Technology Landscape · Federal AI Intelligence

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.

Lifecycle by technology

Share of each technology's records

DeployedPilotPre-deploymentRetiredNot reported

Generative AI

Generative AI
Deployed: 1 (25.0%)Generative AI
Pre-deployment: 3 (75.0%)

Classical / predictive ML

Classical / predictive ML
Deployed: 12 (92.3%)Classical / predictive ML
Pre-deployment: 1 (7.7%)

Natural language processing

Natural language processing
Deployed: 4 (50.0%)Natural language processing
Pre-deployment: 4 (50.0%)

Computer vision

Computer vision
Deployed: 21 (80.8%)Computer vision
Pilot: 1 (3.8%)Computer vision
Pre-deployment: 4 (15.4%)

Not reported

Not reported
Retired: 4 (100.0%)

Sourcing by technology

Where reported

Developed in-houseContract and in-housePurchased from vendorNot reported

Generative AI

Generative AI
Contract and in-house: 1 (25.0%)Generative AI
Not reported: 3 (75.0%)

Classical / predictive ML

Classical / predictive ML
Developed in-house: 5 (38.5%)Classical / predictive ML
Contract and in-house: 5 (38.5%)Classical / predictive ML
Purchased from vendor: 2 (15.4%)Classical / predictive ML
Not reported: 1 (7.7%)

Natural language processing

Natural language processing
Contract and in-house: 3 (37.5%)Natural language processing
Purchased from vendor: 1 (12.5%)Natural language processing
Not reported: 4 (50.0%)

Computer vision

Computer vision
Contract and in-house: 11 (42.3%)Computer vision
Purchased from vendor: 11 (42.3%)Computer vision
Not reported: 4 (15.4%)

Not reported

Not reported
Not reported: 4 (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	Not reported	n
DHS	7%	24%	15%	47%	7%	55

Technology by topic area

Row share within each topic

Topic by AI classification
	Generative AI	Classical / predictive ML	Natural language processing	Computer vision	n
Law Enforcement	3%	26%	14%	57%	35
Transportation	14%	29%		57%	7
Administrative Functions		50%	25%	25%	4
Human Resources	50%		50%		2
Service Delivery	50%		50%		2
Other				100%	1

High-impact designation by technology

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

Generative AI

Generative AI
High-impact: 4 (100.0%)

Classical / predictive ML

Classical / predictive ML
High-impact: 13 (100.0%)

Natural language processing

Natural language processing
High-impact: 8 (100.0%)

Computer vision

Computer vision
High-impact: 26 (100.0%)

Not reported

Not reported
High-impact: 4 (100.0%)

Governance disclosure by technology

High-impact records: reported in place / scope

Technology	High-impact	Testing	Impact assess.	Indep. review	Monitoring	Training
Generative AI	4	0 / 4	0 / 4	0 / 4	0 / 4	0 / 4
Classical / predictive ML	13	11 / 13	10 / 13	10 / 13	11 / 13	10 / 13
Natural language processing	8	3 / 8	1 / 8	1 / 8	1 / 8	2 / 8
Computer vision	26	18 / 26	15 / 26	15 / 26	16 / 26	16 / 26
Not reported	4	0 / 4	0 / 4	0 / 4	0 / 4	0 / 4

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

34 of 55 records name a vendor (62%)

NEC99 records IBM22 records Deloitte11 records Leidos11 records Motorola Solutions11 records TRM Labs11 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 56% 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.