How can GTM teams deliver AI search results (with MCPs)?

Alex Halliday and Berna Gonzalez, co-founders of AirOps, break down how modern GTM teams use Claude, AirOps, and MCPs to turn performance data into action. The session shows how AI assistants surface real insights and generate executive-ready reporting without engineering support.

Top 5 takeaways

1. Marketers can use AirOps and Claude to automate reporting, generate insights, and create executive-ready assets without relying on engineering teams.
2. Providing AI assistants with deep, brand-specific context is the primary driver of accurate, actionable outputs.‍
3. AirOps connectors let users access, analyze, and export data directly into tools like Notion, dashboards, and branded decks, streamlining everyday marketing workflows.
4. AI workflows help teams move from raw data to prioritized actions, including identifying content gaps, competitor opportunities, and pages that need refreshing.
5. The rapid pace of AI and MCP development means marketers should experiment, iterate, and customize workflows to stay ahead of new possibilities.

Key learnings from MCP-powered GTM workflows

As AI becomes embedded in daily work, the real advantage is no longer access to models. It is how effectively teams connect AI to their data, workflows, and decision-making systems.

This session shows how GTM teams use Model Context Protocol to move from insight to impact, turning AI from a chat interface into an execution engine.

1. Marketers can automate reporting and insights without engineering support

One of the biggest shifts highlighted in the session is that marketers no longer need engineering resources to operationalize AI. With AirOps and Claude, teams can automate reporting, analyze performance, and generate executive-ready assets directly from prompts.

This removes bottlenecks that traditionally slow GTM teams and helps leaders move faster from question to decision.

Why it matters for AEO, SEO and GTM

• Faster reporting cycles mean faster optimization
• Teams can self-serve insights without waiting on data teams
• AI becomes a daily operational tool, not a side experiment

Deep, brand-specific context is the key to accurate AI outputs

AI quality is not just about the model. It is about context.

Providing structured, brand-specific context through MCP significantly improves relevance, accuracy, and actionability. Without context, AI guesses. With context, AI executes.

What strong context enables

• More accurate insights tied to your actual performance data
• Outputs aligned to your brand voice, priorities, and goals
• Fewer generic recommendations and hallucinations
  

This is the foundation of reliable AI workflows.

The AirOps Claude Connector turns AI into a direct interface for your data

The AirOps Claude Connector allows marketers to query, analyze, and export data directly into tools they already use, including Notion, dashboards, and branded slide decks.

Instead of pulling CSVs, rebuilding charts, or rewriting summaries, teams can generate polished outputs in minutes.

Impact on day-to-day workflows

• One prompt replaces multiple manual steps
• Reports are consistent and reusable
• Insights flow directly into planning and execution documents

Example prompts include:

• “What is my AI visibility trend over the last 30 days by topic?”
• “Create a PowerPoint slide I can share with my CMO.”

AI workflows accelerate the move from data to action

Raw data does not drive growth. Prioritized action does.

The session shows how AI workflows help teams quickly identify:

• Content gaps and underperforming pages
• Competitor opportunities and visibility weaknesses
• Pages that need updating based on freshness and demand
  

Instead of staring at dashboards, teams get clear next steps they can act on immediately. This is especially powerful for SEO, content, and AI search optimization teams managing large content libraries.

Rapid experimentation is required as AI and MCP evolve

The pace of change in AI, MCPs, and agent-based workflows is accelerating. The teams that win will not wait for a perfect playbook.

They will experiment, iterate, and customize workflows continuously.

What winning teams do

• Test new MCP workflows early
• Refine prompts and context based on real outputs
• Share learnings across growth, content, and product teams
  

AI advantage compounds through iteration, not perfection.

From AI Insights to Real Execution

Model Context Protocol is the layer that connects AI to your real data, tools, and workflows. By preserving context across systems, MCP enables AI to move beyond insights and deliver repeatable, execution-ready outputs for GTM teams.

Check out the guide to kickstart your AirOps Claude Connector.  For full setup details, visit the AirOps MCP documentation.

Go deeper with these resources:

• Apply to AEO Conf on 2/19 in San Francisco
  Join CMOs and senior growth leaders for closed-door conversations on what’s actually working in AEO, AI search, and modern growth systems. Speakers from OpenAI, Reddit, G2, Webflow and more will be there. Apply here.
• Page 360 in AirOps. Find AI search signals, clicks, impressions, CTR, sessions, and freshness dates all in one place within AirOps. Learn more.

Ready to see how AirOps helps teams use these strategies and create great content? Book a call to get started.