Goldfib Key takeaways
- Roughly half of a junior analyst's week is collection and formatting. That is the part to automate first.
- AI is strongest at reading, extracting and comparing text at scale. It is weakest at knowing when it's wrong.
- Design for verification: every automated number should carry its source location so a human can check it in seconds.
- Automation doesn't replace analysts. It changes the ratio of analysts to companies covered.
There is a lot of noise about AI replacing analysts. The more useful question for anyone running a research process is narrower: which tasks in my workflow can a machine do reliably today, and how do I check its work?
This guide breaks the equity research workflow into its component tasks and scores each one for automation potential. That gives you a practical map of where to start.
Where an analyst’s week actually goes
Before automating anything, measure. The breakdown below is a typical pattern for a junior analyst covering Indian companies, assembled from common desk workflows. Your own split will differ, so track a week and see.
Task-by-task automation map
Each task is scored on two things: how much of it current tools can automate, and how risky an undetected error would be.
The pattern is clear. Getting data in and organised is highly automatable. Interpreting what it means is not. Guidance extraction and table extraction sit in between: high potential, but errors there flow directly into models.
The automated research pipeline
For the data layer, see building a research data pipeline from NSE and BSE filings. For the transcript layer, see analysing earnings call transcripts at scale.
What changes when you automate
The gain is not “fewer analysts”. It is more coverage per analyst, or more depth per company. For a family office, that means a small desk can monitor a portfolio that once needed a much bigger team.
Rules for using AI safely in research
- Every number has a source. Extracted figures carry a document, page and quote. No source, no entry.
- Targets are not actuals. Extraction prompts and checks explicitly separate guidance from reported results.
- Standalone vs consolidated is a required field. This is the most common extraction error with Indian filings.
- Sample even what you trust. Spot-check a fixed share of automated extractions every cycle, even when error rates look low.
- Keep client data private. Use tools and settings that don’t train on your inputs. Never paste confidential mandates into consumer tools.
- A human signs off. Automation can draft, flag and compare. A named person is accountable for what goes out.
Where to start
If you are starting from zero, automate in this order. Each step pays for itself before you start the next one.
- Filings and announcements alerts for your holdings, which is cheap and low-risk.
- Shareholding and pledge tracking. See promoter pledging signals.
- Financial table extraction into model history, with verification.
- Transcript guidance tracking.
- Screens and peer tables. See stock screening for Indian markets.
Automation is half of how a lean team can produce institutional-grade research at a fraction of the usual cost. The other half is a disciplined analyst bench, which is the premise of the student-led research model.
Frequently asked questions
Can AI do equity research?
AI can do much of the mechanical work in equity research, such as gathering filings, extracting financial data, summarising earnings calls and drafting sections of reports. It cannot reliably form an investment thesis, judge management credibility or take responsibility for accuracy. In practice, AI works best as an extremely fast junior assistant whose work is always reviewed.
Which research tasks should be automated first?
Start with high-volume, rule-based tasks: downloading results and filings, extracting financial tables, updating historical model data, tracking shareholding changes and flagging new corporate announcements. These save the most time and are the easiest to verify.
What are the risks of using AI in investment research?
The main risks are confident errors such as misread numbers, wrong periods or confused segments; outdated information; and analysts trusting summaries instead of reading sources. Data confidentiality also matters when using third-party AI tools with client information. A verification step and source citations for every extracted figure address most of these.
Will AI replace equity research analysts?
It is more likely to change what analysts do than to remove them. Automation reduces time spent on collection and formatting, which lets one analyst cover more companies or go deeper on fewer. Judgement, accountability and the variant view remain human work.