“If AI can write, why are we still paying writers?” This question likely resonates with any CMO or senior manager navigating tight budgets. The idea is undeniably seductive: humans are expensive and time-consuming, so why not replace them with clever machines, cut costs, and boost productivity?

This perspective is understandable. After years of high inflation, rising interest rates, and disrupted supply chains, organizations globally are seeking cost reductions. CFOs and executive teams often rebrand “cost cutting” as “cost transformation,” but the underlying challenge remains the same.

Marketing departments are certainly feeling the pinch. Gartner reports that the average marketing budget, which stood at 11% of overall company revenue in 2020, dropped to 9.1% by 2023, and is now at 7.7%.

Some organizations justify these cuts by assuming AI renders larger teams and budgets unnecessary. We’ve already seen companies drastically reduce their content teams, believing that a few individuals skilled in prompt engineering are sufficient. Yet, another Gartner study reveals that 59% of CMOs report insufficient budgets to execute their 2025 strategies, suggesting a disconnect.

Conversely, other organizations completely shun AI for content creation due to concerns about quality control, data privacy, complexity, or the belief that AI is a fleeting trend. Both camps likely believe their approach is rational and financially sound. Both are dangerously mistaken. AI is neither the sole solution nor the fundamental problem.

Beeching’s Axe: A Cautionary Tale

Spanish philosopher George Santayana famously wrote, “Those who cannot remember the past are condemned to repeat it.” This wisdom offers a crucial lesson for today’s AI discussions.

In the 1960s, British Railways faced financial woes. The Conservative Government appointed Dr. Richard Beeching, a physicist with no transport experience, to make the railways profitable. Beeching’s solution was straightforward: eliminate all unprofitable routes by assessing passenger numbers and operational costs in isolation. Between 1963 and 1970, his cost-cutting measures led to the closure of 2,363 stations and over 5,000 miles of track (about 30% of the network), resulting in 67,700 job losses.

Decades later, Britain is spending billions to rebuild some of these very routes. It turned out that many “unprofitable” lines were vital not only to the broader rail network’s health but also to regional communities in ways Beeching’s narrow financial analysis failed to grasp. Many businesses today are making their own version of these “Beeching cuts” in content strategy.

The Data-Led Trap

There's a critical difference between being data-led and data-informed. Understanding this distinction can prevent a modern Beeching-style catastrophe in content production.

Data-led thinking treats available data as the complete picture, identifying patterns and accepting them as undeniable truths dictating a clear course of action. For example: “AI generates content for a fraction of our current costs. Therefore, we should replace our writers.”

Data-informed thinking, however, seeks to understand the patterns, identify missing information, and stress-test conclusions. Data becomes a starting point for inquiry, not an endpoint for decisions. It asks: “What value isn’t captured in this data? What would replacing our writers with AI truly mean for our content’s effectiveness when competitors can use the exact same tools?”

This last question highlights the real challenge for companies considering AI-generated content, and its answer won't be found in a spreadsheet. If you can use AI to create content with minimal human input, so can everyone else. Soon, everyone will be generating similar content on similar topics for the same audiences, recycling information and reheating “insights” from identical online sources.

ChatGPT won't magically produce a better blog post for you than for anyone else asking for 1,200 words on the same topic. You need to add your unique value. There is no competitive advantage in relying solely on AI-generated content.

AI-generated content is not a silver bullet. It’s the minimum benchmark your content needs to significantly exceed if your brand is to stand out in today’s noisy online marketplace.

Unfortunately, while organizations recognize the need for content, too many senior decision-makers don’t fully grasp its strategic purpose, let alone the complexities of an effective content strategy.

Content Isn’t A Cost, It’s An Infrastructure

Marketing content is often undervalued, seen as simpler or less significant than other forms of writing. Yet, it arguably has the most challenging job. Every article, ebook, LinkedIn post, brochure, and landing page must fulfill a myriad of strategic requirements.

Of course, your content needs to be informative, backed by solid research. But each piece also plays a strategic role: attracting audiences, nurturing prospects, or converting customers, all while aligning with the brand’s messaging at every stage.

Your content must build authority, earn trust, and demonstrate expertise. It needs to be memorable for brand awareness and recall, and distinctive enough to differentiate your brand. It must be structured for search engines with the right entities, topics, and relationships, without losing the attention of busy human readers. Ideally, it should also include quotable lines or interesting statistics for social media distribution.

While ChatGPT or Claude can string together convincing sentences, expecting them to manage all these strategic demands to the same standard as a skilled content creator will lead to disappointment. No matter how detailed your prompt, something will always be missing. You’re asking AI to synthesize brilliance by merely recycling existing information.

This brings us to a crucial irony: with the rapid adoption of AI-mediated search, your content now needs to become a source that large language models will confidently cite. Expecting AI to create content that AI will then cite is like watching a dog chase its tail—futile and frustrating. If AI provided the information and insights in your content, it already has better, more authoritative sources. Why would AI cite content lacking fresh information or insight?

If your goal is to increase your brand’s visibility in AI responses, your content must offer what can’t easily be found elsewhere.

The Limitations Of Online Knowledge

Despite appearances, AI cannot think, understand, reason, or imagine in the human sense. These terms, often used as euphemisms for how AI generates responses, set incorrect expectations.

Crucially, AI cannot use information that isn’t already available and crawlable online. While we often perceive the internet as a vast repository of human knowledge, it’s far from complete. Much of our world cannot be captured as structured, digitized information. AI can tell you when the next local collectibles market is, but it can’t tell you which dealer has that rare comic you’ve been seeking for years—that requires in-person digging.

Then there are cultural histories and localized experiences that exist more in verbal traditions than in history books. AI can provide extensive information about World War I, but it struggles with topics like the Iranian famine during WW1, which is not widely documented outside Iranian historical texts. Much of this knowledge comes from personal stories passed down through generations, like a great-grandmother’s account of surviving on one almond a day—stories AI cannot access.

AI cannot draw upon the wealth of personal experience and memories we all possess. The greatest source of knowledge is human. It’s always us.

However, while AI cannot do your thinking for you, it can still be an invaluable aid.

You Still Need A Brain Behind The Bot

To be clear: I use AI every day, and my team does too. You should as well. The problem isn’t the tool itself; it’s treating the tool as a strategy, particularly one focused solely on efficiency or cost reduction. Marketing isn't the only sector learning this lesson. Another industry has already discovered that AI doesn't simply replace existing roles.

A recent survey by the Australian Financial Review (AFR) found that most law firms use AI tools. However, far from reducing headcount, 70% of surveyed firms *increased* their hiring of lawyers to vet, review, and approve AI-generated outputs.

This isn’t a failure of their AI strategy because their strategy was never about reducing headcount. They use AI as a digital assistant for tasks like research, drafting, and document handling, freeing up lawyers’ time for strategic, insightful thinking that generates real business value.

Similarly, AI is not a like-for-like replacement for your writers, designers, and other content creators. It’s a force multiplier, helping your team reduce the drudgery that often hinders high-value work. AI can assist with:

  • Summarizing complex information.
  • Transcribing interviews.
  • Creating outlines.
  • Drafting related content like social media posts.
  • Checking content against brand style guides for consistency.

Some writers even use AI to generate a very rough first draft to overcome writer's block. The key is to treat this output as a starting point, not a finished article.

These tasks are massive time-savers, freeing up mental bandwidth for the high-value work AI simply cannot do as well. AI can only synthesize content from existing information; it cannot create new knowledge, generate fresh ideas, interview subject matter experts for hidden insights, or draw upon personal experiences to make your content truly unique.

AI is also prone to algorithmic biases, potentially skewing your content and messaging without your awareness. For instance, the majority of AI training data is in English, creating a significant linguistic and cultural bias. An experienced eye is often required to spot subtle hallucinations or distortions.

While AI can accelerate execution, you still need skilled, experienced creatives for the real thinking and crafting.

You Don’t Know What You Have, Until It’s Gone

Until Beeching closed the line in 1969, the route between Edinburgh and Carlisle was a vital transport artery for the Scottish Borders. On paper, it was unprofitable according to Beeching’s simplistic methodology. However, its closure had massive ripple effects, reducing access to jobs, education, and social services, and impacting tourism. Forcing people onto buses or into cars strained other transport infrastructures.

Beeching solved one narrowly defined problem but undermined British Railways’ broader purpose: ensuring mobility for people across Great Britain. He effectively shifted the consequences and cost pressures elsewhere.

The route was partially reopened in 2015 as The Borders Railway, costing an estimated £300 million to reinstate just 30 miles of line with seven stations.

Beeching’s cuts illustrate the folly of evaluating infrastructure (or content strategy) purely on narrow, short-term financial metrics. Organizations that dismantle their teams in favor of AI will likely find it difficult to reverse course and repair the damage years down the line. Replacing writers with AI risks eroding the connective tissue that defines your content ecosystem and underpins long-term performance: authority, context, nuance, trust, and brand identity.

Experienced content creators won’t wait for organizations to realize their true value. If enough leave the industry, and with fewer opportunities for the next generation to gain skills, the talent pool will shrink significantly. As with the Beeching cuts, rebuilding your content team will likely cost far more in the long term than any short-term savings, especially when factoring in months or years of low-performing content.

Know What You’re Cutting Before You Wield The Axe

According to your spreadsheet, AI-generated content may indeed appear cheaper to produce. However, the effectiveness of your content strategy doesn’t hinge on publishing more for less. This isn’t a situation where just “any old content” will suffice.

Beware of falling into the Beeching trap. Your content workflows might only seem “loss-making” on paper because your current metrics fail to capture all the ways your content delivers strategic value to your business.

Content is not a cost center; it never was. Content is the infrastructure of your brand’s discoverability, making it more critical than ever in the AI era. This isn’t a debate about “human vs. AI content.” It’s about equipping skilled people with the tools to create work worthy of being found, cited, and trusted.

So, before you start swinging the axe, ask yourself: Are you cutting waste, or are you dismantling the very system that makes your brand visible and credible in the first place?

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