Artificial intelligence has fundamentally transformed the marketing landscape, evolving from a futuristic concept to an essential component of modern marketing operations. It’s no exaggeration to state that we’re all using AI in some capacity, from sophisticated content generation and campaign optimization to predictive analytics.
The bigger questions we are asking, however, are how and why.
That’s the journey we’ve been on for a little while now. Still, after a few years of trying out tools, rethinking workflows, and tackling some tough questions (namely, is AI the right tool for what we do?), we’re finding our footing as a creative agency in the age of AI.
The Evolution of Generative AI
Artificial intelligence didn’t become a thing in the past five years. It’s been a thing for decades… in academia, across industrial and information-driven industries, and in creative tools like the Adobe Suite or video games.
But the evolution of AI in the past decade has been significantly different.
We aren’t strangers to this evolution. Through our collaboration with Microsoft and the emerging Project Bonsai, we were helping some brilliant individuals explore the expansion of specialized AI in the industrial, manufacturing, and big data industries. We helped specialists situate the early foundations of modern AI and understand how it would impact insurance analytics, as well as help companies plan the most efficient routes for construction vehicles.
At the same time, we saw massive innovation, particularly in the realm of generative AI.
The Rise of Generative AI
In the early 2000s, artificial intelligence research was primarily focused on machine learning, rule-based systems, and early natural language processing. These were the days when you’d see AI mentioned as an industrial technology or as a subject of research for data scientists (who, often, were creating game-playing algorithms to explore the potential for machine learning to develop independent agents).
These early systems lacked the “generative” part of “gen AI” we now associate with tools like ChatGPT or Midjourney. But they laid the groundwork, plugged into massive cloud computing systems, and leaned more on advanced machine learning algorithms. In 2006, Geoffrey Hinton introduced the concept of “deep learning,” reintroducing neural networks in a form that could process vast datasets more efficiently.
By the early 2010s, open-source frameworks, such as TensorFlow and PyTorch, accelerated progress in AI model training by expanding the development base. Following that, a couple of dams burst:
- In 2014, Ian Goodfellow and colleagues introduced Generative Adversarial Networks, a method for generating synthetic data such as images. GANs marked the first true leap in generative AI, where systems could actually output original images rather than just identify those patterns in existing photos.
- In 2018, OpenAI released GPT-1, a generative pre-trained system capable of completing basic text tasks. Over the next few years, it would evolve into one of the most powerful language models seen.
Now, in 2025, generative AI has transitioned from experimentation to strategic integration. Enterprise platforms, such as Salesforce, HubSpot, and Adobe now offer AI-enhanced campaign tools out of the box.
A Professional Understanding of AI
Tools are tools, and their strengths or limitations are tied to how we use them. If we are willing to tap machines that claim to handle creative, thinking tasks for us, we should give them a deep, critical look under the hood (as much as we can).
The Compelling Benefits of AI in Marketing
Efficiency and Cost Optimization
One of the most cited advantages of AI is operational efficiency. AI systems automate tasks that previously required a great deal of human time and energy, from drafting comprehensive blog posts and social media content to segmenting customer databases and optimizing ad targeting.
- Research conducted by ActiveCampaign revealed major efficiency gains, with small businesses using AI reporting average monthly savings of almost $5,000 and recovering approximately 13 hours of productive work time per week. These savings compound over time, enabling organizations to reallocate resources toward strategic initiatives, creative development, and building customer relationships.
- SurveyMonkey’s research found that 51% of marketers use AI to optimize content, and 45% use it to brainstorm. This allows marketing teams to respond more quickly to market changes, capitalize on trends, and maintain consistent communication across multiple channels without increasing staff.
Personalization at Scale
AI excels at delivering real-time personalization by analyzing vast amounts of behavioral data, browsing history, engagement metrics, and demographic information to tailor messaging and offers across email campaigns, advertising placements, and on-site experiences.
- Major platforms like Netflix and Amazon have long demonstrated AI’s personalization potential, utilizing algorithms to power recommendations that drive a significant portion of their revenue. Today, smaller marketing teams can deploy similar capabilities using accessible AI tools.
- A Synthesia report found that 65% of companies using AI for content personalization experienced better results.
Strategic Data Analysis and Competition
AI tools can analyze massive data sets to uncover insights that would require human analysts weeks or months to identify manually. Whether identifying high-converting customer segments, detecting early indicators of customer churn, forecasting campaign performance, or analyzing competitive positioning, AI transforms raw data into actionable strategic intelligence.
- This analytical capability enables more sophisticated experimentation and optimization strategies. McKinsey’s 2024 AI report found that organizations using AI experienced faster experimentation cycles and more accurate forecasting capabilities, with 65% of organizations now regularly using generative AI in at least one business function. This acceleration allows marketing teams to test and iterate rapidly.
- AI also helps close the gap between enterprise businesses and SMBs. A 2025 study by ActiveCampaign revealed that 75% of small businesses believe AI enables them to compete more effectively with larger competitors, while 77% reported that AI has improved the overall quality of their marketing work.
Where AI Creates New Challenges
Homogenization, Accuracy, and Trust
LLMs are great at recognizing and leveraging patterns to put together sentences. But they still are, essentially, pattern machines, and will create content as such. This means that we often see an inherent, recognizable uniformity that works well for generalized SEO but not for direct marketing or sales creative. Add to that the possibility of AI providing false data in a seemingly confident manner and you create the possibility for hollow content that neither provides accurate information nor offers an enjoyable reading experience for a diverse human audience.
A 2023 study systematically examined this phenomenon, finding that participants using AI assistance in writing tasks produced higher-quality content by traditional metrics but generated work that was significantly more similar across the entire group.
This homogenization problem is particularly challenging for marketers who depend on distinctive brand voice, unique creative storytelling, and differentiated positioning to build customer loyalty and market recognition.
A Loss of Emotional Resonance and Connection
Ignoring more nuanced conversations about machines and empathy, it remains the (practical) case that AI systems often lack an understanding of context, which limits their ability to create messaging that resonates on deeper emotional levels. The challenges of AI-generated emotional content were highlighted by the backlash to Google’s 2024 “Dear Sydney” Olympic campaign, which was widely criticized for tone-deaf messaging and a lack of emotional authenticity.
Consumer awareness and skepticism of AI-generated content are on the rise. The same SurveyMonkey data analysis found that 41% of consumers under 34 have negative feelings about companies using AI for customer experience, and a whopping 90% prefer human customer service reps over AI bots.
Scorch’s Approach to AI: Strategic Recommendations
Implement AI as Enhancement, Not Replacement:
The most effective approach treats AI as a powerful amplifier for human creativity, rather than a substitute. Organizations should utilize AI to generate initial drafts, summarize complex datasets, and automate tasks — not to replace creative functions. Harvard Business Review research indicates that the highest-performing marketing campaigns combine AI-generated insights and efficiency with human storytelling capabilities and editorial judgment.
Avoid AI-Washing:
This term refers to the practice of over-relying on AI or over-promoting the value or benefit of AI to customers. This cuts both ways: customers may become suspicious of over-promising value, and your teams might find themselves relying too heavily on AI, even when it provides false or biased content.
Don’t Get Vendor Locked:
There has been an explosion of AI platforms in the past two years, and it can get easy (and expensive) to platform-hop in search of the right solution. We’re not seeing AI as a hammer in search of a nail… we’re looking internally to see where those pesky nails are sticking up and how we can best hammer them into place.
The Path Forward for Creative AI
Successful marketing organizations in 2025 and beyond will be those that embrace AI for its strengths in speed, efficiency, data analysis, and scale while maintaining human control over creativity, empathy, ethical decision-making, and long-term brand building.
The future of marketing lies not in choosing between human creativity and artificial intelligence, but in developing the wisdom to combine them effectively in service of building meaningful customer relationships and sustainable business growth.
