★ Research Analyst · agent or hire?
AI Agent vs Hiring a Research Analyst: Cost Calculator
Analysts spend much of their time gathering and summarising before they get to thinking. Agents can read a lot, fast, and produce a sourced first pass. The value of an analyst is the judgement on top, which is what you keep.
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Presets are starting assumptions for a small team, not industry statistics. Change every number to match your reality.
The agent saves
$2,404 / month
Pays back the build cost in 6.2 months.
People only
$7.5K/mo
With agent
$5.1K/mo
Hours freed
70 h/mo
12 months, side by side
Cumulative cost. The agent line starts at the build cost.
| Month | People only | Agent |
|---|---|---|
| 0 | $0 | $15K |
| 1 | $7.5K | $20.1K |
| 2 | $15K | $25.2K |
| 3 | $22.5K | $30.3K |
| 4 | $30K | $35.4K |
| 5 | $37.5K | $40.5K |
| 6 | $45K | $45.6K |
| 7 | $52.5K | $50.7K |
| 8 | $60K | $55.8K |
| 9 | $67.5K | $60.9K |
| 10 | $75K | $66K |
| 11 | $82.5K | $71.1K |
| 12 | $90K | $76.2K |
Where the agent cost goes
- Work people still do$3,750
- Reviewing the agent$833
- Model usage$13
- Maintenance$500
Model cost per task: $0.2100 on Claude Opus 5.5, for 60 agent tasks a month.
An agent that pays back in 6.2 months.
I build production AI agents with human review, guardrails and logging.
What the agent can and can't take
Good jobs for the agent
- Collect and summarise sources on a question
- Pull figures into a consistent table with citations
- Compare competitors on stated criteria
- Draft a brief for an analyst to sharpen
Keep with a person
- Know which sources are trustworthy without guidance
- Form a contrarian view the data supports
- Take accountability for a recommendation
What review looks like: An analyst checks every citation the brief relies on and rewrites the conclusion. Review is 100% by default.
Good fit, bad fit
| Good fit if… | Not a good fit if… |
|---|---|
| Recurring briefs with the same structure | One-off deep research on a niche topic |
| Sources the agent can access legally | Paywalled or private sources the agent can’t reach |
| An analyst who wants more time for thinking | Decisions where being wrong is very expensive |
Risks to plan for
- Citations that don’t say what the summary claims
- Out-of-date information presented as current
- Over-confident conclusions
How the comparison works
The calculator compares two ways of getting the same tasks done each month: people doing all of them, or an agent doing a share while people do the rest and review the agent's work.
people only = tasks × minutes ÷ 60 × hourly cost
with agent = remaining human work + review time + model cost + maintenance
model cost = agent tasks × (input tokens × input price + output tokens × output price)
savings = people only − with agent
payback = build cost ÷ savings (never, if savings ≤ 0)
It only counts the share of someone's time spent on these tasks, so it answers “what does this work cost”, not “can I remove a job”. Model prices come from the LLM API Cost Calculator.
Worked example
The research analyst preset with the example salary of $60,000 plus 25% overhead, 120 research briefs a month at 90 minutes each, the agent handling 50% and a person reviewing 100% of those:
| Line | Per month |
|---|---|
| People only | $7,500 |
| Remaining human work | $3,750 |
| Reviewing the agent | $833 |
| Model usage (Claude Opus 5.5) | $13 |
| Maintenance | $500 |
| With the agent | $5,096 |
| Saving | $2,404 |
With a $15,000 example build cost, payback is 6.2 months. Notice how small the model cost is next to the human time that remains: review time and the share the agent can't finish usually decide the answer, not token prices.
Questions about this role
Can AI replace a research analyst?
How do I stop fake citations?
Why does this preset use a larger model?
Is an AI agent cheaper than hiring someone?
Where do the model prices come from?
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Fine print
Tools: on the house.
Engineer: by the hour.
The math was free. A reliable agent takes engineering, which is what I do.
I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.