Methodology · Framework v0.2

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

Evidence demonstrates that foundational conditions are absent or materially inadequate.

2Emerging

Partial or ad hoc capability exists, with important coverage and implementation gaps.

3Pilot-Ready

Documented conditions support a bounded, monitored pilot with accountable owners.

4Deployment-Ready

Capabilities are resourced, governed, and operating across the intended deployment scope.

5Adaptive & Scalable

Repeated evidence shows measurement, learning, adaptation, resilience, and scalable capacity.

Missing evidence is not a low score

Score 1 requires evidence that foundational conditions are absent or inadequate. When evidence cannot support a judgment, PAIR records “insufficient evidence,” excludes that dimension from the composite, and surfaces the validation need.

Repeatable judgment

Evidence remains attached to every rating.

Each rated dimension records evidence quality, type, source, date, geography, evidence confidence, assessor rationale, and assessor confidence. Verified evidence, stakeholder reports, inference, and missing evidence remain visibly distinct.

Calibration Mode compares two or more assessments dimension by dimension. A two-point score range, a rated-versus-insufficient conflict, a safety disagreement, or materially different evidence bases creates a reconciliation flag. PAIR never converts a disagreement into automatic consensus.

Open Calibration Mode →

Research foundations

Conceptually Informed

PAIR is grounded in established research and policy frameworks addressing AI readiness, risk governance, mobility systems, urban design, workforce transition, and autonomous-vehicle deployment. This literature provides a rigorous foundation for the framework’s dimensions and interpretation.

The current beta uses a transparent, unweighted summary rather than presenting a validated composite index. Structured field application will inform future refinement of weighting, evidence standards, and comparisons across places and use cases.

Selected references

Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
Oxford Insights. (2024). Government AI Readiness Index 2024.
National Association of City Transportation Officials. (2019). Blueprint for Autonomous Urbanism: Second Edition.
Open Mobility Foundation. (n.d.). Mobility Data Specification and Curb Data Specification.
U.S. Department of Transportation. (2021). Automated Vehicles Comprehensive Plan.
World Economic Forum. (2025). Future of Jobs Report 2025.
World Economic Forum. (2025). Physical AI: Powering the New Age of Industrial Operations.
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.
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.

Methodological development roadmap

Advancing the PAIR methodology

The next phase will strengthen the framework through structured field application, expert review, refined evidence standards, use-case-specific profiles, longitudinal analysis, and responsibly governed peer benchmarking.

Long-term validated benchmark

Future PAIR Index

Following sufficient field testing and validation, the PAIR methodology may support a validated index for carefully governed comparison and longitudinal learning.

Use the framework. Test the assumptions.

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

Start assessment →