The hidden costs of modern content creation and how marketing teams are caught between the AI ‘devil’ and the human ‘deep blue sea’

How green was my content!

Confession time! When I started creating marketing content, SEO was a dream in some Silicon Valley techie’s brain. Marketers rode successive waves of website writing, keyword-focused blog writing, sharp social media creation with hashtags and SEO-driven marketing. Now, it’s the turn of prompt engineering. 

Transformation is an understatement for what we’re experiencing now. From AI-driven SEO keyword research to AI-assisted content generation, marketing teams are witnessing an AI tsunami in their tasks and tools as the output of LLMs makes its way to websites, social platforms, and marketing materials. Yet, despite these technological leaps, creating quality content has paradoxically become more complex and expensive than ever before.

Marketing teams now find themselves in an uncomfortable position—trapped between the efficiency promised by AI and the reliability delivered by human writers. This predicament isn't merely about choosing between two options; it's about navigating a complex ecosystem of tools, skills, and costs that weren't part of the equation just a few months(!) ago.

The One-Size-Fits-All AI Trap

“ChatGPT or Claude? How about Jasper? Have you checked WriteSonic? DeepSeek does a great job of reasoning while it’s working! Hey, Perplexity reports can perhaps make great blogs!”  – water cooler talk in agencies and marketing hangouts, circa 2025.

Small to medium marketing teams nowadays find themselves overwhelmed by generic AI platforms that promise the world but deliver more – complexity, that is. These platforms typically offer a bewildering array of features designed to cater to enterprises with dedicated AI teams—not the modest marketing department trying to produce next week's blog posts.

Consider the typical scenario: A marketing manager logs into their AI platform, and starts surfing the web for the latest and greatest prompt to get quality blogs. She’s spoilt for choice with Large Language Models (LLMs) to talk to, each with a cheery interface that serves up good content for detailed prompts but many a time, degenerates into some hallucination or the other as the chat progresses. The words sound fabulous, the sentences flow, but read the blog once or twice, and she emerges puzzled - Did I read that right? Is that run-on sentence really leading up to something? Is it really making sense? Within a few blogs, she tires of the repetitive phraseology and approaches. Anything more complex needs sophisticated prompts. Should I sign up for a prompt engineering course then?, she scratches her head.

Then, there are the ‘marketing AI’ platforms with interfaces that seem to require a computer science degree to navigate effectively. What should be a simple task—generating a simple marketing blog or listicle, or social media post—becomes a technical ordeal.

The complexity doesn't end with the interface. Marketing teams must also contend with the reality that most AI platforms are designed as general-purpose tools rather than specialised content creation assistants. The brand voice doesn’t match the topic suggestions! We’re an edtech firm - why is it suggesting meditation-related topics?

Features crucial for marketing teams are often buried beneath layers of functionality more relevant to developers or data scientists. Who wants to be confronted with 50 prompt widgets when you have a sharp set of asks for the month?

The Prompt Engineering Tax

Perhaps the most unexpected cost in modern content creation is the learning curve required to use AI effectively. Prompt engineering—the art and science of crafting instructions for AI models—has become a prerequisite skill for content marketers.

This skill doesn't come easily. Marketing teams must invest significant time in crafting, refining, and testing prompts to get usable output from AI systems. What begins as a simple request often evolves into a multi-paragraph instruction set, complete with examples, constraints, and formatting guidelines. This "prompt tax" is rarely accounted for when organisations consider the efficiency gains of AI.

Even more concerning is how this skill requirement has created a new divide between marketing teams. Those with the resources to hire dedicated prompt engineers or train their staff extensively gain a significant advantage, while smaller teams struggle to extract valuable content from the same AI tools they're paying for.

The Verification Burden

Once content is generated, the work has only just begun. Marketers must now verify everything the AI produces—a necessary but time-consuming process.

Failure to verify AI content comes with serious risks: reputation damage from publishing inaccurate information, legal issues from inadvertently plagiarised content, or simply publishing material that doesn't align with the brand's voice or messaging strategy.

The verification process itself has become increasingly complex. Teams must run content through plagiarism detection tools to ensure originality, fact-check statements that may be hallucinated by the AI, and review the tone and style to ensure consistency with brand guidelines. Some organisations have even begun using AI content detectors to identify and potentially revise sections that sound too obviously machine-generated.

Each of these verification steps requires additional time and tools, adding layers of complexity to what should be a straightforward content production process.

Tool Subscription Overload

The modern content marketer's toolkit has expanded exponentially, and with it, the associated costs. Today's marketing teams typically maintain subscriptions to multiple services:

LLM subscriptions provide access to the AI models that generate initial content drafts. These can range from general-purpose platforms to specialised content generation tools, each with its own pricing model and limitations.

Plagiarism detection tools have become essential to ensure content originality, particularly when working with AI-generated material that might inadvertently replicate existing content.

Grammar and style checkers help refine both human and AI-written content, ensuring grammatical correctness and stylistic consistency across all marketing materials.

SEO optimization tools analyse content for search engine visibility, suggesting keywords, headings, and structural changes to improve ranking potential.

Individually, each tool may seem reasonably priced, but collectively, they create a significant recurring expense that often exceeds what was initially budgeted for content creation, not to mention the time needed to train on the tools and integrate them into marketing workflows.

The Human Writer Premium

Despite the rise of AI, human writers remain invaluable for creating truly distinctive content. However, quality human writing has become increasingly expensive, creating a difficult choice for marketing teams: pay premium rates for human-crafted content or accept the limitations and additional workload that comes with AI.

The scaling problem is particularly acute. While AI content can scale almost infinitely at minimal marginal cost, human-written content scales linearly with cost. Doubling your content output means doubling your investment in human writers.

Many organisations have turned to freelance networks as a compromise, but this introduces new challenges around consistency, availability, and management overhead. Finding writers who truly understand a brand's voice, industry, and audience takes time—a resource already stretched thin for most marketing teams.

The Content Creation Dilemma

Today's marketing teams find themselves in a content creation dilemma: caught between imperfect AI solutions that require significant time investment to use effectively and increasingly expensive human writers who provide quality but at a scale that's difficult to sustain.

This predicament isn't simply about choosing between human and machine—it's about finding a sustainable approach that leverages the strengths of both while minimising the hidden costs that have made content creation so challenging in the modern landscape.

As the industry continues to evolve, forward-thinking organisations are searching for solutions that bridge this gap, offering the scalability of AI with the quality and strategic thinking of human expertise. For marketing teams feeling the pressure from both sides, relief may soon come in the form of more specialised tools or platforms designed specifically for their workflow and content needs.