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What’s up everyone, today we have the pleasure of sitting down with Chris O’Neill, CEO at GrowthLoop.

Summary: Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. Chris also breaks down how reinforcement learning helps avoid a sea of sameness by letting campaigns evolve mid-flight based on live data. To support velocity without sacrificing control, top teams are running red team drills, assigning clear data ownership, and introducing internal AI regulation roles that manage risk while unlocking scale.

In this Episode…

• Reframing AI in Marketing Around Outcomes and Velocity
• How to Use Agentic AI for Marketing Campaign Execution
• How Reinforcement Learning Optimizes GenAI Content
• How to Use AI Agents With Compliance and Velocity in Enterprise
• How to Use Red Team Drills to Test AI Data Ownership
• Modern Marketing Teams Need Internal AI Regulation Roles and Skill Ladders
• From Composable Customer Data Platforms to Compound Marketing Engines

About Chris

Chris is the CEO of GrowthLoop, where he helps companies unlock compound growth by accelerating marketing cycles with agentic AI. Before GrowthLoop, he was Chief Growth Officer at Xero and Chief Business Officer at Glean, guiding both companies through pivotal growth stages.

Earlier, Chris served as CEO of Evernote and led Google Canada as Managing Director, growing annual revenue from $600 million to over $2 billion. He began his career in management consulting at Oliver Wyman and has held leadership roles across technology, fintech, and AI-driven platforms.

The 2025 AI and Marketing Performance Index

The 2025 AI and Marketing Performance Index that GrowthLoop put together is excellent, we’re honored to have gotten our hands on it before it went live and getting to unpack that with Chris in this episode. 

The report answers timely questions a lot of teams are are wrestling with:

• Are top performers ahead of the AI curve or just focused on solid foundations?
• Are top performers focused on speed and quantity or does quality still win in a sea of sameness?

We’ve chatted with plenty of folks that are betting on patience and polish. But GrowthLoop’s data shows the opposite.

🤖🏃 Top performerming marketing teams are already scaling with AI and their focus on speed is driving growth. 

For some, this might be a wake-up call. But for others, it’s confirmation and might seem obvious: Teams that are using AI and working fast are growing faster. We all get the why. But the big mystery is the how. 

In this episode we dig into the how teams can implement AI to grow faster and how to prepare marketers and marketing ops folks for the next 5 years.

Reframing AI in Marketing Around Outcomes and Velocity

Marketing teams love speed. AI vendors promise it. Founders crave it. The problem is most people chasing speed have no idea where they’re going. Chris prefers velocity. Velocity means you are moving fast in a defined direction. That requires clarity. Not hype. Not generic goals. Clarity.

AI belongs in your toolkit once you know exactly which metric needs to move. Chris puts it plainly: revenue, lifetime value, or cost. Pick one. Write it down. Then explain how AI helps you get there. Not in vague marketing terms. In business terms. If you cannot describe the outcome in a sentence your CFO would nod at, you are wasting everyone’s time.

“Being able to articulate with precision how AI is going to drive and improve your profit and loss statement, that’s where it starts.”

Too many teams start with tools. They get caught up in features and launch pilots with no destination. Chris sees this constantly. The projects that actually work begin with a clearly defined business problem. Only after that do they start choosing systems that will accelerate execution. AI helps when it fits into a system that already knows where it’s going.

Velocity also forces prioritization. If your AI project can’t show directional impact on a core business metric, it does not deserve resources. That way you can protect your time, your budget, and your credibility. Chris doesn’t get excited by experiments. He gets excited when someone shows him how AI will raise net revenue by half a percent this quarter. That’s the work.

Key takeaway: Start with a business problem. Choose one outcome: revenue, lifetime value, or cost reduction. Define how AI contributes to that outcome in concrete terms. Use speed only when you know the direction. That way you can build systems that deliver velocity, not chaos.

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How to Use Agentic AI for Marketing Campaign Execution

Many marketing teams still rely on AI to summarize campaign data, but stop there. They generate charts, read the output, and then return to the same manual workflows they have used for years. Chris sees this pattern everywhere. Teams label themselves as “data-driven,” while depending on outdated methods like list pulls, rigid segmentation, and one-off blasts that treat everyone in the same group the same way.

Chris calls this “waterfall marketing.” A marketer decides on a goal like improving retention or increasing lifetime value. Then they wait in line for the data team to write SQL, generate lists, and pass it back. That process often takes days or weeks, and the result is usually too narrow or too broad. The entire workflow is slow, disconnected, and full of friction.

Teams that are ahead have moved to agent-based execution. These systems no longer depend on one-off requests or isolated tools. AI agents access a shared semantic layer, interpret past outcomes, and suggest actions that align with business goals. These actions include:

• Identifying the best-fit audience based on past conversions
• Suggesting campaign timing and sequencing
• Launching experiments automatically
• Feeding all results back into a single data source

“You don’t wait in line for a data pull anymore,” Chris said. “The agent already knows what audience will likely move the needle, based on what’s worked in the past.”

Marketing teams using this model no longer debate which list to use or when to launch. They build continuous loops where agents suggest, execute, and learn at every stage. These agents now handle tasks better than most humans, especially when volume and speed matter. Marketers remain in the loop for creative decisions and audience understanding, but the manual overhead is no longer the cost of doing business.

Key takeaway: AI agents become effective when they handle specific steps across your marketing workflow. By assigning agents to segmentation, timing, testing, and feedback collection, you can move faster and operate with more precision. That way you can replace the long list of disconnected tasks with a tight loop of execution that adapts in real time.

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How Reinforcement Learning Optimizes GenAI Content

Reinforcement learning gives marketers a way to optimize AI-generated content without falling into repetition. Chris has seen firsthand how most outbound sequences feel eerily similar. Templates dominate, personalization tags glitch, and every message sounds like it was assembled by the same spreadsheet. The problem does not stem from the idea of automation but from its poor execution. Teams copy tactics without refining their inputs or measuring what actually works.

Chris points to reinforcement learning as the fix for this stagnation. He contrasts it with more rigid machine learning models, which make predictions but often lack adaptability. Reinforcement learning works differently. It learns by doing. It tracks real-world feedback and updates decision-making logic in motion. That gives marketers an edge in adjusting timing, sequencing, and delivery based on signals from actual behavior.

“It would be silly to ignore all the data from previous experiments,” Chris said. “Reinforcement learning gives us a way to build on it without starting over each time.”

Chris believes this creates space for creative work rather than replacing it. Agents should own the tedious tasks. That includes segmenting lists, building reports, and managing repetitive logic. Human teams can then focus on storytelling, taste, and trend awareness. Chris referenced a conversation with a senior designer at Gap who shared a similar view. This designer believes AI lets him expand his creative range by clearing room for deep work. Chris sees the same opportunity in marketing. The system works best when agents handle the mechanical layers, and humans bring energy, weirdness, and originality.

Many leaders are still caught in operational quicksand. Their teams wrestle with bloated tools and unclear processes. Chris sees AI agents as a lever for reducing that drag. He wants to shrink the distance between a good idea and the moment it reaches a customer. Shorter cycles mean faster feedback and more room to adjust. Reinforcement learning supports that rhythm by updating strategies based on what just happened, not what worked a quarter ago.

Key takeaway: Reinforcement learning helps agents get smarter while your campaigns are in flight. Use it to adjust timing and message logic based on live feedback, not stale rules. That way you can spend less time maintaining brittle flows and more time creating work that actually lands.

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How to Use AI Agents With Compliance and Velocity in Enterprise

AI agents are gaining traction in enterprise stacks, but regulated industries keep defaulting to the same excuse: “We move slower because we have to.” Health tech, fintech, and other compliance-heavy sectors are full of stakeholders who block automation by default. Marketers want to ship faster, but the second a tool touches PII, the brakes hit hard. Chris has heard this argument many times, and he sees it as a story teams tell themselves to avoid doing the hard coordination work.

Chris believes velocity does not require recklessness. It requires structure. He urges enterprise teams to set clear boundaries around what agents are allowed to do. Teams must define where agents can act autonomously and where humans stay involved. This requires more than a PowerPoint slide about risk. It means assigning ownership for data strategy, governance, and response protocols. Chris pushes for red team drills that simulate real-world failures like hallucinations, regulatory breaches, or data leakage. These exercises help companies build muscle memory before deploying agents into live environments.

“You have to be really clear about governance,” Chris said. “Be specific about when agents get autonomy and when they don’t.”

Legacy tools and fragmented stacks are often used as scapegoats, especially in industries shaped by years of acquisitions. Chris sees that complexity as an opening for agents, not a blocker. Protocols like agent-to-agent messaging let tools exchange context across previously disconnected systems. That way, companies can stop waiting for the perfect replatform and start coordinating smarter workflows with what they already have.

Chris believes the real blocker is hesitation disguised as caution. Every department claims to be the exception. Compliance says no. IT says not now. Legal says maybe later. But AI does not need to bypass these groups. It needs to work with them. AI agents can highlight risk areas, summarize legal exposure, and support approvals instead of replacing them. The teams that figure out how to build within these constraints will ship faster than the ones still waiting for the stars to align.

Key takeaway: Regulated companies can move faster with AI by replacing ambiguity with clear rules. Define when agents can act on their own, simulate worst-case scenarios before deploying, and use agent protocols to connect broken systems. Legal and compliance do not need to slow you down when agents are designed to support their workflows instead of avoiding them.

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How to Use Red Team Drills to Test AI Data Ownership

AI finally pushed data governance into the spotlight. Anyone in marketing ops has watched long-ignored tasks like deduplication and enrichment move from the bottom of the backlog to the top of the board deck. Executives actually care about clean records, stable integrations, and full control over which datasets power their models. This urgency is longggg overdue. Data hygiene used to be seen as internal plumbing. Now it is seen as infrastructure for decision-making.

Chris is clear about where companies need to go next. No one has a functioning AI strategy without a functioning data strategy. Data clouds accelerated this understanding. Their growth came from more than cost and scalability. They forced companies to unify performance data, customer behavior, and transactional logs into a single, queryable environment. That consolidation created the foundation for AI experimentation, model training, and performance monitoring.

But the bigger challenge sits upstream. Clean storage is meaningless if no one owns the pipeline. Chris compares data to company hardware. Most organizations track laptops, assign them to employees, and run inventory audits. Yet they rarely apply the same rigor to datasets that train generative models or power customer journeys. This gap leads to vague responsibilities and slow responses when issues arise.

Chris wants teams to simulate those moments in advance. He suggests borrowing the concept of red team drills from cybersecurity. These drills should mimic scenarios like hallucinated product copy, misrouted personalization, or sensitive data exposure. Someone must know who takes the first action, who gets alerted, and how to recover. Writing policies is not enough. Running drills helps teams internalize the plan under pressure.

“You need to simulate things going wrong,” Chris said. “Because something will go wrong. And in that moment, everyone should already know their role.”

Key takeaway: Build a documented system that assigns owners to every critical dataset, including who has access, who modifies the data, and who trains AI models with it. Run small red team drills that walk through potential AI failures, and make sure every stakeholder understands their role. That way you can reduce confusion, recover faster, and build trust in your AI systems.

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Modern Marketing Teams Need Internal AI Regulation Roles and Skill Ladders

AI agents are taking over the repetitive work. That shift is not a theory, it is already happening. Chris believes the marketers who rely on one platform and one tool will be the first to feel it. The ones who adapt will start acting more like editors and product managers. They will guide ideas through messy systems, manage agents as collaborators, and deliver work that travels from prompt to production without losing context. Those marketers will not sit in silos. They will work inside small, agile pods with shared ownership of experiments and outcomes.

“Marketing teams will look more like software teams. Small, self-contained pods that move end-to-end and operate fast.”

Execution alone is no longer enough. Someone has to be accountable for the judgment layer. Case in point: the PGA headshot debacle where white golfers were depicted as professionals and golfers of color were placed in labor roles. Total failure in operational design and Gen-AI regulation. That failure did not start with the model. It started with the absence of ownership. No one in the room had the responsibility or the authority to stop bad output from shipping.

Chris sees a clear need for a new role inside marketing organizations. He describes it as an internal AI regulator. This is not a dotted-line position or a shared Slack channel. It is a named person inside the content pipeline with the power to halt production. Their job is to review AI output for tone, bias, brand safety, and legal risk. They should have the mandate to ask tough questions and the agency to act before things go live.

At GrowthLoop, Chris is building more than job titles. He is creating AI skill ladders that define expectations for every level. These ladders do not reward theory. They define what people should be doing with AI in their actual work. For example, a mid-level lifecycle marketer is expected to:

• Test multiple GPT workflows across use cases
• Benchmark their performance against real campaign goals
• Collaborate with data teams to refine and retrain prompts

That is the floor, not the ceiling. Courage is required at every level. Courage to launch something that might need fixing. Courage to challenge the output of a model the whole team likes. Courage to monitor performance honestly and start over when necessary.

“You are going to have to break a few eggs to make an omelet. That is a mindset.”

Marketing teams are being rebuilt around AI, but not every team will move the same way. The ones that scale responsibly will put human judgment at the center of their workflows. They will define what good looks like. They will operationalize quality instead of chasing it after things go wrong.

Key takeaway: Build two things into your marketing team today: an AI skill ladder and a designated AI regulator human who is in the loop. Define what AI fluency looks like by role and level, and train your team to hit those marks with real-world scenarios, not hypotheticals. Assign clear ownership for output review, bias detection, and brand safety. Do not leave quality control to chance or to the loudest person in the meeting. Create systems that support speed, then hire and grow people who have the courage to operate inside that speed with clarity, discipline, and care.

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How AI Agents Affect Entry-Level Marketing Roles

AI agents have replaced many of the tasks that used to serve as training grounds for early-career marketers. Drafting email copy, pulling performance data, tagging CRM records, and even writing basic briefs have shifted to automation. What once taught marketers how a business runs now sits behind a prompt window. This change has left many wondering how fresh grads will get their start when the starting line keeps moving.

Chris points out that younger workers already operate with a different skill set. His own son uses AI to study, research, and problem-solve in ways that feel foreign to older professionals. This group is not waiting to be trained on AI tools. They are already integrating them into their thinking, workflows, and daily tasks. That behavior represents a capability, not just a preference. Companies need to start treating that comfort with AI as a hiring asset, not a side note.

“You will be viewed as a magician if you become a super user of AI,” Chris said.

Chris encourages companies to create space for reverse mentorship. In most organizations, experience tends to flow top-down. He believes in flipping that, especially with AI. Watch how younger employees interact with tools. Ask them how they work and why they skip certain steps. Use their instincts as a clue about what friction still exists in your processes. Chris once saw a VC firm run product workshops with teenagers. Those sessions completely changed how the firm evaluated gaming startups. Young people often see what’s coming before most executives do.

Instead of rebuilding the same roles with fewer humans, Chris suggests designing new ones that align with how younger professionals already work. They know how to move faster, test ideas in seconds, and find better answers using AI. They also need structure, goals, and feedback. Managers can offer that without micromanaging. Chris believes every employee will manage agents, and the same rules still apply: clear expectations, strong context, and timely feedback create strong contributors—human or not.

Key takeaway: AI-native workers already bring value that older systems were not built to recognize. Companies should stop replicating yesterday’s roles and start building new ones around today’s capabilities. That way you can give early-career marketers a path that reflects how work actually gets done.

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From Composable Customer Data Platforms to Compound Marketing Engines

CDP tools have saturated the market with bold promises and vague value. Teams were told they would get a full customer 360, total autonomy, and seamless orchestration. Instead, many found themselves stuck with systems that slowed them down and left campaigns collecting dust. GrowthLoop watched this unfold, and rather than joining the pile with another acronym, they made a different move. They started naming what they actually do. They called it compound marketing.

The concept borrows from finance. Just like compound interest rewards small, consistent gains, compound marketing stacks incremental wins across campaigns and channels. The outcome is not a one-time spike, but a steady climb that strengthens over time. Chris sees this as a return to execution. No buzzwords, just results. He explained that marketers should care less about what the category is called and more about what the product helps them achieve.

“If you’re not delivering results, you’re not relevant.”

Chris told a story about a customer who had been running only 2 percent of their campaigns through a legacy CDP. After switching to GrowthLoop, that number jumped to near full usage in a matter of weeks. The platform did not rely on extensive onboarding or months of custom setup. People could log in, launch campaigns, and see value immediately. The difference came from the product’s usability, its direct integration with the data warehouse, and its focus on marketer control.

He made it clear that tools only work when teams actually want to use them. If a product feels clunky or creates bottlenecks, it will get abandoned, no matter how powerful it looks in a demo. Chris believes that usability is not just about interface design. It includes thoughtful architecture, fast deployment, and responsive support. He also believes in being a real partner. He admitted the team makes mistakes and works through setbacks, but they show up, stay involved, and operate like an extension of the customer’s team.

Key takeaway: Compound marketing creates lasting impact by stacking small campaign wins over time. Build products that marketers use often, not just admire once. Prioritize usability, reduce onboarding friction, and stay close to your customers. That way you can turn theoretical capabilities into active campaigns.

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How to Decide Which Martech AI Agent Gets to Act

Tool overlap has become a full-contact sport. Every vendor now promises “agentic AI,” even if the only thing their agent does is regurgitate a support article. CDPs are pivoting into orchestration. Lifecycle tools are growing native warehouse connectors. Even your helpdesk tool suddenly wants to run campaigns. And sitting in the middle of this mayhem is the marketing ops lead, expected to referee where and when to turn each of these bots loose.

Chris thinks most teams are already underwater. Complexity is eating capacity. His advice is to ignore the bells and whistles and get brutally honest about which tools and teams can actually ship fast. You need ruthless focus. That means evaluating vendors by a short list:

• Do they build fast?
• Do they let your team iterate quickly?
• Are they clear about what their AI can actually do?

“The sad reality is some of the larger players, the Adobes and Salesforces, are just iterating at such a glacial pace,” Chris said. “Salesforce literally cut off Slack APIs last week. I don’t even know where to start on that. It’s just ridiculous.”

The path forward, according to Chris, is anchored in two bets: the data cloud and agentic AI. Old-school systems of record have peaked. The winning teams will be the ones that minimize the time between having a smart idea, validating it with data, and getting it into market. AI plays a supporting role in that loop, but only when the team using it has the clarity and speed to act. If your workflow takes three weeks and five meetings to get a subject line approved, no AI agent is going to fix that.

Chris’s team treats AI adoption like an expedition. They go out and try things in the wild: writing creative briefs with agents, generating images, and now, experimenting with video creation. He didn’t expect to be this deep into content automation so soon. But they are, because they kept moving. That is the real work. The chaos is permanent, but the ones who can navigate it with momentum and aligned partners will be the ones left standing.

Key takeaway: Stop waiting for AI tools to mature before you act. Choose vendors who build fast, work with partners who can iterate with you, and structure your team so you can get ideas to execution without artificial friction. Use the chaos as a filter to spot which players are actually helping you move forward.

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How AI Acceleration Ties to Data Governance and Marketing Pressure

The 2025 AI and Marketing Performance Index surfaced a trend that many suspected but had not yet seen validated at scale. Teams using AI effectively are moving faster. They are publishing campaigns quicker, acting on data more decisively, and shipping experiments with shorter feedback loops. Chris called this out as one of the clearest signals in the report. The link between AI adoption and operational velocity is no longer theoretical. Teams that treat AI as a workflow layer are seeing measurable gains in execution speed.

The same report also highlighted something heavier. Marketing pressure has intensified, and the spike is not subtle. Respondents reported feeling significantly more pressure than in prior years. Some of that pressure may come from macroeconomic factors, but the structure of marketing roles carries its own weight. Short CMO tenures and compressed performance windows amplify the urgency. Chris expected pressure to be present, but the magnitude still surprised him. High-performing teams are not immune. If anything, they are absorbing more.

Many teams are responding to this pressure by pulling AI closer to the center of their workflows. That shift includes tighter controls, clearer accountability, and better data stewardship. Chris noted that marketers are starting to connect quality data and governance to velocity, not just compliance. They see guardrails as part of building a durable system. Teams that once treated data governance as a blocker now treat it as foundational infrastructure. The framing has changed. The mindset has matured.

“The ability to use and view AI as part of the solution to that problem was something that was a pleasant surprise.”

The companies showing momentum are not improvising. They are building internal systems that let them move fast without eroding trust. They are defining what “good” looks like within their context, using real operational data instead of aspirational benchmarks. Chris made it clear that the goal is not just to decode what top teams are doing, but to help others implement similar mechanics with confidence and control.

Key takeaway: The most effective marketing teams in 2025 are integrating AI into core workflows to gain speed while investing in governance systems that sustain that velocity under pressure. That combination gives them a structural edge, turning pressure into clarity and AI into a tool for repeatable execution.

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How to Structure Work Around People and Problems That Matter

Chris builds his entire operating model around two filters: the people he works with and the problems they are trying to solve. He does not pretend to have balance figured out. He has experienced burnout. He has taken on too much. Through those experiences, he has learned to evaluate opportunities through a simple, personal lens. Are the people worth investing in? Are the problems worth the time? If both are true, then he moves forward. If either fails the test, he walks away.

He does not rely on grand rituals or color-coded calendars to hold it all together. Instead, he moves between intensity and recovery. When he feels the pull to push, he leans into work. When he feels himself slipping, he finds ways to reset. That reset might look like riding up a steep hill with friends, playing three holes of golf to get outside, or sitting in the quiet with his thoughts. His toolkit includes flexibility, physical movement, and social connection.

“You’re going to be out of balance almost all the time, but you can find ways to surge your capacity and stay grounded.”

The idea of mentorship carries weight for Chris because of what it meant to him earlier in his life. People shaped his career by taking risks on him when he did not always feel ready. He now returns that energy, usually with Canadian founders, because he believes the right support at the right moment can completely reshape a career. He treats this as a commitment, not a side project. It forms a core part of how he defines meaning in his life today.

Chris did not offer a productivity framework. He described a value system. He tracks his energy, keeps his people close, and stays honest about when things feel heavy. The work matters, but only when it connects to something real. Everything else can fall away.

Key takeaway: Anchor your work around two clear filters: people and problems. Invest your energy when both align. Use flexibility and reflection to recover from burnout. Structure your rhythm around what brings clarity, and build meaning through mentorship and motion. That way you can sustain ambition without losing your footing.

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Episode Recap

This episode features Chris, CEO of GrowthLoop, and distills key findings from The 2025 AI and Marketing Performance Index, a research report produced by his team. The report outlines how top-performing teams are using AI agents to execute campaigns more effectively. These teams assign agents to specific parts of the workflow, such as segmentation, testing, and feedback collection. This structure creates a fast-moving execution loop that adapts automatically, reducing the need for manual intervention and disconnected processes.

Chris explains how many of these teams are also applying reinforcement learning to help agents get smarter while campaigns are in motion. By using live performance data to adjust message logic and delivery timing, marketers can respond to what is actually happening instead of relying on outdated rules. This change reduces the time spent managing broken flows and increases the time available for creating meaningful, high-impact work.

Teams working in regulated industries are also seeing benefits. Instead of avoiding compliance concerns, leading companies are designing agent behavior around explicit rules and scenario simulations. These companies define which actions agents can take independently, establish safety protocols, and build review checkpoints into the system. This structure enables legal and compliance teams to remain fully involved while maintaining campaign velocity.

We also unpack how to create internal clarity around AI systems. This includes assigning owners to datasets, running small red team drills to prepare for failure scenarios, and ensuring that every stakeholder understands their responsibilities. Marketing leaders are starting to assign human regulators to oversee AI workflows and are developing skill ladders that define what AI fluency looks like by role and level. Training happens through real scenarios, not hypothetical quizzes.

Finally, we touch on how AI is affecting early-career marketing roles. Teams that continue to replicate traditional job structures risk falling behind. The most effective companies are creating new roles for marketers who understand AI tools, data workflows, and adaptive campaign strategies. These workers are already delivering value, even if legacy systems were not designed to measure it. Marketing organizations that invest in these roles are setting themselves up for long-term precision, adaptability, and scale.

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Follow Chris and GrowthLoop 👇

• LinkedIn
• GrowthLoop
• The 2025 AI and Marketing Performance Index

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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