In a prior post, we covered 15 real-world examples of how investors are using AI to sharpen their research process. Here are 15 more.
These come directly from how Hudson Labs users are working today: not hypothetical applications, but workflows that are already running.
16. Find companies with exposure to major themes
Traditional screening tools force a choice between structured filters and keyword search. Neither works well for complex thematic questions like:
"Which power companies are most exposed to data center build-outs?"
Hudson Labs Market Intelligence surfaces relevant companies using AI-based retrieval and reasoning — a capability that is far more powerful than a keyword-based text search. Market Intelligence lets you pair thematic questions with precise sourcing and traditional market metric-based screening.
Example: Power companies with significant exposure to data center build-outs
View and re-run the full result here.
17. Thematic earnings recaps for multiple companies
When a macro narrative is driving sector sentiment — like the current debate around AI coding tools and software valuations — the most useful check is often to audit what the management teams closest to the issue are actually saying.
Hudson Labs’ new Market Intelligence AI tool structures relevant commentary across multiple companies, making it easy to cut through the noise. In the example below, we focus on AI impact and outlook statements across software earnings calls.
Example: AI impact and outlook across software earnings calls
View the full result here.
See more examples here.
18. Monitor Price Changes Across Companies
Pricing is a real-time signal reflecting demand strength, inflation pressure, or competitive dynamics — but pricing changes are rarely disclosed in a structured format.
AI can monitor pricing language across companies and surface where management teams are raising prices, holding the line, or encountering resistance, making it easier for you to track inflation and margin trends as they emerge.
Example 1: Industrial companies increasing prices
View the full result here.
Example 2: Consumer companies increasing prices
View the full result here.
19. Compare Management Guidance to Consensus Estimates
The real signal often lies in the gap between what management is guiding to and what the Street is modeling. That divergence can reveal mispricing, shifting expectations, or upcoming revisions. AI can quickly surface that comparison and flag where the gaps are largest.
Example: Micron Technology – Management guidance vs. consensus estimates
View and re-run consensus queries here.
20. Review All Guidance-Related Statements — Not Just the Headline Numbers
Most AI tools still struggle with forward-looking language. Accurately interpreting tense and identifying implicit guidance requires purpose-built models — and missing a subtly phrased forward statement can mean missing a major signal.
Hudson Labs addresses this with proprietary guidance-specific models that activate when queries use terms like "guidance" or "expectations." The Sources audit feature lets you confirm when the guidance model was applied.
Example: Visa – Guidance and related assumptions (January 2026)
View and re-run the full result here.
21. Understand the Drivers Behind Street Estimates
Consensus estimates often look precise, but the assumptions behind them are rarely explicit. Changes in estimates are driven by management commentary, guidance nuances, and evolving narratives that aren't always captured in the numbers alone.
AI can help connect the dots by linking forward consensus estimates back to earnings calls and reporting, making it easier to understand what’s actually driving street expectations — and what could cause them to change.
Example: Micron Technology – Consensus estimate drivers
View the full result and re-run the query here.
22. Identify Upcoming Catalysts That Could Move the Stock
You can have the right thesis, but without a catalyst, being right may not matter.
Catalysts don't always arrive neatly labeled. AI can surface these signals early by scanning disclosures and highlighting the developments – upcoming events, transactions, or strategic pivots – most likely to move a stock.
Example 1: Robinhood – Potential catalysts
View the full result and re-run the query here.
Example 2: CoreWeave – Upcoming catalysts
View the full result and re-run the query here.
23. Read-Throughs: Understand the Ripple Effects Across Industries
Earnings calls don't just matter for the company reporting them. Management commentary frequently contains signals about demand, pricing, supply chains, and capital allocation that have implications well beyond a single stock. AI can help surface and organize what matters for the broader market.
Example 1: Salesforce – Implications for other companies and industries (last 4 earnings calls)
View the full result and re-run the query here.
Example 2: PACCAR – Tariff and supply chain implications from the last earnings call
View the full result and re-run the query here.
24. Track Product Pipelines
For biotech, pharma, and software, the product pipeline is a core driver of growth. Tracking updates across calls, filings, and presentations is as time-consuming as it is important.
Hudson Labs AI structures product-level guidance, launch timelines, and forward-looking commentary so you never miss an update.
Example 1: Eli Lilly – Product launch guidance for obesity and diabetes
View the full result and re-run the query here.
Example 2: Nvidia – Data center product launches
View the full result and re-run the query here.
25. Understand How a Company's Competitive Environment Is Evolving
Competitive advantage is rarely static. Shifts in product mix, pricing, customer demand, and strategy often show up gradually across disclosures and earnings commentary — easy to miss when reading each document in isolation, easier to spot when AI can synthesize across them.
Example: Cisco competitive differentiation
View the full result and rerun here.
26. Detect Changes in Executive Incentives
Proxy redlines can be as revealing as 10-K redlines — and are far less commonly reviewed. Adjustments to compensation structures and incentive metrics highlight what management is actually optimizing for in the years ahead.
As governance expert Mike @NonGAAP has noted, these changes can serve as early "tells," hinting at upcoming transactions or signaling expected performance trends.
Example 1: The RealReal – Updated bonus thresholds, vesting schedules, and hurdle metrics in April 2025
View full results and try it yourself here.
Example 2: Oracle – No more bonuses
View the full result and re-run the query here.
27. Analyze Governance Updates at Scale
Proxy season is hard to keep up with. AI agents can help.
AI agents can automatically compare proxy statements, track changes in governance structures, and surface what's new as soon as filings hit EDGAR — without requiring you to manually review each proxy.
Example 1: Principal Financial Group – Proxy update
View the full result and re-run the query here.
Example 2: Principal Financial Group – Governance email alert
28. Monitor what's driving large movements in stock price using email alerts
One popular Hudson Labs automation triggers an alert whenever a watchlist company's stock moves beyond a defined threshold, and goes beyond just flagging the price change. The automations handle the initial investigative work, so by the time the alert hits your inbox, you already have context on what's driving the move.
Example: Email alert explaining a price increase in Micron Technology
29. Automatically Convert Tables to Charts
AI can analyze tabular data and generate charts that make trends easier to see and interpret.
On the Hudson Labs platform, any table in your results can be converted to a chart by clicking the Chart icon.
Example 1: UnitedHealth vs. Optum revenue growth
View the full result and recreate the chart here.
Example 2: Oracle RPO revenue recognition timeline
The share of Oracle's remaining performance obligations expected to be recognized more than three years out has increased dramatically — a trend that's much easier to spot in chart form.
View the full result and recreate the chart here.
30. Automate Meeting Scheduling
A productivity note worth including: Superhuman, the email app used by our sales and operations teams, can generate a complete calendar invite from an email thread with a single keystroke.
It handles time zone conversion, includes all relevant attendees, sets an appropriate duration, and drafts follow-up emails when needed. It costs around $40/month and works with Gmail and Outlook. We have no relationship with the company — just genuine appreciation for small, high-impact workflow improvements.
AI is most powerful when it's replacing hours of manual effort, and when the infrastructure behind it ensures the output is actually accurate. Redline comparisons, cross-document synthesis, real-time monitoring, and precise guidance interpretation – all of these examples above are live in Hudson Labs today. The links will take you directly to a re-runnable query.
