Equity Research Analyst
Write and publish research reports
What You Do Today
Author initiation reports, quarterly updates, industry notes, and thematic pieces. Develop and defend investment theses with supporting data, competitive analysis, and valuation work. Route through compliance review before publishing.
AI That Applies
Generative AI drafts report sections from model outputs and data, creates charts and visualizations, and checks compliance with regulatory requirements and firm style guides.
Technologies
How It Works
The system ingests model outputs and data as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — charts and visualizations — surfaces in the existing workflow where the practitioner can review and act on it. The value of sell-side research is the analyst's differentiated view.
What Changes
First drafts of data-heavy report sections generate automatically. Chart creation and formatting accelerate significantly.
What Stays
The value of sell-side research is the analyst's differentiated view. Crafting a compelling investment narrative that clients pay attention to requires original thinking and persuasive writing.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for write and publish research reports, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long write and publish research reports takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your data engineering lead
“Which of our current reports are manually assembled, and how much time does that take each cycle?”
They control the data pipelines that feed your analysis
your VP or director of analytics
“What questions do stakeholders actually ask that our current reporting doesn't answer?”
They're deciding the team's AI tool adoption strategy
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.