Why Agencies Will Ultimately Need to Figure Out AI Agents to Survive
As a long-time agency person, I’ve experienced firsthand the challenges of working in an environment that’s constantly demanding more for less. I’ve been stretched across too many clients with not enough time, yet each one expects bespoke recommendations, tailored presentations, and detailed reporting—all at discounted fees. And let’s be real: many of the lower-level jobs have already been outsourced to India, where agencies hire vast armies of workers to handle implementation work at scale. The last bastion of differentiation for agencies used to be automation tools like Marin and Kenshoo, then came the more customizable platforms like Make and Zapier, and now we’re entering the era of AI agents like ChatGPT.
The landscape has become even more complex with the proliferation of marketing platforms. You’ve got Google Ads, Facebook/Instagram, Amazon Ads, TikTok, LinkedIn, Pinterest, and an expanding universe of programmatic AVOD channels. Maintaining professionalism across hundreds of unique platforms is no longer a luxury—it’s a necessity. So where does that leave agencies? The ones that will survive are those that can blend creativity with scale, leaning into technical implementations of analytics and integrations while offering something truly unique.
The Future of Agencies: Automation Meets Creativity
But what does a system that marries automation, scale, personality, and creativity look like? To stay competitive, agencies must build models that enshrine nuance, brand guidelines, voice tonality, and personality—all while scaling. The key is to create a framework that automates tasks and enhances the creative process. We’re talking about a system that can handle the nitty-gritty details of implementation while leaving room for creativity and strategic thinking.
This isn’t just about building out workflows in Make or Zapier. It’s about creating an environment where AI agents can take on the heavy lifting of data analysis, reporting, and even content generation, freeing up human talent to focus on higher-value activities. Imagine a world where an AI agent not only pulls performance data but also crafts insights, builds client-ready presentations, and even personalizes recommendations based on a deep understanding of the brand’s voice and goals.
Crafting a New Model
So how do agencies begin creating this hybrid model? The first step is embracing the reality that AI is not just a tool but a partner in the creative process. Agencies need to invest in systems that can integrate seamlessly with AI-driven analytics, data pipelines, and creative assets. The goal is to build a framework where data flows smoothly from one stage to the next, allowing AI to add value at each step while humans maintain the creative direction.
The process starts with mapping out what can be automated without losing the essence of the brand. It’s not enough to simply implement AI for efficiency; it’s about ensuring that every automated touchpoint still feels personal, on-brand, and creatively aligned. That requires a blend of AI-driven scale and human creativity—a balance that is difficult to strike but absolutely necessary.
Agencies that succeed in this will not just survive but thrive in an industry that is becoming increasingly commoditized. They’ll be able to offer unique value propositions by leveraging the full power of programmatic creativity, backed by the scale and precision of AI-driven systems.
How a Team of Three Can Leverage AI Without Engineers
For a small digital marketing team of three people, integrating AI without the immediate need for engineers requires a careful balance between automation and maintaining the nuance of manual oversight. Here’s how such a team can start implementing AI solutions while scaling up:
- Use Off-the-Shelf AI Tools for Automation: Start with tools that require minimal technical setup instead of building custom solutions. Use platforms like Zapier or Make to automate repetitive tasks, such as pulling reports, updating spreadsheets, and even posting updates across social channels. These tools allow non-technical users to set up workflows that integrate multiple platforms (Google Ads, Facebook, LinkedIn) with minimal coding knowledge.
- AI-Powered Campaign Optimization: Use tools like AdOptics or Pattern89 that provide AI-driven suggestions on creative, bids, and targeting. These platforms analyze historical performance and automatically recommend adjustments that align with your KPIs. While these tools offer automation, they keep a human in the loop to verify the suggestions, especially when market trends shift quickly.
- Content Generation and Personalization: Utilize AI writing tools like Copy.ai or Jasper for drafting initial versions of ad copy, blog posts, or email sequences. While these tools can accelerate content creation, the team should still fine-tune the outputs to match brand voice and current trends. In practice, you’ll want to feed the AI with recent, industry-specific inputs to keep the content relevant, avoiding stale or overused narratives.
- Data-Driven Decision-Making with AI Insights: Implement analytics tools like Supermetrics or Funnel.io that aggregate marketing data across platforms and apply AI-driven analysis. These tools help small teams understand which campaigns are underperforming and why, without spending hours in data wrangling. The goal is to make sense of performance trends while retaining flexibility in adjusting budget and bidding strategies.
Why Automation Alone Is Problematic
It’s easy to say, “Automate bid adjustments, budget shifts, and audience updates,” but that advice is often misguided. Marketing data fluctuates for various reasons: seasonal demand, sudden interest spikes, or unexpected downturns. A campaign that performs well in June might nosedive in August due to shifts in customer sentiment or external market forces. Automation tools that only follow preset rules can quickly become a liability if they don’t account for these rapid changes.
Here’s the challenge: predicting trends is akin to predicting the stock market in marketing. You need a mix of historical data analysis and an understanding of market sentiment to make informed decisions. In trading, a systemic approach often blends algorithmic strategies with human intuition to adapt to shifting market conditions. In marketing, you need similar flexibility. AI tools can spot patterns and automate responses, but they struggle with sudden trend reversals or fads that go stale quickly.
For instance, something “cool” during the summer—like a particular meme or cultural trend—might feel overdone and out of touch by October. How could AI ever truly grasp the subtlety of what’s still relevant versus what’s lost its edge? This is where human oversight becomes crucial. Small teams should focus on monitoring these shifts, using AI as a guide but maintaining control over final decisions.
The Reality of Task-Specific AI Agents
Why haven’t we seen AI agents seamlessly managing complex tasks across marketing platforms? The challenge lies in the very nature of these agents: they are either too rigid in scope or too broad, often requiring significant customization to deliver consistent results. Current task-specific AI agents excel in B2B environments where processes are well-documented and predictable. For consumers, though, it’s a different story. They want solutions that just work—no manual needed. The moment an AI stumbles, the experience degrades, and consumers are quick to voice dissatisfaction.
Business-facing AI tools, on the other hand, benefit from dedicated teams that can train and tweak them over time. Larger companies can commit to using these agents despite initial limitations because they have the resources to fine-tune them until they reach acceptable performance levels. For a small marketing team, investing in such tailored systems is out of reach, but using more general AI tools that can be incrementally trained is a practical first step.
Building Toward the Future: Hybrid Models with AI at the Core
For agencies and small teams to truly leverage AI agents, the focus should be on hybrid models where AI accelerates specific tasks but human creativity and strategic oversight remain central. A practical approach might involve:
- Starting Small with Workflow Automation: Begin by automating repetitive tasks that don’t require nuanced decision-making. This frees up time to focus on strategy and creative work.
- Leveraging Pre-Trained AI Models: Instead of building custom solutions, use AI tools that come pre-configured for common marketing tasks. Look for models that allow for fine-tuning as your campaigns evolve.
- Iterative Learning and Feedback Loops: Implement a feedback loop where human team members regularly review AI outputs, correcting mistakes and feeding these learnings back into the system. Over time, this approach builds a more resilient, context-aware AI framework.
Ultimately, as AI agents continue to evolve, the teams that succeed will be those that blend efficiency with creativity, harnessing the power of automation while preserving the unique touch that makes marketing effective. The goal isn’t to replace human insight but to augment it—allowing small teams to punch above their weight by strategically leveraging AI-driven tools.
Why Agencies Need to Move Beyond the Consulting Model
When you think about ad agencies, the comparison to consulting firms is clear. Both industries are rooted in a client-first culture, where success hinges on delivering tailored solutions at any cost. In consulting, the drive to meet sky-high client expectations leads to long hours and a constant push for perfection. The economics of consulting demand that firms staff projects leanly, maximizing revenue while stretching teams thin. This is why consultants often find themselves working around the clock—not just for clients but also to maintain internal operations and build the firm.
However, this labor-intensive, high-pressure model is unsustainable in the long run, especially in the context of modern marketing. The consulting approach relies heavily on human capital, but what happens when that capital becomes too costly or inefficient? This is where agencies need to pivot toward a more system-oriented model, much like what you see in industries like oil and gas.
Think about it: No one is trying to build an in-house gas station or an in-house Google. Why? Because these industries have achieved operational excellence through systems, not just people. They’ve built scalable processes that are so effective that attempting to replicate them in-house would be futile. The same logic applies to agencies that want to stay competitive.
Brands may flirt with the idea of building in-house marketing teams, but the reality is that few can replicate the sophisticated systems and integrations that top agencies can offer when they leverage AI and automation. The difference lies in moving away from a model dependent solely on labor to one built on scalable systems that integrate technology, creativity, and data-driven insights.
In industries like oil, the focus isn’t just on the workforce; it’s on the infrastructure, the machinery, the supply chains—all of which are optimized to run like clockwork. For agencies, this means developing proprietary tools, AI-driven platforms, and integrated workflows that create value far beyond what a typical in-house team can achieve. It’s about scaling through systematized creativity and automated processes that don’t rely on endlessly hiring more people.
Agencies must stop thinking of themselves as merely service providers and start seeing themselves as creators of critical infrastructure for brands. This infrastructure—comprising AI agents, automated systems, and seamless integrations—should be so essential that brands realize building it in-house is not only inefficient but impossible.
In the future, the agencies that thrive will be the ones that have evolved into this hybrid model: part creative powerhouse, part technology platform. They’ll be the ones who understood that winning in today’s landscape requires outcompeting not just other agencies but the very notion of in-housing itself. And just like no one is building an in-house gas station, brands will come to see that trying to build in-house marketing at scale is equally futile without the sophisticated systems that only the most forward-thinking agencies can offer.
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