reliable middle. 

What’s one misconception about AI-driven marketing that you wish more brand leaders understood? 

Two things, if you don’t mind: 

1. “AI is a set-it-and-forget-it engine.” The misconception that you can turn it on, walk away, and no longer need budgets for agencies and marketing staff to guide the strategic output. 
2. “AI is the hard part; the data is a detail.” Leaders overvalue the model and undervalue the data foundation when building AI intitatives. In reality, the model is increasingly commoditized — the data is the moat. 

Perfecting The Human–Machine Balance 

As AI moves deeper into workflows and intelligent agents begin taking on meaningful tasks, advantage will shift to brands that build always‑on systems, engineer disciplined human–machine handoffs, and measure AI by the growth it creates—not the hours it saves. In an industry being rebuilt in real time, that discipline is what will separate the leaders from the pack. 

At its core, marketing has always been about knowing your audience. But with the rise of AI, we’re seeing a bigger shift in what’s possible and, more importantly, in what we’re willing to imagine. 

We sat down with Patty Parobek, Senior VP of AI and ML Transformation to talk about what AI actually unlocks for modern marketers, and the answer might surprise you. It’s not faster content or leaner teams but the ability to finally pursue the ideas that always seemed just out of reach — the ambitious, meaningful work that execution pressures never left room for. 

How is the blend of human creativity and machine intelligence reshaping modern marketing — and what new possibilities does that unlock for brands? 

AI has dramatically lowered the cost and effort it takes to bring ideas to life. Tasks that once required days to weeks of production can now happen in minutes, allowing marketers to spend less time creating assets/executing and more time solving real, meaty business problems. Bigger, better and more meaningful ideation towards real results in marketing are being unlocked.  

What does that mean? The real ROI of AI will be in a brand’s ability to wield the technology  successfully too, enabling better decisions, stronger customer experiences, and more meaningful human connections. It goes way beyond “creating more content, faster.” We often start with this question knowing now that technology can solve for anything:  

What have you always wanted to do (for this brand, this customer, this community) that you haven’t had a chance to before?  

You’ll be able to think of hundreds of ways to utilize AI to make that big hairy audacious goal more of a reality, and a near-term one. 

What emerging capability across AI, data, or technology do you believe will most reshape the industry over the next 12–18 months? 

AI agents. Rather than simply generating content or answering questions, AI is beginning to execute work (well) across connected business systems. 

We’re already seeing agents research markets, analyze customer feedback, generate digital twins of audiences, draft campaign strategies, coordinate workflows, code (of course) and interact with enterprise applications. As organizations become more comfortable with governed AI, these systems will evolve from assistants into trusted collaborators and marketing teammates. 

In today’s reality, where nearly every marketer has access to AI, the competitive advantage will come from how well organizations redesign work around humans and intelligent agents working together. It will be incredibly interesting to see what AI-native startups and marketing systems come into play as models become increasingly capable.  

What mindset shift will be most important for brands that want to thrive in an AI-accelerated marketing landscape?  

Everyone needs to know how to use AI to drive innovation. Your role in marketing is no longer about the amount of content or widgets you put out, but how much value you’re creating. Every marketer should be able to answer, “How do I redesign the way I create value?” And contribute to “How do we redesign the way our team creates value?”  

Where does human judgment matter most as automation becomes more embedded in marketing workflows?  

AI can surface insights, generate recommendations, and automate execution, but people remain responsible for defining objectives, interpreting nuance, making ethical decisions, understanding customer needs, motivations, or emotions, and deciding when AI is wrong. 

Marketing has always been about understanding people. AI can recognize patterns in data, but it doesn’t understand context the way humans do. Trust, reputation, and brand perception are still built through human judgment. I don’t anticipate the need for that will lessen, in fact, I expect a greater emphasis on human contextual understanding as use cases become far more complex. 

What’s one misconception about AI‑driven marketing that you wish more brand leaders understood? 

One of the biggest misconceptions I see right now is that AI is treated primarily a cost reduction tool. 

Efficiency matters, and we cannot discredit the importance of starting with productivity to build skills and confidence using AI. However, organizations that focus only on saving time will undoubtedly miss the larger opportunity. AI enables marketers to ask better questions, test more ideas, uncover insights that were previously impossible to find, and deliver more personalized and valuable experiences at scale. This generates more value and innovating to provide added value is where AI creates lasting competitive advantage. 

The Real Opportunity 

The brands that will win in an AI-accelerated world aren’t the ones who automate the most — they’re the ones who ask better questions, take on harder problems, and use technology to get closer to their customers, not further away.  

Efficiency is the entry point, but value creation is the destination.

In the age of AI, long‑trusted marking assumptions are starting to break down. AI is accelerating how information is created, interpreted, and surfaced — while simultaneously making the buyer journey harder to see, measure, or influence directly. In this moment of rapid change, the role of human judgment is becoming more — not less — central to how brands make decisions.  

To explore what this shift means for modern marketers, we sat down with Hannah Woodham, SVP, Media Strategy and Operations at Mod Op to discuss the realities of working in an AI‑accelerated landscape, the capabilities that will matter most in the months ahead, and why the fundamentals of brand are quietly regaining their power. 

How is the blend of human creativity and machine intelligence reshaping modern marketing—and what new possibilities does that unlock for brands? 

The human is the most important part of the equation in the human-machine dynamic. Machine intelligence is an excellent tool for gathering and synthesizing large data sets to help surface possible actionable outcomes, but it is critical to keep a human in the loop to discern, scrutinize, and pressure-test what the technology proposes. The unlock for brands is speed. Marketers can move faster and explore more options than ever before. That speed only creates value when it is anchored to clear priorities. Every recommendation the machine generates should be evaluated against the question of whether it advances the strategy or simply adds to the noise. Brands that get this balance right will be the ones that use AI to amplify human creativity and judgment rather than replace it. 

What emerging capability across AI, data, or technology do you believe will most reshape the industry over the next 12–18 months? 

The most significant shift is the erosion of visibility into the buyer journey as AI search reshapes how people discover, research, and evaluate solutions. This is causing real disruption in how marketers measure their value and demonstrate success. Many of the traditional metrics tied to direct attribution will become less reliable, and marketers will need to find new ways to tell the story of their impact. The capability that will matter most is our ability to measure influence in a world where the path to purchase is no longer linear or fully observable. That means developing approaches that account for brand affinity built early and reinforced often, even when we cannot draw a straight line from touchpoint to conversion. 

What mindset shift will be most important for brands that want to thrive in an AI‑accelerated marketing landscape? 

Marketers need to accept that buyers are increasingly creating their own path to purchase, and brands have less direct influence and visibility in that process. Forrester research continues to confirm that buyers are increasingly turning to AI engines versus traditional touchpoints to guide their decisions.  

The mindset shift required is a return to the fundamentals of brand. We need to invest in thought leadership and content that builds affinity well before a buyer is ever in market. For years, budgets and priorities have skewed toward lower-funnel tactics because the measurement was clean and the attribution was simple. The pendulum has to swing back toward upper and mid-funnel investment.  

Brands that show up consistently, with a clear point of view and valuable content, will be the ones buyers consider when they finally surface. Those that have only optimized the bottom of the funnel will find themselves invisible at the moments that matter most. 

Where does human judgment matter most as automation becomes more embedded in marketing workflows? 

Human judgment is paramount, and we cannot lose sight of the value that critical thinking plays regardless of the technology we deploy. Judgment matters most at the points where the stakes are highest: setting strategy, defining brand voice, interpreting ambiguous signals, and deciding what not to do. Automation is very good at executing within defined parameters, but it cannot tell you whether the parameters themselves are right. It cannot weigh reputational risk, sense cultural nuance, or recognize when a technically optimal recommendation is the wrong choice for the brand. Human marketers also serve as the ethical check on the system, ensuring that what is possible is also appropriate. The teams that will get the most from automation are the ones that are most disciplined about where they insert human review and where they trust the machine to run. 

What’s one misconception about AI‑driven marketing that you wish more brand leaders understood? 

AI is not something you turn on and leave running. It is only as good as the data feeding it. If your data is fragmented, messy, or incomplete, you cannot leverage AI fully, and you will get outputs that look credible but lead you in the wrong direction. Trash in, trash out. Before brand leaders invest in the next layer of AI capability, they need to invest in the foundation: clean, connected, well-governed data. That is the less glamorous work, but it is what separates the organizations that get real value from AI from the ones that end up with expensive tools and disappointing results. 

Where Marketing Goes From Here 

What is clear is that AI isn’t simplifying marketing — it’s raising the stakes. Speed without direction creates noise. Automation without judgment creates risk. And data without governance creates false confidence. The brands that will navigate this new landscape successfully are the ones willing to rebuild their foundations: stronger data discipline, clearer strategic priorities, and a renewed commitment to showing up early in the buyer’s journey. AI can widen the field of possibility, but only human marketers can decide which possibilities are worth pursuing.

EMARKETER recently released its GEO cheat sheet. The infographic offers recommendations on five things to do now to optimize for AI platforms, including adapting your SEO playbook.

EMARKETER’s first tip is to “move deliberately, not desperately” because “GEO best practices are still forming.” What’s the #1 thing marketers who are concerned about AI visibility can do today? 

Stop waiting for the playbook to be finished.  

The brands that are going to win in AI search aren’t the ones who waited for the “definitive GEO guide” to drop. They’re the ones who started treating their content like reference material right now. That means structured answers, clear entity definitions, and pages that can be quoted without context — because that’s exactly how LLMs use them. 

If I had to name one thing: audit your most important pages for answer density. Can a language model extract a clean, accurate answer from each page without reading the whole thing? If not, that’s your first fix. Every page should be able to stand alone as a source. 

The deliberate part is real, though. Don’t chase every AI platform the same way. Understand which systems your audience is actually using to research your category, then optimize for those first. Spread thin and you optimize for nothing. 

EMARKETER’s cheat sheet calls out that “many SEO tactics transfer directly to AI search.” Can you share some examples of where you’ve seen this hold true? 

Quite a bit transfers across GEO and SEO, honestly — but the reason it works is different, and that distinction matters. 

Take structured content. In SEO, we use H2s and H3s to signal hierarchy to Google. In GEO, that same structure helps LLMs chunk your content into discrete, quotable units. Same tactic, different mechanism. Same result: you get cited. 

Schema markup is another one. We’ve been recommending JSON-LD for years from an SEO standpoint — FAQPage, HowTo, Organization. That same structured data is exactly what AI systems rely on to understand what a page is about, who authored it, and whether the source is credible. You’re essentially handing the model a fact sheet. 

E-E-A-T signals — author credentials, first-hand experience, editorial standards — were an SEO priority after Google’s Helpful Content updates. They’re just as relevant for GEO. Models that are trying to surface trustworthy answers are looking for the same signals Google was: who wrote this, are they qualified, does the content reflect real experience? 

The biggest gap is intent coverage. SEO pushed us to go deep on long-tail keywords. GEO pushes you to go deep on conversational questions — the full arc of what someone might ask about your topic, not just the head terms. Same instinct, bigger scope. 

We know that major LLMs pull from community platforms like Reddit, YouTube, and Wikipedia. How should brands be thinking about their presence on those platforms as part of a GEO strategy? 

This is one of the most underrated parts of GEO, and most brands aren’t treating it with nearly enough urgency. 

LLMs are trained on the open web — and the open web heavily weights community-generated content. Reddit threads show up in ChatGPT responses. YouTube transcripts get indexed. Wikipedia is essentially a primary source for entity resolution. If your brand is absent or poorly represented in those spaces, you have a gap that no amount of on-site optimization fixes. 

The practical move here is a presence audit. Search for your brand on Reddit, check if your category conversations even mention you, look at what Wikipedia says (or doesn’t say) about you or your competitors. That’s your baseline. 

From there, it’s not about gaming forums — that approach erodes trust fast. It’s about earning mentions through genuinely useful contributions: thought leadership that practitioners reference, data that journalists cite, explanations clear enough that communities organically link back to your site. Those are the signals that compound over time. 

For our clients, we treat community platform mentions as a GEO factor in their own right. If a brand is invisible on Reddit and YouTube in their category, that’s a real visibility deficit — not a “nice to have” fix. 

EMARKETER notes that “credible, first-party content raises your AI visibility.” How does this align with SEO best practices? Is there a genuine double-dip opportunity here? 

Yes — and this is where GEO and SEO are most aligned. First-party, authoritative content has always been the foundation of sustainable search visibility. GEO just makes the stakes higher. 

In SEO, we talk about topical authority: owning a subject area by publishing comprehensive, interlinked content that demonstrates depth. That architecture — pillar pages, supporting content, internal linking — is exactly what helps LLMs understand that your brand is a credible source on a given topic. The double-dip is real. 

What “first-party” means in GEO goes beyond just publishing on your own domain, though. It means content that contains something no one else has: your proprietary data, your client outcomes, your operational experience, your named experts. That’s what differentiates you as a source from every other brand publishing generic thought leadership on the same keywords. 

The brands I see getting cited in AI answers consistently have three things: original data or frameworks they created themselves, named experts attached to that content, and a track record of publishing that predates the AI search era. That history matters. Models assign more weight to sources that have accumulated consistent, high-quality mentions over time — not just brands that showed up last quarter optimizing for AI.  

So yes — if you’re creating genuinely original, well-structured, expert-attributed content, you’re building equity in both channels simultaneously. That’s probably the most efficient place to put your content investment right now. 

EMARKETER notes: “The AI search landscape will look dramatically different in two years.” What do you predict will be the biggest change between now and how marketers approach GEO in 2028? 

The measurement problem gets solved — and that changes everything. 

Right now, GEO is being treated as a belief system by some and a black box by others. The brands investing in it are doing so on faith and early signals. By 2028, I think we’ll have attribution models that can credibly connect AI-driven brand mentions to pipeline, not just impressions. When that happens, GEO moves from a content team conversation to a revenue conversation — and budgets will follow. 

The second shift I’d bet on: personalization at the query level. The AI search experiences we have today are relatively static. You ask a question, you get an answer. What’s coming is systems that understand your role, your context, your prior queries, and surface sources calibrated to you specifically. That means generic B2B content loses its edge fast. Brands that create genuinely differentiated, audience-specific content — not just repurposed whitepapers — will pull ahead. 

And the third thing: brand entity strength becomes a real competitive moat. Right now, entity optimization is still a gap most brands haven’t closed. Wikidata records incomplete, Knowledge Panel data inconsistent, NAP data scattered. By 2028, the brands that treated entity consolidation seriously will have a structural advantage in how models recognize and cite them. It’s not flashy work, but it’s defensible. 

Any final thoughts?

Start now. Win later. 

The marketers who approach GEO like it’s SEO circa 2012 — early, systematic, and patient — are going to look very prescient in a couple of years. That’s not a prediction about technology. It’s a prediction about discipline. The brands that audit their content today, close their entity gaps this quarter, and earn community mentions before AI attribution is table stakes will have a compounding advantage that latecomers simply can’t buy their way into. GEO rewards consistency and history. The best time to start was six months ago. The second best time is now. 

Interest in more? Complete the form below to unlock Mod Op’s free GEO audit.

The Cannes Lions International Festival of Creativity is known as the global advertising and marketing industry’s largest and most prestigious annual awards event, but it’s also a stress test of an industry in flux. What were the biggest trends spotlighted at this year’s Cannes Lions? 

Mod Op’s Chris Harihar spend the week on location, sharing his perspective with PRDaily, MediaPost, and Performance Marketing World. Here’s a few of his takeaways from Cannes 2026:

Trust Shines as the Difference-Maker 

Early in the week, Chris noted “authenticity” as a key theme, calling out that, “the challenge is no longer just reaching people. It’s reaching them in a way that feels credible.”

That desire for authenticity is likely what made influencers and creators a big topic at Cannes – more on that in a moment. The theme also showed through in how brands leveraged partners and platforms during the event. 

Read more on MediaPost: Forget AI, ‘Authenticity’ Is The Talk Of Cannes 

Creators Took Center Stage 

Between shifts in the media landscape and the desire to build trust with customers, it’s no surprise that Cannes has become increasingly creator driven. But, as Chris shared with PR Daily readers, “this year felt like the first where creators and creator partnerships became a dominant focus.” 

Despite creator presence at Cannes, Chris was quick to point out that all creator-brand engagements aren’t created equal: “The relationship between a brand and a creator has to feel ‘real’ and genuine. If it comes across as a cash grab for the creator or an audience rental for the brand, it will fail.” 

Read more on PR Daily: 5 PR takeaways from the Cannes Lions 

Brands are Looking for Agencies that Provide Creativity, Judgement and Expertise – Not Hours 

Cannes conversation included the topic of agencies moving away from more traditional billing structures to more output-based engagements. It’s not a new topic, but one that takes on new context in the age of AI. 

“It’s interesting to think about how agencies can continue to use AI to improve operations and efficiency,” Chris shared in a piece for MediaPost. “At the same time, one theme I’ve heard repeatedly during Cannes is that creative work should remain largely sacrosanct. AI can help optimize creative, drive scale, and adapt content for different audiences, but the creative work itself is still valued because it requires the [human] judgment, creativity, and expertise that great agency work demands.” 

Read more on MediaPost: Takeaways On The Future of Agencies  

Looking for more insights from upcoming events? Connect with Chris Harihar and the team on LinkedIn.