The AI Content Trap: Multiplying Mediocrity at Scale

Marketing has always suffered from a volume addiction; however, the advent of generative AI has turned a bad habit into a terminal condition. In the recent discussion with Volker Hildebrand in our CRMKonvo, we explored the uncomfortable reality that while AI has made marketing faster and cheaper, it has largely failed to make it better. The cynical view, which I happen to hold is that marketers frequently confuse the amount of content produced with the actual impact on the customer. We are now in an era where everyone has the same tools to flood the market with what in the words of Volker just “multiplies mediocrity” – or in mine creates instant mediocrity.

The core problem is that generative AI multiplies mediocrity by definition. It ingests existing data and spits out an average of what is already there; consequently, when every startup uses these tools to build their websites and social posts, they all end up saying the same. If you look at the CRM space today, the messaging is often nearly indistinguishable. Everyone promises "revolutionary" efficiency and "seamless" integration. As Volker noted, this is a trap for startups; if they cannot differentiate their story, they simply will not survive the noise.

TL;DR

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The Productivity Mirage

Vendors love to sell AI based on productivity gains. They promise you can save 20 percent of your time on content creation. But as we discussed, productivity is a hollow metric if you do not have a plan for that saved time. If you save 20 percent of your time just to produce 20 percent more "slop" or low-quality content, you have solved nothing, nor have you saved anything. You have actually made the problem worse by increasing the background noise for your customers. The real question for any marketing leader hence is how that saved time can be reinvested into understanding and resolving the customer's actual pain points.

Product Marketing: The Center of the Universe

Volker makes a compelling case for Product Marketing as the organizational "center of the universe". In a tech landscape where products are increasingly complex, the role that sits between engineering, sales, and the customer is the only one capable of maintaining narrative integrity. He argues that this role should report directly to the CEO. This is because the items product marketing owns: pricing, packaging, roadmap strategy, and win-rate optimization: are the literal lifeblood of the company.

If you bury this function under a traditional marketing silo, it becomes a "content factory" for sales decks and brochures. When it reports to the top, it becomes a strategic filter. In the age of AI, this filter is more necessary than ever. AI can draft a battle card, but it cannot understand the nuanced political reality of a specific enterprise buying center.

The "center of the universe" concept is about architectural integrity. Product Marketing must ensure that the technology actually solves a business problem rather than just serving as a shiny new feature to mention in a press release. If the messaging does not address what keeps the customer up at night, then all the generative AI in the world will not improve your win rate.

From Personalization to Individualization

We have been chasing "personalization" since the 1990s; Volker’s PhD thesis touched on it back in the 90s. Yet, most of what passes for personalization today is still just "segmented mass marketing". The real shift happens when we move toward true individualization. This could be the death of the "campaign" as we know it. After all, a campaign is, by its nature, a scattergun approach that is irrelevant to most people in the target group.

True individualization, powered by a combination of predictive and generative AI, means the customer journey is unique to the person. If Thomas visits a site, he sees the architectural whitepaper because the predictive engine knows he’s an analyst. If a procurement officer visits, they see the ROI calculator. This isn't just swapping a name in an email: it's a dynamic reconstruction of the entire engagement layer.

The Rise of the Agentic Customer

Perhaps the most significant strategic shift on the horizon is the rise of the "agentic" customer, the shift from B2B (Business to Business) to B2A (Business to Agent). We are rapidly approaching a time when the initial 80 percent of a purchase journey isn't conducted by a human researcher, but by an AI agent. When 80 percent of the interaction is bot-to-bot, traditional marketing fluff becomes useless. How do you market to a bot? You can't appeal to its emotions with a fancy hero image or a catchy slogan. You have to provide structured, high-quality, verifiable data that the agent can ingest. The bot cares about structured data, proof points, and reliability. This will force a radical return to "the fundamentals" Volker repeatedly mentioned: reliability, relevance, and proof points. Marketing leaders must prepare for a reality where their "customer" is no longer a person, but an agent looking for the most rigorous solution to a defined problem.

A Pragmatist’s Guide to Avoiding the AI Slop-Pocalypse

If you are an enterprise leader looking to "AI-enable" your customer experience, stop listening to the vendor slide decks for a moment. Take a breath. You might be about to make a very expensive mistake if you don’t follow three simple rules.

Here is the pragmatist's guide to not making that mistake.

Prioritize Predictive Over Generative

Currently, everyone is obsessed with the "Gen" in GenAI, but for CX, the "Predictive" side is often more valuable. Don’t just buy tools that just help you write faster. Instead, use AI to identify patterns: which customers are about to churn, which leads are actually ready to buy, and what content actually helps close deals. As Volker noted, tools that track actual consumption or customer journeys (did they stop at the pricing page?) provide infinitely better data than "clicks". Use AI to find the needle; don't just use it to make a bigger haystack.

Kill the "Auto-Pilot" Content

If your marketing team is using AI to generate content and pushes it directly to customers without a rigorous human-in-the-loop review, you are actively eroding your brand equity. AI-generated content is, by definition, an average of everything that already exists on the internet – it’s instant mediocrity. It cannot innovate; it can only regurgitate. Use AI for drafts, for brainstorming, and for reformatting (e.g., turning a long webinar into short clips, or a blog), but never for the final "voice". Authenticity is about to become your scarce resource.

The Training Gap is a Strategic Risk

You cannot simply buy a subscription for a tool and expect "transformation". The most common failure point is the "dump and run" approach. If you aren't investing in training your people on how to prompt, how to critique AI output, and how to integrate these tools into a unified RevOps workflow, you are just buying shelfware, or worse, something counter-productive. AI is a skill, not just a software category. If your team doesn't understand the "human-in-the-loop" necessity and how to work with AI, they will eventually be replaced by the very mediocrity they are producing.