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series_id
large_stringclasses
4 values
date
date32
value
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39.1
66.7
RAMPAIALL
2025-12-01
44.422288
RAMPAIALL
2026-01-01
45.437184
RAMPAIALL
2026-02-01
47.668199
RAMPAIALL
2026-03-01
50.080775
RAMPAIALL
2026-04-01
51.930727
RAMPAIALL
2026-05-01
53.181785
RAMPAIALL
2026-06-01
53.915459
RAMPAIALL
2026-07-01
54.771521
RAMPAIALL
2026-08-01
55.16722
RAMPAIALL
2026-09-01
55.825228
RAMPAIALL
2026-10-01
56.796274
RAMPAIENTERPRISE
2025-12-01
53.298998
RAMPAIENTERPRISE
2026-01-01
54.440102
RAMPAIENTERPRISE
2026-02-01
56.514316
RAMPAIENTERPRISE
2026-03-01
59.613674
RAMPAIENTERPRISE
2026-04-01
62.201722
RAMPAIENTERPRISE
2026-05-01
62.921348
RAMPAIENTERPRISE
2026-06-01
63.589152
RAMPAIENTERPRISE
2026-07-01
64.88849
RAMPAIENTERPRISE
2026-08-01
65.095569
RAMPAIENTERPRISE
2026-09-01
65.644107
RAMPAIENTERPRISE
2026-10-01
66.707516
RAMPAIMIDMARKET
2025-12-01
48.151314
RAMPAIMIDMARKET
2026-01-01
49.435556
RAMPAIMIDMARKET
2026-02-01
51.866911
RAMPAIMIDMARKET
2026-03-01
55.183727
RAMPAIMIDMARKET
2026-04-01
57.272263
RAMPAIMIDMARKET
2026-05-01
59.095728
RAMPAIMIDMARKET
2026-06-01
60.126969
RAMPAIMIDMARKET
2026-07-01
60.917693
RAMPAIMIDMARKET
2026-08-01
61.34795
RAMPAIMIDMARKET
2026-09-01
62.45755
RAMPAIMIDMARKET
2026-10-01
63.207599
RAMPAISMB
2025-12-01
39.078752
RAMPAISMB
2026-01-01
40.011913
RAMPAISMB
2026-02-01
42.280688
RAMPAISMB
2026-03-01
44.270523
RAMPAISMB
2026-04-01
46.017422
RAMPAISMB
2026-05-01
47.283849
RAMPAISMB
2026-06-01
47.975894
RAMPAISMB
2026-07-01
48.90171
RAMPAISMB
2026-08-01
49.439872
RAMPAISMB
2026-09-01
50.021731
RAMPAISMB
2026-10-01
51.228968

US macro data: Ai Adoption (4 series)

4 ai adoption series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-11-12 on) of each series' latest values. It is refreshed weekly.

import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-ai-adoption/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-ai-adoption/data/wide_monthly.parquet")  # one column per series

or datasets.load_dataset("FinzData/macro-ai-adoption"). Wide tables by frequency: wide_monthly (4 series). series.csv lists every series with its title, units, frequency and how far back the full history goes.

The full history, point in time, through one API

The FinzData API serves every series back to its first observation, every revision (what was known on any past date, so backtests do not peek at later revisions), and daily updates, in the same format:

import finzdata as fz                     # pip install finzdata
c = fz.Client()                           # free API key: https://api.finzdata.com/
c.macro("RAMPAIALL")                        # full history, latest values
c.macro("RAMPAIALL", vintage="first")       # as first released
c.macro("RAMPAIALL", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="ai_adoption")          # the catalogue for this category

Most-watched series in this package

ID Title Units Frequency Full history from
RAMPAIALL Artificial Intelligence Adoption by Ramp Businesses: Overall Percent Monthly 2023
RAMPAIENTERPRISE Artificial Intelligence Adoption by Ramp Businesses: Company Size: Large Businesses Percent Monthly 2023
RAMPAIMIDMARKET Artificial Intelligence Adoption by Ramp Businesses: Company Size: Medium-Sized Businesses Percent Monthly 2023
RAMPAISMB Artificial Intelligence Adoption by Ramp Businesses: Company Size: Small Businesses Percent Monthly 2023

All 4 series: series.csv. Browse them at https://finzdata.com/macro/category/ai-adoption.

More from FinzData

Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData. Data for research, not investment advice.

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