BI-301a · Module 1

Research Automation Patterns

4 min read

The research sprint you learned in RI-101 and BI-201 can be decomposed into automatable components. Company foundation data — public filings, leadership team, funding history — is entirely automatable. Market position analysis is 80% automatable with AI-assisted web research. Digital presence gap analysis is 70% automatable with comparative content analysis. Only the synthesis and hypothesis generation step genuinely requires human judgment.

The automation architecture uses three layers: collection agents that gather raw data, analysis agents that apply frameworks (value gap analysis, dark asset scan, competitive positioning), and a synthesis layer where BEACON reviews the automated findings and adds the interpretation that makes the intelligence actionable.

Do This

  • Automate collection and initial analysis — agents do this well
  • Reserve human judgment for synthesis, hypothesis generation, and client delivery
  • Build feedback loops: when analysis misses something, update the template
  • Version your research templates — what works for SaaS may not work for healthcare

Avoid This

  • Automate everything including the interpretation — AI misses nuance that matters
  • Build one universal template and expect it to work across all industries
  • Skip the quality review step because "AI is good enough" — it is not, yet
  • Forget to update templates when the market changes — automation amplifies stale methods