BI-301b · Module 3

Automated Dark Asset Screening

3 min read

The dark asset discovery sprint can be partially automated. The data collection phase (website analysis, financial data, job postings, review analysis) is 80% automatable through AI-assisted web research and structured extraction. The benchmarking phase is 90% automatable through CIPHER's statistical models. The inventory phase — identifying which metrics exceed peer benchmarks — is fully automatable. The only step that requires human judgment is the interpretation: which of these candidate dark assets are actually meaningful to this customer's market position? That judgment is where BEACON adds value that the automation cannot.

Do This

  • Automate data collection and benchmarking — these are mechanical steps that AI performs reliably
  • Use automation to produce candidate lists, not final inventories — human judgment validates the candidates
  • Run automated screening across the entire customer portfolio quarterly — surface new dark assets as companies evolve
  • Feed automated screening results to ANCHOR for customer health context — a customer whose dark asset portfolio is growing is getting stronger

Avoid This

  • Fully automate the discovery process including interpretation — the automation misses context that determines whether a metric is a meaningful dark asset or a statistical artifact
  • Replace the human discovery sprint with automation for Tier 1 accounts — the highest-value relationships deserve the deepest human analysis
  • Skip automation for Tier 3 accounts — automation is precisely what makes portfolio-scale dark asset discovery possible