How to use Deep Research for GTM

Posted on August 13, 2025
Source: Kyle Poyar’s Growth Unhinged by Torsten Walbaum. Read the original article

TL;DR Summary of Mastering Deep Research with AI: Practical GTM Use Cases and Tips

Deep Research within AI tools like ChatGPT revolutionizes how fast-growing startups conduct complex, information-heavy tasks by drastically reducing time spent on research. Effective prompting and providing relevant context are crucial for obtaining tailored, high-quality AI outputs. Choosing the right AI tool depends on your specific needs—ChatGPT excels in depth and rigor, while others like Gemini and Perplexity offer useful alternatives for specialized tasks. This guide offers actionable insights, prompt templates, and GTM-focused use cases to unlock Deep Research’s full potential for startups.

Optimixed’s Overview: Unlocking AI Deep Research to Accelerate GTM Strategies and Decision-Making

Introduction to AI Deep Research

Deep Research is an advanced AI capability that automates end-to-end complex tasks such as data gathering, contextual analysis, and report generation—traditionally requiring hours of manual effort. Unlike common AI use cases, Deep Research demands well-crafted prompts and sufficient input context to deliver personalized, actionable intelligence. This makes it a powerful tool for go-to-market (GTM) teams who regularly tackle diverse strategic projects without subject matter expertise.

Key Challenges and How to Overcome Them

  • Source Quality and Credibility: AI agents may rely on outdated or biased data, mixing social media opinions with facts. Mitigation strategies include specifying preferred primary sources in prompts and using advanced models like GPT-5 to curate high-quality references before research.
  • Context Provision: AI requires detailed background such as company operations, goals, and constraints to create customized outputs rather than generic summaries.
  • Prompt Engineering: Structured prompts with clearly defined goals, context, style, and source instructions significantly improve output relevance and readability.

Best Practices for Crafting Effective Deep Research Prompts

A strong prompt typically includes:

  • Goal: Precise description of the objective and expected AI deliverable.
  • Context: Information about the company, project constraints, and desired outcome.
  • Content Requirements: Specific elements or sections to be included, such as comparisons, code snippets, or frameworks.
  • Style: Formatting preferences like Pyramid Principle, use of bullet points, tables, and summaries.
  • Source Preferences: Prioritize factual, primary, and recent sources with in-text citations.
  • Instructions: Additional methodological guidance or requests for iterative context gathering from the AI.

Comparing AI Tools for Deep Research

  • ChatGPT (especially GPT-5): Best for deep, comprehensive research with contextual understanding and detailed reporting. Its Agent Mode enables interaction with websites for enriched data gathering.
  • Gemini: Strong alternative with generous usage limits and good report quality.
  • Perplexity: Excels in targeted research on specific websites or social forums with granular source control.
  • Claude and Grok: Deliver concise, well-formatted introductory reports ideal for quick topic overviews.

Practical GTM Use Cases Demonstrated

  • Marketing Attribution Model Development: AI can produce end-to-end, tactical guides tailored to your tech stack and market segment.
  • Competitive Ad Library Analysis: Using Agent Mode, AI reviews hundreds of ads and generates detailed positioning and messaging reports.
  • Website and Pricing Page Audits: Automated critiques with actionable improvement suggestions and mockup copy accelerate homepage overhauls.
  • Customer Comparison Content Creation: AI synthesizes feature comparisons from authoritative sources with up-to-date verification to support sales enablement.
  • International Market Expansion Prioritization: AI helps build frameworks, source credible market data, and rank countries based on multiple strategic dimensions.

Additional Insights and Future Potential

Beyond the highlighted examples, Deep Research can support a vast array of GTM activities such as documenting complex workflows, analyzing social media sentiment, and generating growth hack compendiums. Combining reasoning models like GPT-5 or Claude Opus with Deep Research reports can refine strategic decisions and execution plans. The approach encourages iterative context enrichment and transparent research planning, ensuring alignment with business goals and minimizing rework.

Conclusion

For startups aiming to scale rapidly, leveraging AI Deep Research is a game-changing strategy to accelerate learning curves, optimize resource allocation, and produce high-impact deliverables. Mastery of prompt engineering, context-sharing, and tool selection is essential to harness its full value. As AI capabilities evolve, integrating Deep Research into GTM workflows will become increasingly indispensable for competitive advantage.