Technology Landscape

Technology landscape

How reported AI classifications spread across agencies, missions, lifecycle stages and sourcing. Each record carries one reported classification, so categories do not overlap. A classification describes the technique, not a specific commercial model.

Sourcing by technology

Where reported

Developed in-houseContract and in-housePurchased from vendorNot reported

Generative AI

Generative AI
Developed in-house: 2 (2.2%)Generative AI
Contract and in-house: 6 (6.7%)Generative AI
Purchased from vendor: 37 (41.6%)Generative AI
Not reported: 44 (49.4%)

Classical / predictive ML

Classical / predictive ML
Developed in-house: 5 (4.6%)Classical / predictive ML
Contract and in-house: 8 (7.4%)Classical / predictive ML
Purchased from vendor: 64 (59.3%)Classical / predictive ML
Not reported: 31 (28.7%)

108

Natural language processing

Natural language processing
Developed in-house: 5 (7.9%)Natural language processing
Contract and in-house: 5 (7.9%)Natural language processing
Purchased from vendor: 28 (44.4%)Natural language processing
Not reported: 25 (39.7%)

Computer vision

Computer vision
Developed in-house: 1 (3.2%)Computer vision
Contract and in-house: 2 (6.5%)Computer vision
Purchased from vendor: 23 (74.2%)Computer vision
Not reported: 5 (16.1%)

Agentic AI

Agentic AI
Developed in-house: 1 (25.0%)Agentic AI
Purchased from vendor: 1 (25.0%)Agentic AI
Not reported: 2 (50.0%)

Not reported

Not reported
Not reported: 19 (100.0%)

Technology adoption by agency

Row share: each agency's records by classification · 16 largest portfolios in the filter

Agency by AI classification
	Generative AI	Classical / predictive ML	Natural language processing	Computer vision	Agentic AI	Not reported	n
DOJ	28%	34%	20%	10%	1%	6%	314

Technology by topic area

Row share within each topic

Topic by AI classification
	Generative AI	Classical / predictive ML	Natural language processing	Computer vision	Agentic AI	n
Law Enforcement	22%	36%	25%	15%	2%	170
Administrative Functions	45%	27%	21%	6%		62
Information Technology	44%	41%	12%		3%	34
Health and Medical		86%	14%			7
Procurement and Financial Management	33%	67%				6
Service Delivery	40%	40%	20%			5
Human Resources	25%	50%	25%			4
Cybersecurity	67%	33%				3
Energy and the Environment			100%			1
Government Benefits Processing	100%					1
Science				100%		1
Transportation				100%		1

High-impact designation by technology

High-impactPresumed high-impact, determined notNot high-impactNot reported

Generative AI

Generative AI
High-impact: 21 (23.6%)Generative AI
Presumed high-impact, determined not: 11 (12.4%)Generative AI
Not high-impact: 57 (64.0%)

Classical / predictive ML

Classical / predictive ML
High-impact: 41 (38.0%)Classical / predictive ML
Presumed high-impact, determined not: 16 (14.8%)Classical / predictive ML
Not high-impact: 51 (47.2%)

108

Natural language processing

Natural language processing
High-impact: 22 (34.9%)Natural language processing
Presumed high-impact, determined not: 4 (6.3%)Natural language processing
Not high-impact: 37 (58.7%)

Computer vision

Computer vision
High-impact: 20 (64.5%)Computer vision
Presumed high-impact, determined not: 1 (3.2%)Computer vision
Not high-impact: 10 (32.3%)

Agentic AI

Agentic AI
High-impact: 1 (25.0%)Agentic AI
Not high-impact: 3 (75.0%)

Not reported

Not reported
High-impact: 9 (47.4%)Not reported
Not high-impact: 10 (52.6%)

Governance disclosure by technology

High-impact records: reported in place / scope

Technology	High-impact	Testing	Impact assess.	Indep. review	Monitoring	Training
Generative AI	21	0 / 21	0 / 21	0 / 21	0 / 21	0 / 21
Classical / predictive ML	41	0 / 41	0 / 41	0 / 41	0 / 41	0 / 41
Natural language processing	22	0 / 22	0 / 22	0 / 22	0 / 22	0 / 22
Computer vision	20	0 / 20	0 / 20	0 / 20	0 / 20	0 / 20
Agentic AI	1	0 / 1	0 / 1	0 / 1	0 / 1	0 / 1
Not reported	9	0 / 9	0 / 9	0 / 9	0 / 9	0 / 9

Shows counts reported in place against all high-impact records of that technology. The remainder includes in-progress, not applicable and unanswered. Full governance view

Systems, vendors and technology are different things

133 of 314 records name a vendor (42%)

Microsoft1313 records Thomson Reuters88 records Axon66 records LexisNexis55 records Adobe44 records Amazon Web Services44 records OpenAI44 records SAS44 records Veritone44 records Exiger22 records Google22 records Palantir22 records Verisk22 records Cellebrite11 records Cisco11 records

Vendor: the supplier the agency names in a free-text field. Matched to a standard list of companies; one record may name several.

System name: the agency's name for the system (reported for 60% of records).

AI classification: the technique category the agency selected. A vendor name does not show which model or technique is used, and this platform does not infer it.