Artificial intelligence is changing how marketing functions operate. The question most senior leaders are actually asking isn't whether to adopt it — it's where to apply it, what to protect from it, and how to govern it without slowing everything down.
Those are judgment questions. And judgment is what this series is built around.
AI in Practice is BlindSpot's applied research and perspective on how enterprise B2B marketing functions — product marketing, demand generation, brand, and beyond — can deploy AI with clarity and discipline.
This is not a tools directory or a prompt library. Every piece of content here is written for senior practitioners and leaders who need to make real decisions about how AI fits into their function, their team, and their GTM motion.
Every post is written from operational experience, not vendor positioning. BlindSpot works inside enterprise B2B marketing functions. What we publish here reflects what we've seen work, what we've seen fail, and where the real risk in AI adoption tends to hide.
AI creates genuine leverage in marketing functions — but capturing that leverage requires judgment, not just access.
The handoff from AI to human judgment is non-negotiable in certain areas — and getting that boundary wrong is costly.
Governance doesn't have to mean bureaucracy — but it does have to mean intentionality.
The organizations using AI to compound their advantage are doing something fundamentally different from those generating more output nobody reads.
Every article, framework, and assessment in this series addresses one or more of these questions.
And what does capturing that leverage actually require — in terms of process, data, and human oversight?
And why does getting that boundary wrong tend to be more costly than not using AI at all?
The goal is clarity and speed — not compliance theater. What does that actually look like in a marketing function?
The difference isn't the model. It's the judgment layer that decides what to do with the output.
Product marketing sits at the intersection of the decisions AI is most likely to influence and the judgments AI is least equipped to make. Most PMM teams adopt AI by task, not by framework. Here's how to map your responsibilities across what to automate, augment, and protect.
Read the article →Product marketing sits at the intersection of the decisions AI is most likely to influence and the judgments AI is least equipped to make.
Maps the full PMM responsibility set against AI applicability — identifying highest-leverage areas for deployment and the decisions that must stay with a human leader.
Updated as new content publishes.
A scoring rubric and a four-gate method — Score, Qualify, Cluster, Sequence — for deciding which PMM programs belong in the Automate, Augment, or Protect zone, applied to a real 70-asset operating system.
Read article →An inside look at building BlindSpot's free AI-powered GTM diagnostic — why the questionnaire was cut to 12 questions, why competitive positioning was left out as its own category, and why the tool gives direction rather than a prescriptive fix.
Read article →Most PMM teams adopt AI by task, not by framework. Here's how to map your responsibilities across what to automate, augment, and protect.
Read article →AI has evolved from tool to infrastructure, becoming the new operating system for product marketing. How to evaluate your strategy, operations, and messaging to achieve clarity, connection, and scale.
Read article →The transformative impact of AI on modern marketing, balancing innovation with ethical considerations — and strategies for responsible adoption that drive growth while maintaining trust.
Read article →If your organization needs a structured, senior-led approach to deploying AI in product marketing or GTM — BlindSpot can help you do it with clarity and discipline.
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