PAIR Assessment ToolPhysical AI Readiness
Methodology · Beta 1.0

A transparent framework for place-based readiness.

PAIR is an exploratory research and decision-support framework for asking whether the environments, systems, institutions, and public-value conditions around Physical AI are ready for deployment.

What is Physical AI?

Intelligence that perceives, coordinates, or acts in the physical world.

Physical AI includes autonomous vehicles and robotaxis, robotics, intelligent curb systems, AI-enabled logistics, connected infrastructure, automated depots, digital twins, smart charging systems, and other technologies that interact with real places and people.

Technical performance matters, but deployment also depends on streets and facilities, interoperable systems, capable institutions, skilled workforces, public legitimacy, safety practices, and a viable case for long-term value.

PAIR’s central question

Are the place, systems, institutions, and public-value conditions ready for Physical AI deployment?

Framework structure

Domains are the structure. Dimensions are the measures.

The four PAIR domains and ten readiness dimensions are one nested framework—not separate models.

Site

Place

Is the physical environment ready?

System

Architecture

Can Physical AI connect to the wider system?

Govern

Institutions

Can we govern, operate, and respond?

Value

Returns

Does deployment create value?

PPlace

1Physical Infrastructure

2Curb, Access & Public Realm

3Energy, Charging & Depot Capacity

AArchitecture

4Digital & Data Infrastructure

5Mobility & System Integration

IInstitutions

6Governance & Institutional Capacity

7Workforce & Operations

8Safety, Emergency Response & Resilience

RReturns

9Public Trust, Equity & Community Acceptance

10Economic Development & Deployment Viability

From assessment to learning

Readiness is a cycle, not a finish line.

PAIR makes the reasoning path visible—from the domain being examined to the evidence informing action and the evaluation that follows deployment.

01PAIR Domain
02Readiness Dimension
03Evidence
04Maturity Assessment
05Gap
06Action
07Deployment
08Evaluation
Maturity assessment

A shared five-point language

Ratings describe current conditions and support structured discussion. They are not grades or certifications.

1Not Ready

Foundational conditions are absent or undocumented.

2Emerging

Early capabilities exist, with important gaps remaining.

3Pilot-Ready

Conditions can support a bounded, monitored pilot.

4Deployment-Ready

Capabilities support managed operational deployment.

5Adaptive & Scalable

Systems can learn, improve, and scale responsibly.

Research foundations

Conceptually informed—not yet empirically validated.

PAIR draws conceptually from existing research and policy frameworks addressing AI readiness, risk governance, mobility systems, urban design, workforce transition, and autonomous-vehicle deployment. These sources inform PAIR; they do not directly validate its scoring structure.

The beta does not use a validated composite index because weights, evidence standards, and comparisons have not yet been tested across multiple places and use cases. The overall mean is contextual information only.

Selected references

  1. Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
  2. Oxford Insights. (2024). Government AI Readiness Index 2024.
  3. National Association of City Transportation Officials. (2019). Blueprint for Autonomous Urbanism: Second Edition.
  4. Open Mobility Foundation. (n.d.). Mobility Data Specification and Curb Data Specification.
  5. U.S. Department of Transportation. (2021). Automated Vehicles Comprehensive Plan.
  6. World Economic Forum. (2025). Future of Jobs Report 2025.
  7. World Economic Forum. (2025). Physical AI: Powering the New Age of Industrial Operations.
  8. Riggs, W., Appleyard, B., & Johnson, M. (2020). A design framework for livable streets in the era of autonomous vehicles. Urban, Planning and Transport Research, 8(1), 125–137.
  9. Jiang, L., Chen, H., & Chen, Z. (2022). City readiness for connected and autonomous vehicles: A multi-stakeholder and multi-criteria analysis through analytic hierarchy process. Transport Policy, 128, 13–24.
From beta to validated benchmark

What could come next

Future versions may add validated weighting, expert review, evidence scoring, peer-city benchmarking, use-case-specific profiles, and longitudinal tracking.

Future validated product

PAIR Index

A future PAIR Index may be developed only after repeated application, expert review, methodological refinement, and comparative evidence.

Use the framework. Test the assumptions.

The beta is designed for client meetings, workshops, research pilots, and baseline assessments.

Start assessment →