AI Slop - Why sales and marketing deserve better

Over the last year, AI has been rapidly embedded into GTM workflows. Teams are generating outbound emails, LinkedIn posts, call summaries, and follow-ups at unprecedented speed. Using AI like this often looks like progress, but many leaders are seeing little change in the outcomes that actually matter.

Pipeline quality has not improved meaningfully, deals are not closing faster, and buyer engagement often feels worse, not better. The problem is not the model capability, it’s that most AI is operating without any real context of the business it is meant to support.

From the buyer’s side, this gap is becoming increasingly visible. Buyers are not rejecting AI, they are rejecting irrelevance. After years of templated outreach, and now AI-generated messaging layered on top, many have become highly sensitive to language that sounds personalised but clearly is not. The issue is that they fail to reflect a real understanding of the buyer’s situation, priorities, or constraints. When that happens repeatedly, buyers disengage long before a meaningful conversation ever begins.

The same lack of context affects both sales and marketing. What differs is how that gap shows up, and what it takes to address it in each.

Why AI slop shows up in marketing

Modern models can produce clean messaging with minimal instruction, but when they are not grounded in a clear strategy they default to generalities. The output becomes too safe and easy to ignore.

You see this in subtle but consistent ways. The same value propositions are recycled across very different markets, surface-level “personalisation” hooks appear without changing the substance of the message, and phrasing patterns repeat across inboxes, ads, and feeds. Over time, this convergence trains buyers to tune out. Not because the content is wrong, but because all it does is repeat existing ideas and language rather than offering anything meaningfully new.

This is why so much AI-generated marketing content feels familiar the moment you see it. It carries little informational value, and therefore little reason for a buyer to pay attention. And this causes a problem that many GTM leaders are now facing. As AI-driven outbound becomes more common, it becomes much harder to cut through the noise it creates.

This challenge surfaced repeatedly in our CRO Playbook, drawn from real roundtable conversations with revenue leaders and operators. This slide in the playbook surfaces the insights.

Cutting through the noise is no longer about sending more messages or optimising copy at the margins. It requires credibility across channels and signals that feel human, not automated. Thoughtful, differentiated outreach such as presence in communities that buyers already trust, peer recommendations, and even deliberately human touches (like handwritten notes) are a breath of fresh air in an AI-saturated market.

The point is not to reject AI, but to be deliberate about where it helps and where it hurts. When AI is used to mass-produce generic outreach, it accelerates noise. When it supports a strategy built on credibility and relevance, it reinforces what already earns attention.

Why AI slop shows up differently in sales

In sales, the problem is not that AI output is ignored, it’s that it is too generic to be useful.

You may have pasted call transcripts into an AI tool, or asked for advice specific to your business, and received output that technically made sense but felt obvious, shallow, or disconnected from reality. That is the version of AI slop sales teams experience most often. It sounds reasonable, but it does not help you make a better decision or run a sharper deal.

This does not mean AI cannot be valuable in sales. In fact, when it has the right inputs, it can be extremely effective. The issue is that AI does not automatically have the context of your business. It does not know your pipeline, your accounts, your stakeholders, or the history behind a deal unless that information is explicitly made available.

In sales terms, the missing context usually includes:

• The actual stage and health of the deal
• Who the real stakeholders are and how they are engaging
• What objections have already surfaced on calls
• Which product and positioning are in play
• How similar deals have succeeded or failed in the past

When this information is absent, the model is forced to generalise. Even strong reasoning produces surface-level guidance when it is disconnected from execution reality. The difference is not how the question is phrased, but how much of the business truth the model is allowed to see.

How Hive Perform prevents sales AI slop

Hive Perform is designed to solve this exact problem. Rather than generating generic AI advice in isolation, Hive keeps the model continuously connected to the living context of your pipeline.

Talk tracks are informed by your sales methodology, your product messaging, and what is actually happening across calls, emails, and deals. When Hive surfaces guidance, it reflects real execution patterns. What messaging is landing with buyers, where deals tend to stall, and how top performers handle similar moments.

The model supplies the reasoning, Hive supplies the context.

That combination is what turns AI from a source of vague suggestions into a tool that sharpens judgment, improves deal execution, and supports reps in real conversations.

The takeaway

AI slop is not an inevitable outcome of using AI, it is the result of using AI without context.

In marketing, slop shows up as generic messaging that buyers ignore.

In sales, it shows up as generic advice that cannot guide real decisions.

The teams that see real impact will not be the ones producing more AI content. They will be the ones grounding AI in clear strategy, clean inputs, and the lived reality of their business.

If you want to see how contextual talk tracks change real deal conversations, explore Hive Perform. And for a deeper look at how to avoid generic output across your GTM motion, download the CRO Playbook here.