Where AI Falls Short: The Human and Community Layer GTM Still Needs

# Format: Best Practices & Playbooks

# Theme: Emerging Tech

# Challenge: AI Adoption

# Role: Community/DevRel

AI is transforming GTM, but it can’t replace human judgment or community insight.

September 22, 2025 · Last updated on November 7, 2025

Joshua Zerkel

Artificial intelligence is reshaping go-to-market, from predictive analytics in sales to generative content in marketing. These tools promise speed and scale, and many teams are seeing real gains. But speed without context can create new risks. AI doesn’t understand nuance, empathy, or trust the way humans and communities do. The most effective GTM leaders in 2025 are not asking “How much can we automate?” but “Where do humans and community add irreplaceable value?”

The answer lies in balance. AI amplifies efficiency. Human expertise and community insight ensure relevance, trust, and connection. Here are the areas where AI falls short — and where human and community layers are essential.

Understanding motivations, not just behaviors

AI is good at detecting what people do. It struggles to explain why. Coursera, for instance, can use algorithms to recommend courses based on browsing and completion data, but it’s the instructor-led forums and peer discussions that uncover the motivations behind those choices. GTM leaders need both: AI to highlight behavior patterns and community to explain the “why” that drives them.

Key takeaways:

Pair behavioral data with qualitative insights from community.

Use community discussions to reveal motivations that numbers can’t.

Incorporate human interpretation into GTM planning alongside AI models.

Treat “why” questions as a human responsibility, not an AI output.

Building trust through authenticity

AI can generate content quickly, but authenticity requires human voices. Notion’s growth owes much to its thriving template-sharing community, where real users publish their workflows. These authentic contributions carry credibility that AI-generated materials cannot replicate. GTM leaders must resist over-automation and make space for human-driven storytelling.

Key takeaways:

Use AI for drafting and efficiency, but amplify authentic user stories.

Encourage leaders and practitioners to share their own playbooks.

Audit AI-generated content for tone and credibility before publishing.

Position authenticity as a growth driver, not just a brand value.

Navigating ethical and cultural nuance

AI often misses nuance, especially across cultural contexts. Adobe’s Behance platform thrives because it is built on peer recognition of creative work, with human judgment driving what gets elevated. Algorithms alone cannot capture the cultural or artistic significance of certain contributions. Community input provides the guardrails that keep GTM relevant and respectful.

Key takeaways:

Involve diverse community voices in campaign and product reviews.

Use peer and customer perspectives to catch cultural blind spots early.

Combine AI efficiency with human oversight in sensitive contexts.

Treat nuance as a strategic advantage, not just a risk to manage.

Responding to emerging risks

AI can surface anomalies, but communities often detect risks first. In Salesforce’s Trailblazer Community, members openly share reactions to new features or policy changes, often highlighting potential friction before it shows up in customer support or sentiment data. These signals help GTM leaders respond early and adjust messaging proactively.

Key takeaways:

Monitor community discussions for early warning signs.

Share qualitative signals alongside AI-driven sentiment analysis.

Train GTM teams to recognize which risks require immediate human response.

Close the loop with customers to show responsiveness.

Creating belonging and advocacy

AI can recommend next steps, but it can’t create belonging. Advocacy emerges when customers feel recognized and supported as part of something larger. Khan Academy illustrates this dynamic: while AI tutors adapt lessons to student progress, the forums and teacher communities provide encouragement and recognition that sustain long-term engagement.

Key takeaways:

Treat community as a retention and advocacy multiplier AI can’t replicate.

Create spaces for customers to connect and celebrate progress.

Recognize advocates publicly to deepen loyalty.

Position belonging as a core GTM outcome, not a byproduct.

Why this matters for GTM leaders

AI accelerates execution, but without human judgment and community insight, GTM risks losing trust, nuance, and connection. Leaders who integrate all three layers (AI for efficiency, humans for authenticity, and community for belonging) build stronger, more resilient strategies.

AI can make GTM faster, but it can’t make it meaningful. The human and community layers are what turn efficiency into lasting growth.

Where have you seen human or community insight make the difference when AI alone wasn’t enough?

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Table Of Contents

Where AI Falls Short: The Human and Community Layer GTM Still Needs

AI is transforming GTM, but it can’t replace human judgment or community insight.

Understanding motivations, not just behaviors

Building trust through authenticity

Navigating ethical and cultural nuance

Responding to emerging risks

Creating belonging and advocacy

Why this matters for GTM leaders

Popular

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