35% Concept Research Assessment Report

Executive Summary

This report documents the 35% Concept Research Assessment for Task #5, focused on establishing a structured, defensible framework for evaluating Artificial Intelligence (AI) tools applicable to the planning, design, construction, and lifecycle management of Department of Defense (DoD) medical facilities. The intent of Task #5 is not to develop new AI solutions, but to systematically assess existing, market-available tools to determine their feasibility within DoD constraints and their potential impact on UFC 4-510-01 criteria and associated workflows.

At the 35% milestone, the primary objective is to demonstrate methodology rather than reach conclusions. This phase establishes the evaluation boundaries, a lifecycle-based AI classification taxonomy, a tiered feasibility model, and a weighted scoring governance structure. Together, these components create a repeatable, auditable approach that aligns with USACE and DHA expectations, prioritizes cybersecurity and standards compliance, and avoids premature endorsement of immature technologies.

Key outcomes of the 35% effort include:

A clear, defensible definition of “AI” as it applies to Task #5, distinguishing operationally relevant tools from marketing-driven claims.

A three-tier feasibility classification system (DoD-Ready, DoD-Possible, Not Currently Feasible) that reflects real-world deployment constraints rather than binary approval decisions.

A lifecycle-first AI taxonomy aligned with medical facility delivery phases, with built-in flexibility to accommodate cross-cutting capabilities.

A weighted scoring rubric that prioritizes DoD feasibility and UFC relevance over novelty, ensuring alignment with the project’s criteria-driven mission.

Defined evaluation outputs, soft thresholds, and governance logic to support transparency, consistency, and SME-informed judgment.

This framework positions the project for the 65% phase, where the evaluation matrix will be populated with a broader set of tools and preliminary shortlisting logic will be applied. The 35% framework ensures continuity, future-proofing, and credibility as findings mature and inform potential updates to UFC 4-510-01.

1. Introduction and Purpose

Task #5 provides MCX with a structured methodology to evaluate AI tools as standards-ready capabilities rather than experimental technologies. The focus is on identifying tools that can measurably improve outcomes in DoD medical facility delivery while remaining compatible with USACE workflows, cybersecurity requirements, and UFC governance.

The 35% submittal is intended to:

Demonstrate the proposed evaluation framework and documentation format.

Confirm the completeness and appropriateness of AI categories.

Establish a clear path forward for comparative tool evaluation at later milestones.

This phase does not produce recommendations for adoption. Instead, it lays the analytical foundation required for defensible decision-making later in the project.

2. Evaluation Boundaries and Guiding Principles

2.1 Mission Statement

Task #5 evaluates AI tools to determine what exists today, what is usable within DoD constraints, and how those capabilities could inform future UFC criteria and workflows. The task explicitly avoids AI development or pilot implementation.

2.2 Guiding Principles

Five principles guide all evaluation decisions:

• Standards First — AI must support and clarify UFC criteria, not replace them.
• Evidence Over Hype — Preference is given to tools with demonstrated value and documented use.
• DoD Feasibility — Deployment model, cybersecurity posture, and data handling are decisive factors.
• Criteria Impact — Evaluation consistently asks how a tool could influence UFC 4-510-01.
• Future-Proofing — The framework supports structured, phased adoption rather than one-off pilots.

2.3 Definition of AI for Task #5

For this research, AI tools are defined as software platforms or systems that use machine learning, computer vision, natural language processing, generative algorithms, or predictive analytics to automate, augment, or materially improve decision-making across the medical facility lifecycle. This definition is intentionally broad yet excludes tools that rely solely on marketing claims without functional AI capability.

3. Tiered Feasibility Classification

Rather than a binary approved/not-approved approach, tools are classified into one of three feasibility tiers:

• Tier 1 — DoD-Ready (Near-Term Feasible): Enterprise-grade tools with documented use in regulated environments and plausible operation within DoD cybersecurity and data constraints.
• Tier 2 — DoD-Possible (Mid-Term Feasible): Mature commercial tools that provide value but require policy, IT, or contractual changes for DoD deployment.
• Tier 3 — Not Currently Feasible (Long-Term / Informational): Early-stage or research-driven tools lacking enterprise controls or requiring unrestricted cloud access.

Tiering functions as a feasibility gate and is applied before any comparative scoring. This prevents infeasible tools from being artificially elevated due to feature richness alone.

4. AI Classification Strategy

4.1 Lifecycle-Based Taxonomy

AI tools are initially organized by primary medical facility lifecycle phase:

Planning & Programming

Design (BIM / VDC / Optimization)

Construction

Operations & Lifecycle Management

This structure mirrors UFC organization and USACE workflows, improving interpretability for reviewers and working groups.

4.2 Built-In Flexibility

Each tool may also be tagged with secondary lifecycle applicability and meaningfully cross-cutting capabilities. This allows the framework to evolve at later phases without invalidating earlier work or restructuring prior deliverables.

5. Weighted Scoring Governance

5.1 Scoring Philosophy

Not all evaluation criteria are equally important. The scoring framework is intentionally weighted toward DoD feasibility and UFC relevance, reflecting the project’s standards-driven mission.

5.2 Scoring Categories and Weights

Tools are scored on a 1–5 scale across five categories:

• DoD / USACE Feasibility — 30%
• UFC & Standards Relevance — 25%
• Interoperability & Workflow Integration — 20%
• Maturity & Market Adoption — 15%

ROI Potential (Time / Cost / Risk) – 10%

Scores are multiplied by category weights to produce a total weighted score out of 100. Scoring occurs only after feasibility tiering.

6. Thresholds, Outputs, and Governance

6.1 Evaluation Outputs

Each evaluated tool will produce:

Core identification data and lifecycle classification

Feasibility tier assignment

Quantitative weighted scorecard

Qualitative narrative addressing strengths, limitations, assumptions, and medical relevance

UFC touchpoint mapping indicating potential criteria impact

6.2 Soft Thresholds

Soft thresholds are used to flag risks (e.g., cybersecurity, UFC relevance, interoperability) without disqualifying tools. Final judgments are informed by SME review rather than scores alone.

6.3 SME Validation

Subject Matter Experts review tier assignments, flags, and narratives at the 35%, 65%, and 95% milestones. All adjustments are documented to preserve transparency and auditability.

7. Path Forward to 65%

The 35% framework establishes the methodological backbone for subsequent phases. At 65%, the evaluation matrix will be expanded with additional tools, comparative benchmarking will begin, and preliminary shortlisting logic will be applied to distinguish pilot candidates, criteria-informing tools, and informational references.

8. Conclusion

The 35% Concept Research Assessment successfully establishes a clear, defensible, and future-proof framework for evaluating AI tools relevant to DoD medical facilities. By separating feasibility from

Artificial Intelligence Review

(65% Submittal)

USACE Medical Facilities Mandatory Center of Expertise & Standardization

Rogers, Lovelock & Fritz, Inc.

Executive Summary

This report documents an established structured, defensible framework for evaluating Artificial Intelligence (AI) tools applicable to the planning, design, construction, and lifecycle management of Department of Defense (DoD) medical facilities. The intent is not to develop new AI solutions, but to systematically assess existing, market-available tools to determine their feasibility within DoD constraints and their potential impact on UFC 4-510-01 criteria and associated workflows. This framework addresses a key gap: the absence of a standardized, defensible method for evaluating AI tools within DoD constraints, where cybersecurity, interoperability, and standards alignment are critical.

Key outcomes of the effort thus far include:

A clear, defensible definition of “AI” as it applies to this effort, distinguishing operationally relevant tools from marketing-driven claims.

A three-tier feasibility classification system (DoD-Ready, DoD-Possible, Not Currently Feasible) that reflects real-world deployment constraints rather than binary approval decisions.

A lifecycle-first AI taxonomy aligned with medical facility delivery phases, with built-in flexibility to accommodate cross-cutting capabilities.

A weighted scoring rubric that prioritizes DoD feasibility and UFC relevance over novelty, ensuring alignment with the project’s criteria-driven mission.

Defined evaluation outputs, soft thresholds, and governance logic to support transparency, consistency, and SME-informed judgment.

This framework positions the project, where the evaluation matrix is populated with a broad set of tools and applied shortlisting logic. The framework ensures continuity, future-proofing, and credibility as findings mature and inform potential updates to UFC 4-510-01.

Participants

The following individuals contributed to the content of this report:

Michael Lanier, PMP, Project Integrator Team Lead, USACE Medical Facilities MCX

Allison Pride, RA, Senior Architect, USACE Medical Facilities MCX

Van Woods, Senior Researcher, USACE ERDC Information Technology Laboratory

Brian R. White, AIA NCARB LEED AP, Architect, RLF Architecture Engineering Interiors

1. Introduction and Purpose

This research effort provides a structured methodology to evaluate AI tools as standards-ready capabilities rather than experimental technologies. The focus is on identifying tools that can measurably improve outcomes in DoD medical facility delivery while remaining compatible with USACE workflows, cybersecurity requirements, and UFC governance.

2. Evaluation Boundaries and Guiding Principles

2.1 Mission Statement

This research effort evaluates AI tools to determine what exists today, what is usable within DoD constraints, and how those capabilities could inform future UFC criteria and workflows. The task explicitly avoids AI development or pilot implementation.

2.2 Guiding Principles

Five principles guide all evaluation decisions:

• Standards First — AI must support and clarify UFC criteria, not replace them.
• Evidence Over Hype — Preference is given to tools with demonstrated value and documented use.
• DoD Feasibility — Deployment model, cybersecurity posture, and data handling are decisive factors.
• Criteria Impact — Evaluation consistently asks how a tool could influence UFC 4-510-01.
• Future-Proofing — The framework supports structured, phased adoption rather than one-off pilots.

2.3 Definition of AI

For this research, AI tools are defined as software platforms or systems that use machine learning, computer vision, natural language processing, generative algorithms, or predictive analytics to automate, augment, or materially improve decision-making across the medical facility lifecycle. This definition is intentionally broad yet excludes tools that rely solely on marketing claims without functional AI capability.

3. Tiered Feasibility Classification

Rather than a binary approved/not-approved approach, tools are classified into one of three feasibility tiers:

• Tier 1 — DoD-Ready (Near-Term Feasible): Enterprise-grade tools with documented use in regulated environments and plausible operation within DoD cybersecurity and data constraints.
• Tier 2 — DoD-Possible (Mid-Term Feasible): Mature commercial tools that provide value but require policy, IT, or contractual changes for DoD deployment.
• Tier 3 — Not Currently Feasible (Long-Term / Informational): Early-stage or research-driven tools lacking enterprise controls or requiring unrestricted cloud access.

Tiering functions as a feasibility gate and is applied before any comparative scoring. This prevents infeasible tools from being artificially elevated due to feature richness alone. Only tools classified as Tier 1 (DoD-Ready) or Tier 2 (DoD-Possible) proceed to weighted scoring, ensuring that infeasible tools are excluded from comparative evaluation.

4. AI Classification Strategy

4.1 Lifecycle-Based Taxonomy

AI tools are initially organized by primary medical facility lifecycle phase:

Planning & Programming

Design (BIM / VDC / Optimization)

Construction

Operations & Lifecycle Management

This structure mirrors UFC organization and USACE workflows, improving interpretability for reviewers and working groups.

4.2 Built-In Flexibility

Each tool may also be tagged with secondary lifecycle applicability and meaningfully cross-cutting capabilities. This allows the framework to evolve at later phases without invalidating earlier work or restructuring prior deliverables.

5. Weighted Scoring Governance

5.1 Scoring Philosophy

Not all evaluation criteria are equally important. The scoring framework is intentionally weighted toward DoD feasibility and UFC relevance, reflecting the project’s standards-driven mission.

5.2 Scoring Categories and Weights

Tools are scored on a 1–5 scale across five categories:

• DoD / USACE Feasibility — 30%
• UFC & Standards Relevance — 25%
• Interoperability & Workflow Integration — 20%
• Maturity & Market Adoption — 15%

ROI Potential (Time / Cost / Risk) – 10%

Scores are multiplied by category weights to produce a total weighted score out of 100. Scoring occurs only after feasibility tiering.

6. Thresholds, Outputs, and Governance

6.1 Evaluation Outputs

Each evaluated tool will produce:

Core identification data and lifecycle classification

Feasibility tier assignment

Quantitative weighted scorecard

Qualitative narrative addressing strengths, limitations, assumptions, and medical relevance

UFC touchpoint mapping indicating potential criteria impact

6.2 Soft Thresholds

Soft thresholds function as risk indicators (e.g., cybersecurity gaps, low interoperability), prompting additional SME review rather than automatic exclusion. Final judgments are informed by SME review rather than scores alone.

6.3 SME Validation

Subject Matter Experts review tier assignments, flags, and narratives. All adjustments are documented to preserve transparency and auditability.

7. AI Generated Software for Mission-Specific Design Tools

7.1 Context

The Department of Defense relies on highly specialized standards such as UFC 4-510-01 for Military Medical Facilities to ensure consistency, safety, and operational performance in facility design. Updating and implementing these standards requires significant coordination across architects, engineers, and subject-matter experts.

One challenge identified during the early research process is the lack of custom digital tools that translate UFC criteria into practical workflows during planning and design.

Historically, developing these tools required dedicated software development resources or reliance on commercial software platforms that may not align precisely with DoD requirements.

7.2 Emerging Capability: AI-Generated Software

Recent advances in AI coding agents, such as Claude Code, demonstrate a new capability: the ability to generate functional software tools directly from written instructions.

Instead of commissioning a development team, users can describe the desired functionality in natural language and the AI can generate the application structure, logic, and interface.

This enables rapid creation of mission-specific digital tools tailored to unique workflows, including those associated with UFC implementation.

7.3 Demonstration Example

During the research effort supporting this project, a custom AI Evaluation Matrix application was created using Claude Code.

The tool was developed to support the systematic evaluation of artificial intelligence technologies relevant to the design and lifecycle performance of DoD medical facilities.

Rather than relying on existing software platforms, the application was generated through AI-assisted development based on a clear description of required capabilities, including:

Scoring frameworks for AI tools

Tier-gate feasibility screening

Compatibility checks with DoD cybersecurity and deployment constraints

UFC touchpoint mapping to design workflows

The development process required hours rather than weeks or months, demonstrating how AI coding agents can rapidly generate custom research and evaluation tools.

7.4 Relevance to UFC Research Objectives

This example highlights how AI-generated software can support DoD research and design processes in several ways:

Rapid prototyping of specialized tools

Teams can quickly create digital tools that interpret and apply UFC criteria within design workflows.

Customization for government standards

Tools can be tailored specifically to UFC, USACE, and DHA requirements rather than adapting commercial software designed for general markets.

Accelerated innovation cycles

Ideas for improving design evaluation or compliance verification can be implemented and tested almost immediately.

7.5 Strategic Implications for DoD Design

The ability to rapidly generate software tools may significantly change how design and research teams support military facility standards.

Rather than waiting for commercial software vendors to develop new capabilities, teams may increasingly be able to create targeted applications on demand to support planning, design evaluation, and facility lifecycle analysis.

This capability aligns with the broader goal of the research task:

to identify emerging AI technologies that measurably improve the design, construction, and lifecycle performance of DoD medical facilities while remaining compatible with USACE and DHA standards.

7.6 Key Takeaway

AI coding agents demonstrate that custom digital tools for implementing UFC guidance can now be created quickly and cost-effectively.

The example of the AI Evaluation Matrix illustrates how AI-generated software can enable new workflows that support DoD design research, analysis, and decision-making.

As these capabilities mature, they may allow research teams and design organizations to rapidly develop mission-specific applications that improve the implementation of UFC standards across the entire facility lifecycle.

Examples of custom AI Evaluation Matrix application was created using Claude Code.

8. AI Evaluation Matrix

“Screen shots of matrix”

9. Conclusion

This research effort successfully establishes a clear, defensible, and future-proof framework for evaluating AI tools relevant to DoD medical facilities. By separating feasibility from performance-based scoring, the framework ensures that only viable, DoD-aligned tools are evaluated in depth, supporting transparent, defensible, and criteria-driven decision-making for future UFC updates.

Appendix A – USACE AI Systems

Tools Currently Available

The suite of tools available for general use by USACE employees continues to evolve at a rapid pace. Here is a snapshot of the currently available tools:

Generative AI for the DoW

https://genai.mil/

CoPilot

https://copilot.microsoft.com/

Vantage

https://vantage.army.mil/workspace/slate/app/ri.slate.main.document.68ea85eb-7d19-4dd1-b6b2-58625f3f8348/home

Ask Sage

https://chat.genai.army.mil/dashboard

Perplexity

https://www.perplexity.ai/

Appendix B – USACE AI Policy

Office of the Chief of Information

Hello USACE Team:

Generative Artificial Intelligence (GenAI) platforms like ChatGPT, GROK, Google Gemini, Copilot, and Meta AI that leverage large language models (LLMs) offer transformative benefits in terms of productivity, automation, and innovation. However, their use presents significant security, operational, and ethical risks that must be considered in accordance with USACE, Army, and DoD guidance. The USACE AI guidance provided below has been developed in alignment with guidance provided by the Army CIO in CS-SEC-SC-003 (CUI) Safeguarding United States Government Data and Information on Open Source and Commercial Applications and Services and ADS-GOV-AI-024 Chief Information Officer Guidance on Generative Artificial Intelligence and Large Language Models.

USACE is actively working on strategy, policies, and the deployment of AI tools to the workforce to include leveraging DoD and Army provisioned AI tools such as CamoGPT, Vantage, NIPRGPT, and the Army Enterprise LLM Workspace (AskSage). Future deployments of Microsoft CoPilot with CUI / IL5 authorization across our USACE Microsoft 365 platform are coming this Fall. Many other updates are anticipated through the end of 2025 with new DoD, Army, and USACE capabilities that are currently in development. We will distribute updates and recommended training on these capabilities as they are deployed.

Please see the below USACE guidance when using AI platforms as part of your work.

Currently Approved GenAI Platforms for CUI or Sensitive Data (these require CAC authentication):

General GenAI / LLM Tools:

CamoGPT: https://camogpt.army.mil/

Army Enterprise LLM Workspace (AskSage) : https://chat.genai.army.mil/

Initial trial tokens available. Army is working to provision additional resources and USACE will be developing procurement guidance if necessary.

NIPRGPT: https://niprgpt.mil/

Powerful Data Analytics and AI Platforms:

Army Vantage Data Analytics Platform: https://vantage.army.mil/

ADVANA Analytics: https://advana.data.mil/

Contracting / Acquisition Focused Tools:

AcqBot: https://acqbot.niprgpt.mil/

CDAO Tradewinds: https://www.tradewindai.com/

NOTE: Other LLM / GenAI platforms such as public versions of ChatGPT, Copilot, GROK, Claude, Meta AI available through commercial vendors are NOT authorized for any sensitive or CUI information and should only be used for Distribution A, non-sensitive activities.

How Can You Learn More?

For more information on USACE AI efforts and links to training, prompt engineering guidance, and more, reach out to the USACE AI Community of Practice: https://usace.dps.mil/sites/KMP-AI

A wealth of free training on GenAI tools and prompt engineering is also available through the Army’s Udemy platform at: https://armyciv.udemy.com/

Approved Usage Guidelines:

Do not input Classified, CUI, or sensitive data (e.g., PII, HIPAA, procurement sensitive, attorney-client privileged, and pre-decisional materials) into publicly available GenAI platforms. Please review the examples provided in the attached reference Example CUI and Sensitive Information Types and Media Reported Security Failures.

Mandatory CUI Training: https://securityawareness.dcsa.mil/cui/index.html

Only use approved, authorized or USACE hosted GenAI platforms when working with sensitive data, ensuring the platform’s impact level is appropriate for the data classification.

Public GenAI platforms may only be used with unclassified, non-sensitive, non-CUI data.

Aggregation of multiple data points or types of information may still result in CUI output so proceed with caution.

Certain platforms including DeepSeek are explicitly prohibited for Army use under any circumstances.

Validate and verify GenAI generated output prior to usage. Incorporating it into official USACE documents, briefings, or decisions given the abundance of AI hallucinations is a significant issue, including hallucinations that would be considered CUI.

Uploading Files into GenAI Platforms:

Uploading of files into GenAI platforms that are not authorized to handle CUI is currently blocked (such as ChatGPT and Google Gemini).

CIO/G6 is currently re-examining this policy to allow for users to upload non-sensitive, Distribution A information as needed into platforms like ChatGPT to support your work. Be on the look out for updates on this topic.

It is your responsibility to ensure that no CUI files, text, or data are uploaded into unapproved AI platforms that are not authorized to handle CUI.

Information Security and Data Protection:

Comply with USACE cybersecurity policies, Army and DoD security requirements (e.g., DoDI 5200.48 – Controlled Unclassified Information).

Clearly label content that is generated in whole or in part using GenAI tools.

Do not use any sensitive data to train or fine tune GenAI models unless approved by the appropriate data owners.

Be aware that public GenAI platforms retain user inputs and uses the input to train their models, increasing the risk of unauthorized data disclosure. Although specifics of information or user inputs are typically concealed, all information input into GenAI platforms trains these models to generate new information.

Verification and Compliance:

Carefully review GenAI output for accuracy, appropriateness, and alignment with official guidance.

When in doubt, consult your local USACE Security office or CIO/G6 cybersecurity team.

If GenAI outputs will be used in public facing or decision-making documents, always validate with authoritative sources.

For external communication in particular, ensure coordination with appropriate Public Affairs Office personnel who have also implemented Army guidance on AI in public affairs and communications. Please see the attached Army AI Guidance for Public Affairs for more information.

Need Help or Have Questions?

Please coordinate with your USACE CIO/G6 office or contact our Cybersecurity Team DLL-CEIT-ZS-GovernancePolicy@usace.army.mil for clarification on approved usage and platform availability.

Please contact Governance & Architecture Division for any IT policy related questions USACE-CIO-GAD@usace.army.mil

Contact PrivacyOffice@usace.army.mil for any privacy related question.

For questions on AI and communications, engagement with your local Public Affairs Office.

Why It Matters:

Proper use of GenAI protects Army data, USACE missions, and public trust. As stewards of critical information of many forms, it’s essential we strike the right balance between modernization using AI and operational security.

References / Attachments:

CS-SEC-SC-003 (CUI) Safeguarding United States Government Data and Information on Open Source and Commercial Applications and Services.

ADS-GOV-AI-024 Chief Information Officer Guidance on Generative Artificial Intelligence and Large Language Models

Example CUI and Sensitive Information Types and Media Reported Security Failures

Army AI Guidance for Public Affairs

Thank you for your continued commitment to safeguarding information as we responsibly integrate emerging technologies into USACE operations.

v/r

Robert

Robert D. Moser, PhD, SES

Chief Information Officer / G6

US Army Corps of Engineers (USACE)

Email: robert.d.moser@usace.army.mil

SIPR: robert.d.moser24@mail.smil.mil

Phone: 601.634.3261 (office) / 601.618.9066 (mobile)

EA: Ms. Jocelyn Johnson / 202.579.1024 / Jocelyn.N.Johnson@usace.army.mil

Appendix C – USACE AI Adoption Strategy

U.S. ARMY CORPS OF ENGINEERS (USACE)

ARTIFICIAL INTELLIGENCE (AI) ADOPTION

STRATEGY

6 February 2025

This appendix provides a comprehensive A–Z glossary of terms relevant to this research task (Artificial Intelligence Review) supporting the update of UFC 4-510-01 (Design: Military Medical Facilities). Definitions reflect DoD, USACE, AEC, cybersecurity, BIM, and AI governance contexts. This is a living appendix and will be updated as additional tools, platforms, and workflows are evaluated.

• Algorithm — A defined computational procedure used to process data and generate outputs.
• AEC — Architecture, Engineering, and Construction industry sector.
• API (Application Programming Interface) — Mechanism allowing software systems to exchange data and functionality.
• Asset Management — Lifecycle tracking and optimization of facility systems and equipment.
• Audit Log — Recorded system/user activity for accountability and compliance.

• BIM (Building Information Modeling) — Digital representation of facility physical and functional characteristics.
• BEP (BIM Execution Plan) — Document defining BIM uses, standards, and responsibilities.
• Black Box Model — AI system with limited transparency into internal decision logic.
• Building Automation System (BAS) — Control system managing HVAC, lighting, and other building systems.

• Change Order — Contract modification affecting scope, cost, or schedule.
• Clash Detection — Identification of model conflicts (e.g., MEP vs structure).
• Cloud Computing — Vendor-hosted computing resources accessed via network.
• COBie — Structured data format for facility asset handover.
• Computer Vision (CV) — AI enabling interpretation of images and video.
• Constructability — Practical buildability of a design.
• Cybersecurity Posture — Overall security readiness of a system or vendor.

• Data Governance — Policies and controls managing data quality, security, and usage.
• Data Residency — Geographic/legal location of stored data.
• Data Sensitivity — Risk classification of data (e.g., PHI, drawings, mission data).
• Digital Delivery — End-to-end digital project information workflow.
• Digital Twin — Dynamic digital representation of a facility linked to performance data.

• Edge Computing — Localized processing near data source.
• Enterprise-Grade — Software designed for large regulated organizations.
• Explainability — Ability to interpret and justify AI outputs.

• FedRAMP — Federal authorization program for cloud security compliance.
• Feasibility Tier — Classification of AI deployability (Tier 1 DoD-Ready; Tier 2 DoD-Possible; Tier 3 Informational).
• Facility Lifecycle — Planning, design, construction, operations, and sustainment phases.

• Generative AI — AI capable of producing designs, layouts, text, or alternatives.
• Generative Design — Algorithm-driven creation of optimized design options.

• Handover Data — Information transferred from construction to operations.
• Hybrid Deployment — Combination of cloud and on-premise computing.

• IFC (Industry Foundation Classes) — Open BIM data exchange standard.
• Interoperability — Ability of systems to exchange and use shared data.
• Integration Risk — Potential workflow disruption due to poor system compatibility.

• Justification Narrative — Documented reasoning supporting evaluation scores or recommendations.

• Knowledge Model — Structured representation of rules or expertise within software.

• Lifecycle Phase — Planning, Design, Construction, Operations.
• Large Language Model (LLM) — AI model trained on large text datasets for language tasks.

• Machine Learning (ML) — Algorithms that learn patterns from data.
• Maturity — Degree of proven deployment and vendor stability.
• Model-Based Coordination — Use of BIM to coordinate disciplines.

• Natural Language Processing (NLP) — AI enabling analysis and generation of human language.
• Network Segmentation — Isolation of systems for cybersecurity control.

• On-Premises Deployment — Software hosted within local secure infrastructure.
• Operations & Maintenance (O&M) — Facility sustainment activities post-construction.

• Predictive Analytics — AI forecasting outcomes using historical data.
• PHI (Protected Health Information) — Sensitive health-related data requiring protection.
• Pilot Program — Controlled deployment to evaluate tool performance.

QA/QC (Quality Assurance / Quality Control) – Processes ensuring compliance and accuracy.

• Risk Mitigation — Actions reducing likelihood or impact of adverse events.
• ROI (Return on Investment) — Measurable benefit relative to cost.

• Scoring Matrix — Structured evaluation table assigning weighted scores.
• Soft Threshold Flag — Non-disqualifying risk indicator in evaluation framework.
• Standards Relevance — Degree of impact on UFC criteria or workflows.

• Tier 1 (DoD-Ready) — Near-term feasible within DoD constraints.
• Tier 2 (DoD-Possible) — Feasible with policy or IT adjustments.
• Tier 3 (Informational) — Not currently deployable.
• Traceability — Ability to track decisions and data lineage.

• UFC (Unified Facilities Criteria) — DoD design standards governing military facilities.
• UFC Touchpoint — Section or workflow within UFC potentially influenced by AI.

• Validation — Confirmation that system outputs meet intended requirements.
• Vendor Stability — Financial and operational reliability of software provider.

• Workflow Integration — Alignment of tool functionality with existing project processes.
• Weighted Scoring — Assignment of percentage-based importance to evaluation criteria.

• XML (eXtensible Markup Language) — Structured data format sometimes used in BIM exchanges.

• Yield Optimization — Improvement of performance or efficiency through data-driven methods.

• Zero Trust Architecture — Cybersecurity model requiring continuous verification of users and devices.

Artificial Intelligence Review

(95% Submittal)

USACE Medical Facilities Mandatory Center of Expertise & Standardization

Rogers, Lovelock & Fritz, Inc.

Executive Summary

This report documents an established structured, defensible framework for evaluating Artificial Intelligence (AI) tools applicable to the planning, design, construction, and lifecycle management of Department of Defense (DoD) medical facilities. The intent is not to develop new AI solutions, but to systematically assess existing, market-available tools to determine their feasibility within DoD constraints and their potential impact on UFC 4-510-01 criteria and associated workflows. This framework addresses a key gap: the absence of a standardized, defensible method for evaluating AI tools within DoD constraints, where cybersecurity, interoperability, and standards alignment are critical.

Key outcomes of the effort thus far include:

A clear, defensible definition of "AI" as it applies to this effort, distinguishing operationally relevant tools from marketing-driven claims.

A three-tier feasibility classification system (DoD-Ready, DoD-Possible, Not Currently Feasible) that reflects real-world deployment constraints rather than binary approval decisions.

A lifecycle-first AI taxonomy aligned with medical facility delivery phases, with built-in flexibility to accommodate cross-cutting capabilities.

A weighted scoring rubric that prioritizes DoD feasibility and UFC relevance over novelty, ensuring alignment with the project's criteria-driven mission.

Defined evaluation outputs, soft thresholds, and governance logic to support transparency, consistency, and SME-informed judgment.

This framework positions the project, where the evaluation matrix is populated with a broad set of tools and applied shortlisting logic. The framework ensures continuity, future-proofing, and credibility as findings mature and inform potential updates to UFC 4-510-01.

Participants

The following individuals contributed to the content of this report:

Michael Lanier, PMP, Project Integrator Team Lead, USACE Medical Facilities MCX

Allison Pride, RA, Senior Architect, USACE Medical Facilities MCX

Van Woods, Senior Researcher, USACE ERDC Information Technology Laboratory

Brian R. White, AIA NCARB LEED AP, Architect, RLF Architecture Engineering Interiors

1. Introduction and Purpose

This research effort provides a structured methodology to evaluate AI tools as standards-ready capabilities rather than experimental technologies. The focus is on identifying tools that can measurably improve outcomes in DoD medical facility delivery while remaining compatible with USACE workflows, cybersecurity requirements, and UFC governance.

2. Evaluation Boundaries and Guiding Principles

2.1 Mission Statement

This research effort evaluates AI tools to determine what exists today, what is usable within DoD constraints, and how those capabilities could inform future UFC criteria and workflows. The task explicitly avoids AI development or pilot implementation.

2.2 Guiding Principles

Five principles guide all evaluation decisions:

• Standards First — AI must support and clarify UFC criteria, not replace them.
• Evidence Over Hype — Preference is given to tools with demonstrated value and documented use.
• DoD Feasibility — Deployment model, cybersecurity posture, and data handling are decisive factors.
• Criteria Impact — Evaluation consistently asks how a tool could influence UFC 4-510-01.
• Future-Proofing — The framework supports structured, phased adoption rather than one-off pilots.

2.3 Definition of AI

For this research, AI tools are defined as software platforms or systems that use machine learning, computer vision, natural language processing, generative algorithms, or predictive analytics to automate, augment, or materially improve decision-making across the medical facility lifecycle. This definition is intentionally broad yet excludes tools that rely solely on marketing claims without functional AI capability.

3. Tiered Feasibility Classification

Rather than a binary approved/not-approved approach, tools are classified into one of three feasibility tiers:

• Tier 1 — DoD-Ready (Near-Term Feasible): Enterprise-grade tools with documented use in regulated environments and plausible operation within DoD cybersecurity and data constraints.
• Tier 2 — DoD-Possible (Mid-Term Feasible): Mature commercial tools that provide value but require policy, IT, or contractual changes for DoD deployment.
• Tier 3 — Not Currently Feasible (Long-Term / Informational): Early-stage or research-driven tools lacking enterprise controls or requiring unrestricted cloud access.

Tiering functions as a feasibility gate and is applied before any comparative scoring. This prevents infeasible tools from being artificially elevated due to feature richness alone. Only tools classified as Tier 1 (DoD-Ready) or Tier 2 (DoD-Possible) proceed to weighted scoring, ensuring that infeasible tools are excluded from comparative evaluation.

4. AI Classification Strategy

4.1 Lifecycle-Based Taxonomy

AI tools are initially organized by primary medical facility lifecycle phase:

Planning & Programming

Design (BIM / VDC / Optimization)

Construction

Operations & Lifecycle Management

This structure mirrors UFC organization and USACE workflows, improving interpretability for reviewers and working groups.

4.2 Built-In Flexibility

Each tool may also be tagged with secondary lifecycle applicability and meaningfully cross-cutting capabilities. This allows the framework to evolve at later phases without invalidating earlier work or restructuring prior deliverables.

5. Weighted Scoring Governance

5.1 Scoring Philosophy

Not all evaluation criteria are equally important. The scoring framework is intentionally weighted toward DoD feasibility and UFC relevance, reflecting the project's standards-driven mission.

5.2 Scoring Categories and Weights

Tools are scored on a 1–5 scale across five categories:

• DoD / USACE Feasibility — 30%
• UFC & Standards Relevance — 25%
• Interoperability & Workflow Integration — 20%
• Maturity & Market Adoption — 15%

ROI Potential (Time / Cost / Risk) – 10%

Scores are multiplied by category weights to produce a total weighted score out of 100. Scoring occurs only after feasibility tiering.

6. Thresholds, Outputs, and Governance

6.1 Evaluation Outputs

Each evaluated tool will produce:

Core identification data and lifecycle classification

Feasibility tier assignment

Quantitative weighted scorecard

Qualitative narrative addressing strengths, limitations, assumptions, and medical relevance

UFC touchpoint mapping indicating potential criteria impact

6.2 Soft Thresholds

Soft thresholds function as risk indicators (e.g., cybersecurity gaps, low interoperability), prompting additional SME review rather than automatic exclusion. Final judgments are informed by SME review rather than scores alone.

6.3 SME Validation

Subject Matter Experts review tier assignments, flags, and narratives. All adjustments are documented to preserve transparency and auditability.

7. AI Generated Software for Mission-Specific Design Tools

7.1 Context

The Department of Defense relies on highly specialized standards such as UFC 4-510-01 for Military Medical Facilities to ensure consistency, safety, and operational performance in facility design. Updating and implementing these standards requires significant coordination across architects, engineers, and subject-matter experts.

One challenge identified during the early research process is the lack of custom digital tools that translate UFC criteria into practical workflows during planning and design.

Historically, developing these tools required dedicated software development resources or reliance on commercial software platforms that may not align precisely with DoD requirements.

7.2 Emerging Capability: AI-Generated Software

Recent advances in AI coding agents, such as Claude Code, demonstrate a new capability: the ability to generate functional software tools directly from written instructions.

Instead of commissioning a development team, users can describe the desired functionality in natural language and the AI can generate the application structure, logic, and interface.

This enables rapid creation of mission-specific digital tools tailored to unique workflows, including those associated with UFC implementation.

7.3 Demonstration Example

During the research effort supporting this project, a custom AI Evaluation Matrix application was created using Claude Code.

The tool was developed to support the systematic evaluation of artificial intelligence technologies relevant to the design and lifecycle performance of DoD medical facilities.

Rather than relying on existing software platforms, the application was generated through AI-assisted development based on a clear description of required capabilities, including:

Scoring frameworks for AI tools

Tier-gate feasibility screening

Compatibility checks with DoD cybersecurity and deployment constraints

UFC touchpoint mapping to design workflows

The development process required hours rather than weeks or months, demonstrating how AI coding agents can rapidly generate custom research and evaluation tools.

7.4 Relevance to UFC Research Objectives

This example highlights how AI-generated software can support DoD research and design processes in several ways:

• Rapid prototyping of specialized tools — Teams can quickly create digital tools that interpret and apply UFC criteria within design workflows.
• Customization for government standards — Tools can be tailored specifically to UFC, USACE, and DHA requirements rather than adapting commercial software designed for general markets.
• Accelerated innovation cycles — Ideas for improving design evaluation or compliance verification can be implemented and tested almost immediately.

7.5 Strategic Implications for DoD Design

The ability to rapidly generate software tools may significantly change how design and research teams support military facility standards.

Rather than waiting for commercial software vendors to develop new capabilities, teams may increasingly be able to create targeted applications on demand to support planning, design evaluation, and facility lifecycle analysis.

This capability aligns with the broader goal of the research task:

to identify emerging AI technologies that measurably improve the design, construction, and lifecycle performance of DoD medical facilities while remaining compatible with USACE and DHA standards.

7.6 Key Takeaway

AI coding agents demonstrate that custom digital tools for implementing UFC guidance can now be created quickly and cost-effectively.

The example of the AI Evaluation Matrix illustrates how AI-generated software can enable new workflows that support DoD design research, analysis, and decision-making.

As these capabilities mature, they may allow research teams and design organizations to rapidly develop mission-specific applications that improve the implementation of UFC standards across the entire facility lifecycle.

8. AI Evaluation Matrix

The AI Evaluation Matrix was developed to systematically evaluate and prioritize Artificial Intelligence technologies for application within UFC 4-510-01 medical facility planning, design, construction, and operations. The tool provides a structured framework for assessing AI solutions against DoD-specific criteria including deployment feasibility, cybersecurity considerations, UFC relevance, interoperability, market maturity, and return on investment.

The application combines a tier-based feasibility assessment, a weighted scoring methodology, and SME validation workflows to create a repeatable and defensible evaluation process. AI tools are categorized by capability area and scored using a standardized rubric, allowing direct comparison across technologies and identification of potential pilot candidates.

The platform currently includes evaluations of leading AI solutions across generative design, QA/QC, construction analytics, digital twins, and document intelligence. Results are stored in a searchable database with dashboard, reporting, and export capabilities to support ongoing research, benchmarking, and decision-making efforts.

9. Use Cases

• (1) Standards & Code Cross-Referencing — Rapidly cross-reference and identify overlapping or conflicting requirements across applicable criteria; FGI Guidelines, NFPA 101 (Life Safety Code), NFPA 99 (Health Care Facilities Code), Joint Commission, Architectural Barriers Act (ABA), UFC 4-510-01 (DoD Medical Facilities), and other applicable standards.

(2) Design Criteria Document Drafting & Review – Develop, review and quality-check Basis of Design (BOD), Design Analysis, Narratives, and supporting documentation. SMEs can use the system to ensure consistency in terminology and tone, completeness of discipline specific narratives, and alignment with organizational templates and standards.

(3) Space Programming & Adjacency Validation – Validate functional space programs, departmental adjacencies, and clinical workflow logic (e.g., sterile core relationships, surgical suite circulation, ED-to-imaging flow). Utilize AI to cross reference programmed areas against established programming documentation (SEPS / PFD), identify missing functional areas, summarize and list programmed areas not meeting requirements (Net/Gross SF), cross-reference against established meeting summaries of customer adjacency priorities, clinical workflows, and identify missing support spaces.

(4) Infection Control & Risk Assessment Support – Assist in reviewing Infection Control Risk Assessments (ICRA) / Interim Life Safety Measures. Review contract technical standards, construction phasing, and infrastructure. Support SOW development, interdisciplinary coordination (Mech, Electrical, Fire Protection, Architectural), and development of ICRA.

(5) Plan Review & Compliance Gap Analysis – Integrate into software supporting design and construction submittals. Utilize to perform structured technical compliance reviews — comparing submitted documents against applicable criteria and comparative performance, quality, and quantity analysis between contract documents, producing prioritized comment matrices for technical reviews in support of review meetings.

• (6) Knowledge Repository & SME Continuity — Codify the institutional knowledge of senior SMEs into a searchable, knowledge base — mitigating risk from retirements and workforce turnover, and onboarding junior staff faster through conversational access to "historical knowledge."

(7) RFI, Submittal & Specification Analysis – Quickly review and summarize RFIs, submittals, and technical specifications (e.g., CSI MasterFormat divisions), draft suggested responses, and check spec language against design criteria for completeness and contradictions.

(8) Lessons Learned & Technical Post-Occupancy Reports – Aggregate and evaluate Technical Post-Occupancy Evaluations (TPOE) and Lessons Learned across portfolio to extract recurring administrative, design, and construction issues, informing updates to standards and criteria documents.

(9) Regulatory & Standards Change Monitoring Support – Help SMEs interpret and operationalize updated standards (e.g., new FGI or ASHRAE editions), summarizing what's changed, impact to design, construction and operations & maintenance, and recommended revisions to internal criteria documents.

(10) Stakeholder Communication & Translation – Translate dense technical criteria into plain-language briefings for clinicians, executives, and end-users, and generate presentation materials, comparison tables, and visual diagrams (e.g., decision flowcharts, adjacency matrices) to support design charrettes.

Recommended next steps: Prioritize use cases that (1) carry the highest compliance/safety risk, (2) consume the most SME time today, and (3) have well-structured source documents to review. Cases 1, 2, 5, and 6 typically deliver the fastest, most defensible ROI.

9.1 Detailed Workflows, Sample Prompts & Implementation Roadmap

Below is a comprehensive playbook for all 10 use cases. Each includes a workflow, sample prompts, and key inputs to review. A consolidated implementation roadmap follows at the end.

9.2 Standards & Code Cross-Referencing

Workflow:

Complete code research and review the governing standards into a dataset (FGI, NFPA 99/101, ASHRAE 170, UFC 4-510-01, Joint Commission, ABA, local codes).

Identify a specific system or feature of work for analysis.

Produce a comparison matrix that identifies overlapping requirements and prioritize the most stringent requirements for consideration.

SME validates and exports to the criteria document.

Sample Prompts:

"Compare the air change rate, pressure relationship, and temperature requirements for an Operating Room across FGI 2022, ASHRAE 170-2021, and UFC 4-510-01. Present as a table and identify the governing (most stringent) value for each parameter."

"Calculate occupant loads and identify minimum corridor width requirements for specific facility type and occupancy and flag any conflicts between NFPA 10, Building Code, and applicable design standards or guides."

Key Inputs: Current editions of all applicable standards, organizational supplements, AHJ amendments.

9.3 Design Criteria Document Drafting & Review

Workflow:

Review your code research, program requirements, design objectives / goals / performance requirements and BOD/Design Narrative template.

Provide project parameters (facility type, size, location, CONOPS, O&M, Sustainability / Performance).

Generate first draft section-by-section.

Complete quality control review.

Sample Prompts:

"Draft a Mechanical Basis of Design narrative section for a 120-bed community hospital, following the attached template structure and referencing ASHRAE 170 ventilation criteria."

"Review the attached Design Narrative against our standard template and produce a checklist of missing or incomplete sections."

Key Inputs: BOD/narrative templates, applicable criteria, CONOPs.

9.4 Space Programming & Adjacency Validation

Workflow:

Review the functional space program (room list, areas, departments).

Document requirements or clinical workflow priorities (user group meetings, CONOPS.

Utilize AI to evaluate concept / schematic layout adjacencies, benchmark areas, and flag gaps.

Sample Prompts:

"Review this surgical suite space program. Identify any missing support spaces (e.g., sterile core, sub-sterile, equipment storage, PACU) based on Program for Design (PFD)."

"Generate an adjacency matrix (mermaid diagram) showing required relationships between the ED, imaging, lab, and surgical departments."

Key Inputs: Space program spreadsheet (as CSV), area benchmarks, workflow narratives.

9.5 Infection Control & Risk Assessment Support

Workflow:

Review ASHRAE 170, FGI ventilation tables, and ICRA templates.

Provide room-by-room list.

Generate ventilation/pressure tables and ICRA draft language.

SME reviews and certifies.

Sample Prompts:

"Create a ventilation requirements table (ACH, pressure relationship, filtration MERV, RH/temp) for these rooms: Isolation Exam Room, OR, Bronchoscopy, Soiled Utility, per ASHRAE 170."

"Draft ICRA Class III language for a renovation adjacent to an active oncology infusion suite."

Key Inputs: ASHRAE 170 tables, FGI ventilation appendix, ICRA matrix/templates.

9.6 Plan Review & Compliance Gap Analysis

Workflow:

Review governing design criteria AND the design submittal.

Run a structured comparison.

Generate a prioritized comment matrix (issue, reference, severity, recommendation).

SME validates before issuing comments.

Sample Prompts:

"Compare the attached 50% design submittal against our design criteria. Produce a comment matrix with columns: Comment, Discipline, Location/Sheet, Issue, Governing Reference, Severity (Critical/Major/Minor), Recommendation."

"Identify any life safety code deficiencies in the egress design described in this narrative."

Key Inputs: Design criteria, submittal documents/narratives, comment matrix template.

9.7 Knowledge Capture & SME Continuity

Workflow:

Review historical project files, standard details, lessons learned, internal guides.

Build a curated, searchable knowledge dataset.

Train staff on conversational retrieval.

Periodically refresh the dataset.

Sample Prompts:

"What is our organization's standard approach to headwall configuration in med/surg patient rooms, based on the applicable standards and clinical workflows?"

"Summarize the rationale behind our preference for [X system] in central sterile, as documented in past project files."

Key Inputs: Standard details, internal design guides, decision memos, retiring-SME interview transcripts.

9.8 RFI, Submittal & Specification Analysis

Workflow:

Review the relevant spec section, contract document and design criteria.

Provide the RFI/submittal.

Generate a summary and a draft response with citations.

SME review.

Sample Prompts:

"Summarize this RFI and draft a response. Check the proposed submittal against Division 23 specifications and design criteria; identify non-compliance and cite location within contract documents."

"Review this submittal data sheet against the specified performance requirements and list any discrepancies."

Key Inputs: Spec sections (CSI MasterFormat), design criteria, RFI/submittal documents.

9.9 Lessons Learned & Post-Occupancy Synthesis

Workflow:

Review technical post occupancy survey reports and lessons-learned across the portfolio.

Query for recurring themes.

Generate trend analysis, evaluate context of the lesson learned. Identify if issue is unique to a project, location or PDT. Determine and recommended criteria updates if appropriate.

Submit CCR to appropriate standards committee.

Sample Prompts:

"Across the submitted 12 POE reports, identify the top recurring design issues by frequency and discipline. Present as a ranked table."

"Identify any unique or project specific considerations that may have led to the design issue."

"Recommend specific revisions to our patient room design criteria or design and construction process, based on the recurring lessons learned."

Key Inputs: POE reports, lessons-learned logs, AARs, warranty/maintenance complaint data.

9.10 Regulatory & Standards Change Monitoring Support

Workflow:

Review the previous and current editions of a standard.

Request a change summary.

Assess design/cost impact.

Update internal criteria.

Sample Prompts:

"Summarize the substantive changes between FGI 2018 and FGI 2022 for outpatient facilities and describe the design impact of each."

"Which sections of our internal design criteria need revision based on the ASHRAE 170-2021 updates?"

Key Inputs: Prior and current standard editions, internal criteria documents.

9.11 Stakeholder Communication & Translation

Workflow:

Provide the technical content and target audience.

Generate plain-language briefings, tables, or diagrams.

Produce presentation materials.

SME reviews for accuracy.

Sample Prompts:

"Translate OR ventilation requirements into a plain-language one-pager for clinical staff who are not engineers."

"Create a PowerPoint summarizing key design criteria decisions for an executive steering committee."

"Generate a decision flowchart (mermaid) for selecting patient room type based on acuity."

Key Inputs: Technical criteria, audience profile, branding/template guidance.

9.12 Consolidated Implementation Roadmap

• Phase 1 — Foundation (Months 1–2): Use Cases 1, 2, 6. Key activities: review core standards & templates; build knowledge dataset; train pilot SMEs; establish prompt library. Success metrics: dataset live; 5+ SMEs trained; reduced lookup time.
• Phase 2 — Quality & Review (Months 3–4): Use Cases 4, 5, 7. Key activities: operationalize compliance gap analysis & ventilation tables; integrate into review process. Success metrics: faster plan reviews; standardized comment matrices.
• Phase 3 — Programming & Insights (Months 5–6): Use Cases 3, 8. Key activities: program validation workflows; portfolio-wide lessons-learned synthesis. Success metrics: reduced programming rework; criteria updates issued.
• Phase 4 — Governance & Communication (Months 7–8): Use Cases 9, 10. Key activities: standards-change monitoring cadence; stakeholder-facing materials; governance committee. Success metrics: continuous currency criteria; improved stakeholder satisfaction.

9.13 Cross-Cutting Enablers

• Human-in-the-loop — SMEs always validate AI outputs before official use. This step is critical for safety/compliance and accountability.
• Prompt library — Build a shared, version-controlled repository of vetted prompts.
• Data governance — Ensure reviewed standards are current editions and properly licensed; manage sensitivity/classification of project data.
• Feedback loop — Capture SME corrections to continuously improve prompts and datasets.

10. Conclusion

This research effort successfully establishes a clear, defensible, and future-proof framework for evaluating AI tools relevant to DoD medical facilities. By separating feasibility from performance-based scoring, the framework ensures that only viable, DoD-aligned tools are evaluated in depth, supporting transparent, defensible, and criteria-driven decision-making for future UFC updates.

Appendix A – Comprehensive Terminology (A–Z)

This appendix provides a comprehensive A–Z glossary of terms relevant to this research task (Artificial Intelligence Review) supporting the update of UFC 4-510-01 (Design: Military Medical Facilities). Definitions reflect DoD, USACE, AEC, cybersecurity, BIM, and AI governance contexts. This is a living appendix and will be updated as additional tools, platforms, and workflows are evaluated.

• Algorithm — A defined computational procedure used to process data and generate outputs.
• AEC — Architecture, Engineering, and Construction industry sector.
• API (Application Programming Interface) — Mechanism allowing software systems to exchange data and functionality.
• Asset Management — Lifecycle tracking and optimization of facility systems and equipment.
• Audit Log — Recorded system/user activity for accountability and compliance.

• BIM (Building Information Modeling) — Digital representation of facility physical and functional characteristics.
• BEP (BIM Execution Plan) — Document defining BIM uses, standards, and responsibilities.
• Black Box Model — AI system with limited transparency into internal decision logic.
• Building Automation System (BAS) — Control system managing HVAC, lighting, and other building systems.

• Change Order — Contract modification affecting scope, cost, or schedule.
• Clash Detection — Identification of model conflicts (e.g., MEP vs structure).
• Cloud Computing — Vendor-hosted computing resources accessed via network.
• COBie — Structured data format for facility asset handover.
• Computer Vision (CV) — AI enabling interpretation of images and video.
• Constructability — Practical buildability of a design.
• Cybersecurity Posture — Overall security readiness of a system or vendor.

• Data Governance — Policies and controls managing data quality, security, and usage.
• Data Residency — Geographic/legal location of stored data.
• Data Sensitivity — Risk classification of data (e.g., PHI, drawings, mission data).
• Digital Delivery — End-to-end digital project information workflow.
• Digital Twin — Dynamic digital representation of a facility linked to performance data.

• Edge Computing — Localized processing near data source.
• Enterprise-Grade — Software designed for large regulated organizations.
• Explainability — Ability to interpret and justify AI outputs.

• FedRAMP — Federal authorization program for cloud security compliance.
• Feasibility Tier — Classification of AI deployability (Tier 1 DoD-Ready; Tier 2 DoD-Possible; Tier 3 Informational).
• Facility Lifecycle — Planning, design, construction, operations, and sustainment phases.

• Generative AI — AI capable of producing designs, layouts, text, or alternatives.
• Generative Design — Algorithm-driven creation of optimized design options.

• Handover Data — Information transferred from construction to operations.
• Hybrid Deployment — Combination of cloud and on-premise computing.

• IFC (Industry Foundation Classes) — Open BIM data exchange standard.
• Interoperability — Ability of systems to exchange and use shared data.
• Integration Risk — Potential workflow disruption due to poor system compatibility.

• Justification Narrative — Documented reasoning supporting evaluation scores or recommendations.

• Knowledge Model — Structured representation of rules or expertise within software.

• Lifecycle Phase — Planning, Design, Construction, Operations.
• Large Language Model (LLM) — AI model trained on large text datasets for language tasks.

• Machine Learning (ML) — Algorithms that learn patterns from data.
• Maturity — Degree of proven deployment and vendor stability.
• Model-Based Coordination — Use of BIM to coordinate disciplines.

• Natural Language Processing (NLP) — AI enabling analysis and generation of human language.
• Network Segmentation — Isolation of systems for cybersecurity control.

• On-Premises Deployment — Software hosted within local secure infrastructure.
• Operations & Maintenance (O&M) — Facility sustainment activities post-construction.

• Predictive Analytics — AI forecasting outcomes using historical data.
• PHI (Protected Health Information) — Sensitive health-related data requiring protection.
• Pilot Program — Controlled deployment to evaluate tool performance.

QA/QC (Quality Assurance / Quality Control) – Processes ensuring compliance and accuracy.

• Risk Mitigation — Actions reducing likelihood or impact of adverse events.
• ROI (Return on Investment) — Measurable benefit relative to cost.

• Scoring Matrix — Structured evaluation table assigning weighted scores.
• Soft Threshold Flag — Non-disqualifying risk indicator in evaluation framework.
• Standards Relevance — Degree of impact on UFC criteria or workflows.

• Tier 1 (DoD-Ready) — Near-term feasible within DoD constraints.
• Tier 2 (DoD-Possible) — Feasible with policy or IT adjustments.
• Tier 3 (Informational) — Not currently deployable.
• Traceability — Ability to track decisions and data lineage.

• UFC (Unified Facilities Criteria) — DoD design standards governing military facilities.
• UFC Touchpoint — Section or workflow within UFC potentially influenced by AI.

• Validation — Confirmation that system outputs meet intended requirements.
• Vendor Stability — Financial and operational reliability of software provider.

• Workflow Integration — Alignment of tool functionality with existing project processes.
• Weighted Scoring — Assignment of percentage-based importance to evaluation criteria.

• XML (eXtensible Markup Language) — Structured data format sometimes used in BIM exchanges.

• Yield Optimization — Improvement of performance or efficiency through data-driven methods.

• Zero Trust Architecture — Cybersecurity model requiring continuous verification of users and devices.