What would an “AI-First” marketing team actually look like?

The term "AI-First" is certainly having a moment. Earlier this year Shopify's CEO made headlines requiring employees to prove AI can't do a job before requesting new headcount. Duolingo declared itself an "AI-first" company. Every conference keynote seems to champion the concept, and it seems every CMO I talk to is being asked by their CEO how to make marketing “AI-first.” This term came up again in the recent CMO Huddles Studio podcast I was honored to be able to participate in.

So it’s certainly a thing. But what does "AI-first" actually mean for a marketing organization, and is it even the right way to frame the goal? Is it what we as CMOs should be striving for?

As marketing leaders, we need to think critically about whether "AI-first" is just the latest buzzword or is a genuine operating philosophy, and if so, what that means in practice. Buzzword or not, this conversation speaks to an undeniable trend in marketing, of leaning ever-deeper into AI across the function, so it’s worth putting some precision to the terminology.

What "AI-First" Actually Means

Let's start there, and get precise about the terms we’re using, as they matter. While there are a ton of definitions floating around, I prefer to look at it as a tiered model: AI-Enabled, AI-Augmented, and finally, AI-First.

AI-Enabled means using AI tools to improve existing processes: your team uses ChatGPT for blog post drafts or long-form whitepapers, Beautiful.ai for presentations, Otter for AI summaries of customer calls, or algorithmic bidding for media. It’s arguably where most marketing teams sit today: AI as an aide to specific and often isolated workloads.

AI-Augmented is the next step forward, where human-led work is enhanced by AI throughout the marketing workflow. AI tools become standardized and embedded, while AI Agents start to surface and make a practical impact in production. There’s a general maturity developing with AI platforms, use cases, and employee skillsets. But as with AI-Enabled, AI-Augmented is still about AI grafted on to the existing marketing team, processes, and functions, just at a deeper and more sophisticated level. The majority of marketing organizations are likely somewhere on this journey between Enabled and Augmented today, and set to evolve further towards AI-Augmented marketing in the coming year+.

AI-First is a fundamentally different concept altogether: it means organizing the marketing function around AI capabilities from the ground up, where AI is the default and human involvement is the exception requiring justification, just as we saw with Spotify. The gap between "using AI tools to support our existing team and workflow” and being genuinely "AI-first" is therefore vast.

AI-first isn't about tool adoption. It's about organizational design, decision rights, workflow architecture, talent strategy, and culture. It depends on a mature, reliable, and accurate AI technology base (a challenge) and requires rethinking everything from how campaigns are conceived to how performance is measured to how you build your team.

The Case for AI-First Marketing

The leading ROI case for marketing adoption of AI as of now is the efficiency argument, and it is real and compelling. Jasper's 2025 State of AI report shows 63% of organizations using generative AI report productivity improvements. Real-time optimization across creative and media workflows, faster reaction and production cycles, cost savings from reduced out-sourcing, and so on.

The competitive pressure CMO’s feel is equally real. Research from ContentGrip notes 69.1% of marketers already integrate AI into operations, creating the risk of competitors moving faster because of deeper AI integration, and the absolutely real peer pressure CMO’s feel of being viewed as an AI laggard. Plus the "do more with less" mandate from CFOs isn't going away. Gartner's 2025 CMO survey reported marketing budgets holding steady at 7.7% of revenue despite rising customer acquisition costs, and the push to delay hiring and reduce expenses in a challenging economy continues to grow.

Then there's the capabilities AI promises to unlock for marketers. Personalization at scale suddenly becomes a realistic goal even for the most resource-constrained marketing teams. The same is true for predictive analytics that inform strategy rather than just reporting past activity. Entire swaths of the customer journey can become automated, faster, more personalized, and more effective at driving conversions.

BCG's research emphasizes the scale of the AI opportunity: CMOs can potentially "reinvent their entire operating model" through AI.

These benefits of AI appear to be genuine and potentially revolutionary for the practice and profession of marketing. No CMO interested in keeping their job can afford to ignore AI and its potential marketing use cases. The question then isn't really whether AI can help marketers; it has powerful and even transformative potential, even with some of the immaturity and rough edges remaining in the tech. The question is whether organizing entirely around AI is the right answer and what exactly that means in practice.

The Case for Caution

The raw potential of AI can be absolutely intoxicating, especially for business leaders focused on the magical mix of scale and bottom line. But there are risks to going all-in on a truly AI-first organizational strategy, broadly categorized around 1) creativity / homogenization, 2) customer fatigue, 3) accuracy vs reliability, 4) talent impact, and 5) ethical concerns.

I covered some of these concerns in my AI predictions for 2026 article over on AgenticCMO.

Creativity & Homogenization: In my podcast discussion I touched briefly on this. SurveyMonkey research flags "lack of creativity" as a core AI limitation; LLMs trained on existing data often struggle to produce genuinely novel ideas. Marketing differentiation often comes from taking bold creative leaps, not just more efficient process implementation. If everyone uses similar LLMs, training on the same data, optimizing for common signals and shared best practices, do we risk "algorithmic sameness" where everyone's brand voice and messaging starts to converge? Does this argue for a continued primary role of humans within the creative process, even in an AI-first marketing team?

Customer Fatigue: There remains an uncanny valley in AI creative output today, and that is as evident in mass-personalized copy as it is in the more obvious AI-created video. Pair that with the potential for a massive ramp up in content and messaging production enabled by AI, and it starts to beg some questions. How of-putting is this for customers and prospects? Will they notice, care, or push-back? Will “made by humans” start to become a valid brand differentiator, potentially compounded if some of the more problematic societal predictions about mass AI adoption (environmental, employment, energy costs, etc.) start to come true?

"Everyone there learns a brutal lesson fast: a model can be 97% accurate but only 70% reliable — and the gap can bankrupt you." - Kushal Chakrabarti

Accuracy vs Reliability: The quote above highlights a considered risk of going full Agentic AI in any business function. Modern LLMs may be highly accurate but notoriously unreliable in that accuracy (this is a great read, by the way). They are improving rapidly, but the reliability of LLM output will remain a topic of interest for anyone planning to bet their business on agentic AI solutions for the forseeable future.

Talent Impact: Harvard's analysis raises concern that AI will eliminate entry-level marketing jobs, which in turn raises an uncomfortable question: where is the pipeline for future marketing leaders in your organization going to come from if the junior roles go away? Is the bet that over time even those leaders will be replaced by AI Agents, or simply that this is a problem for another day? Is an AI-first marketing approach foreshadowing a great hollowing out of the marketing profession?

Ethical Concerns: CoSchedule's 2025 report shows 40% of marketers cite data privacy concerns and 38% lack the technical expertise to implement AI responsibly. Algorithmic bias can also reproduce problematic patterns through negative stereotyping in marketing targeting and messaging. Even with the (candidly pretty remarkable) recent improvements in the quality of major LLMs from Anthropic, Google, and OpenAI, brand safety risks may still emerge if AI-generated content or social creative lacks real human oversight.

What Might AI-First Marketing Actually Look Like in Practice

The obvious difference in an AI-first marketing team vs traditional, AI-enabled or even AI-augmented marketing is the approach to human talent and staffing. It essentially argues for a Shopify-like approach: Start with the intent to deploy AI Agents for a given need or function, and only supplement with human marketers where and if absolutely necessary. This kind of approach implies a fairly major shift in how CMO’s need to think about their teams and resource allocation.

Structure & Talent: It’s not just about reducing human headcount in traditional production and execution roles, though that is presumed to be a significant component of the ROI calculation for this type of model. It’s about entirely new roles emerging: AI operators, prompt engineers, human “oversight” specialists. More traditional generalist and junior roles may fade while specialists in either AI orchestration or uniquely human skillsets that AI can’t quite address (strategic thinking, creative ideation, internal/external stakeholder relatioships) become more valuable. At a bare minimum, “AI-first” argues for a fairly radical rethink of marketing organizational structures, role definitions, and required technical skillsets.

My take: You still need the traditional talent leads to anchor your team - Product Marketing, Growth, Brand & Communications, and Marketing Ops. But the talent and skill mix underneath those leads will shift dramatically away from production and towards AI Agent oversight, training and orchestration. Marketing Ops (+ AI) will also be more important than ever, as they add AI Agent, platform management, and LLM training to their traditional basket of CRM, automation, customer data, and analytics.

Budget: AI-first likely dictates a significant shift in marketing budgets from headcount towards technology, a trend we’ve been seeing for years with the rise of the importance of the martech stack, but accelerated even more. New budgets may emerge specifically for AI pilot programs, aggressive experimentation efforts, and infrastructure for rapid iteration. Data infrastructure becomes foundational and even more tightly aligned with IT, as AI can only be as good as the data it learns from, and their ongoing vigilence about AI agent access and use of confidential company or customer data. We should also expect increased focus on learning and development (L&D) investments, as smaller marketing teams take a renewed interest in ensuring their remaining talent keeps their AI skills fresh to stay on top of the incredibly fast-moving AI landscape.

Culture: AI-first will require a highly intentional focus by CMOs on instilling a disruption-fluent culture across their team. From decision-making to reaction speed to fearless experimentation, many of the cultural attributes of the most fluid and flexible organizations of today will take on paramount importance in an AI-first marketing team. Think about decision-making in a world where, as BCG predicts, AI Agents handle 20%+ of marketing’s total workload over the next two to three years: Traditional, hierarchical decision-authority won’t cut it, as the whole promise of agentic AI rests on AI autonomously making decisions on the fly.

Or speed and experimentation: To be truly AI-first, you need a culture built to thrive in it, one that is ready to react, adapt, and execute far faster than ever before and be truly fearless in experimenting with new Agents, new AI tools and platforms, and new models that may have zero precedence or best practices and therefore carry high risk of failure.

Psychological safety, combined with devolved decision-making, experimentation mindsets, and a high degree of employee autonomy, will be the cultural cornerstones of AI-first teams.

Where will it begin for marketing teams looking to go AI-first? Much depends on the development and maturity of the rapidly emerging class of agentic AI solutions, but the natural path is most likely outgrowth from where AI has had the most impact on marketing workloads already: personalization, localization, creative and content production, media planning and buying, measurement, customer insights, customer touchpoint automation, etc.

A Disruption Fluency Perspective

How does AI-first map to building genuinely adaptive marketing organizations? Within the Disruption-Fluent Marketing framework I mentioned above, I identified four dimensions of disruption fluency (the full framework is coming out soon). For any aspiring AI-first CMOs, they should consider how to shape and priortize each of these dimensions in order to build a team and culture capable of executing their AI vision.

On Leadership Tension: There's a risk that more traditional top-down administrative leadership attempts to reassert control through AI-driven governance and internal policy, strangling your team’s adaptive capacity. But there's also opportunity: AI handling routine admin and governance work could free leaders to pursue the flexible and adaptive leadership approaches AI-first demands. The critical question will be does AI-first enhance or constrain the leadership approaches your organization needs? What’s the right mix?

On Operational Agility: AI can dramatically accelerate insight-to-action cycles (or OODA Loop speed, for those familiar with the concept). But too-rigid agentic AI workflows can become their own bureaucracy, especially if the mix of technologies and tools continues to proliferate and the attendent management burden of maintaining them grows in line. The "Minimum Viable Bureaucracy" concept applies here, as AI systems deserve the same scrutiny we'd give any process that might constrain team flexibility or consume resources.

On Sensing & Learning: AI excels at processing signals at scale. But over-reliance on AI-detected patterns may miss the more qualitative, cultural, or weak signals that humans might otherwise catch. Organizational learning needs to include constant evaluation and back-checking of AI-derived insights, not just on outputs like campaign performance or production volume and quality. And even AI-first marketing organizations should never discount the value of human-to-human interactions when it comes to understanding the customer: Time spent by your human marketers out in the field, at events, or otherwise with customers and partners is rarely wasted, and may become even more important in lean AI-first marketing teams.

On Cultural Readiness: This is where AI-first can go wrong quickly. I would argue as a result, this is where CMOs must start. AI-first mandates can seriously damage psychological safety if your employees fear for their jobs or their ongoing value. Critics have noted that policies like Shopify's "could undervalue human contributions and lead to job insecurity." Cultural readiness for AI-first requires not only AI skillsets, but also honest conversations about what AI means for your people; challenging and uncertain conversations that many organizations may be avoiding.

The Path Forward?

For all the opportunity an AI-first marketing organization promises, the main concern may be CMO’s charging fast into an exciting new way of working - be it from their own enthusiasm or belief, competitive pressures, or CEO mandates - without intentionally considering the risks or the tough cultural and structural work needed for this path to succeed.

Perhaps the best way to think of it isn’t “AI-First” but “AI-Intentional.” A leadership and team commitment to intentionally evaluating and adopting AI and AI Agents not in all ways and everywhere, but as dictated by the risk factors, potential ROI, and technology and talent maturity. Different parts of the marketing function may warrant different levels of AI integration and speed of adoption. Media planning or content production? Full speed ahead. Brand strategy and creative ideation? Perhaps a more deliberate approach with a deeper human investment makes sense for the moment.

The biggest takeaway: ”AI-first” refers to a means, not an outcome. It’s almost like saying you take a “Hammer-first” approach to your construction business and then go looking for nails. AI is a tool; the focus needs to be on the outcomes, with AI being considered as a potentially powerful tool to achieve those outcomes, in full awareness of potential risks vs impact.

While you as CMO are investing now in promising applications of AI today, and empowering your teams to experiment and innovate on potential use cases for tomorrow, don't forget to put in the heavy but necessary work on team culture, decision-making models, governance, talent reselling, leadership approaches. These are the less flashy but utterly critical foundations on which any AI-first, or AI-Intentional, marketing team must be built.

If we’ve learned one thing from marketing and AI in 2025, it’s that any plans you set in Q1 for specific AI technology or platform adoption will likely be irrelevant by Q2. The landscape is changing that fast.

But if you take the time now to build a disruption-fluent team with an “AI-intentional” mindset, you’re laying the foundation you need in order to win in the AI-dominated years ahead.