RFI for Artificial Intelligence Assisted Productivity v2.docx

REQUEST FOR INFORMATION

Notice Type: Request for Information

1. DISCLAIMER

This is a Request for Information issued solely for information and planning purposes. It does not constitute a Request for Proposal (RFP) or a promise to issue a RFP in the future. This notice does not commit the Government to contract for any supply or service. Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract. No reimbursement will be made for any costs associated with responding to this notice.

2. PURPOSE

The Air Force Research Laboratory (AFRL) is soliciting information from qualified vendors regarding AI-powered solutions to:

a) optimize HR workforce development,

b) streamline enterprise workflow tasking and task responses,

c) facilitate user-friendly program management enabling enterprise integrated portfolio management, and

d) augment science and technology literature searches and gap analysis.

AFRL seeks advanced platforms, software, or services that deliver improvements in ease of use, efficiency, accuracy, and strategic decision-making across these domains.

3. BACKGROUND

AFRL plans to implement technology solutions that address the following challenges:

· Data Silos & Variety: Disparate data sources across AFRL (directorates, programs, projects, functional areas, legacy systems) exist in silos, using diverse formats (structured/unstructured), making unified access and integration difficult.

· Manual Data Handling: Current data ingestion and transformation processes are often manual, time-consuming, and prone to errors, hindering timely data availability.

· Complexity of Data: The data handled by AFRL is often complex, requiring specialized knowledge for accurate interpretation and analysis. The data is a combination of structured and unstructured data across multiple different formats and systems.

· Visualization Bottleneck: Traditional visualization tools struggle to effectively represent and explore the complex, multi-dimensional data, limiting insight generation.

· Limited AI Integration: Lack of automated AI-driven tools for data discovery, transformation, and analysis slows down the process of extracting valuable insights from the data fabric.

· Security Requirements: Stringent security protocols and access controls add complexity to data sharing and collaboration within the data fabric. This is required at all classification levels.

These issues collectively impede AFRL's ability to rapidly leverage its data assets for streamlining business and decision making, accelerating research and development, and enhancing workforce support.

4. REQUIREMENTS DESCRIPTION

AFRL seeks advanced platforms, software, or services that deliver improvements in ease of use, efficiency, accuracy, and strategic decision-making across four domains. Please provide details regarding AI-assisted productivity solutions that address the following domains a) HR workforce development, b) enterprise workflow tasking and task responses, c) user-friendly program management enabling enterprise integrated portfolio management, and d) augmented science and technology literature searches and gap analysis. The domains are not listed in priority order. Solutions may address one or more of the domains. For each, indicate which domain your solution supports and plan for each component to be severable. Listed below are ideas for what solutions may include, additional ideas are encouraged to be included.

a. HR Workforce Development

· AI-driven chatbot to provide personalized career advice and integrate with the AFRL systems. Tools for skills gap analysis and comprehensive career management, facilitating seamless user experiences and strategic career development.

· AI-driven Chatbot/Virtual Career Mentor: Search for relevant information providing personalized recommendations for career development. Function as a virtual career mentor, offering tailored advice and resources based on individual profiles and career trajectories.

· Skills Gap Analysis and Training Recommendations: Conduct skills gap analysis, identifying individual and organizational training needs. Provide personalized training recommendations based on the identified gaps and career development goals, facilitated through an AI-powered system. Analyze strategic planning documents, unit-level job roles and requirements, and mission statements to determine current and future skill and knowledge needs. Compare skill needs and gaps against university programs and specialties to create tailored recruiting messages, identify key recruiting locations, and target universities with relevant skill sets.

· Comprehensive Career Management: Assist employees in managing their career progression, offering insights into potential career paths, required skills, and training opportunities. User-friendly interface offering a seamless experience from development planning to execution and tracking of progress.

b. Tasking and Workflow Automation With Assisted Response Generation

· Automated task assignment, reminders, and progress tracking. Identify any blockers or delays with audit trail. Seamless access to available data to provide draft responses. Catalog and archive approved responses based on DAF records management policy.

· Workflow & Document Automation: Automate data entry, approvals, file organization, report generation, and document processing across organization to reduce manual workload and error rates.

· Customer Service Management: Chatbots and routing systems to handle responses, triaging, and escalation pathways; track follow-ups and log outcomes for continuous improvement.

· Leverage Data to Draft Responses: Mine existing data across multiple structured and unstructured data sources to streamline access to data. Summarize data and propose responses to natural language tasking questions.

c. AFRL Portfolio & Program Insights

· User-centric multimodal data capture facilitating efficient and effective program management. Data capture into a data fabric that enables both tactical program management (milestone tracking, risk analysis, automated reporting, etc.) and strategic enterprise level portfolio management (enterprise assessment, predictive analytics, assessment of technical and financial shifts, etc.).

· Integrate & Streamline User Friendly Program Management Tool(s): Automate repetitive tasks, optimize workflow, and enable easier status updates. Early detection/prediction of risks by analyzing patterns in project data, helping teams mitigate issues before they escalate. Data tagging thru AI-assisted data analysis. Optimize resource allocation and provide visibility into team workload. Easy collaboration thru automated meeting summaries, actionable tasks, and automatic document generation for program reviews.

· Strategic Portfolio Management Thru Advanced Analytics: Uncover hidden patterns, correlations, and trends within the data that are not readily apparent through traditional reporting methods. This may include, but is not limited to, predictive analytics, anomaly detection, and causal inference. Enable enhanced decision making, strategic alignment and collaboration, as well as dynamic portfolio optimization.

· Interactive Dashboards & Reports: Intuitive, customizable dashboards and reports that visualize key findings and facilitate data-driven decision-making. Enable drill-down analysis and ad-hoc querying.

d. Literature and Document Search for Scientists and Engineers

· Enhanced discoverability, reducing barriers and fragmentation of information facilitating cutting edge research, discovery, and intellectual collaboration. Semantic document search, tagging, classification, and gap analysis. Natural language translation from open source as well as subscribed library content via APIs to access electronic indices to enable access to literature for a full horizon scan of past and current research, trend analysis and advanced analytics. Enhanced collaboration tools to identify potential allied and adversarial areas of focus.

· Smart Search: Ability to connect (ex: via CAC-enabled/IP authentication) with multiple repositories both internal and external (ex: open source, institutional databases, library licensed subscriptions, contract information, etc.), supporting structured and unstructured content to search, retrieve, tag, translate, and summarize relevant information. Integrate with existing data sources, databases (e.g., SQL, NoSQL, data lakes) and data formats.

· Gap and Trend Analysis: Identify gaps, uncover hidden patterns, correlations, and trends within the data that are not readily apparent through traditional literature searches. This may include, but is not limited to, predictive analytics, anomaly detection, and causal inference.

· Collaboration & Networking Insights: Identify potential collaborators based on research domains, co-authorship, institutional affiliations and topic similarity, etc.

NOTES: Vendors should describe their solutions considering the following:

Data Capture, Entry & Retrieval:

· User Experience: Enhancing user experience with easy data entry, limiting multiple manual and repetitive data entry requirements.

· Connectivity: Connect to diverse data sources (APIs, databases, logs, etc.).

· Extraction & Transformation: Automatically extract and transform structured/unstructured data into the standardized data fabric schema using NLP, pattern recognition, etc.

· Retrieval: Ability to search, collect, and retrieve relevant information from internal and external sources. Ability to effectively manage data and records.

· Data Quality: Ensure data accuracy, completeness, and consistency through validation and cleansing.

· Automation: Minimize manual data entry efforts.

· Feedback Loop: Incorporate user feedback to improve extraction and transformation accuracy.

Visualization & Analysis:

· Intelligent Visualization: Generate relevant and insightful visualizations based on data patterns and user needs.

· Data Summary: Automated summarization of retrieved data (e.g., highlighting key points, summarizing documents, extracting essential information).

· Natural Language Querying: Enable users to query the data fabric using natural language.

· Automated Data Analysis: Perform automated data analysis to interpret data, deliver insights, detect anomalies, generate reports, and identify trends.

· Advanced Data Analysis: Advanced AI for predictive modeling, automated decision support, scenario analysis, and logic-based recommendations.

· Personalized Insights: Provide personalized insights and recommendations based on user roles and data usage patterns.

· Contextual Awareness: Understand the context of the data to provide more meaningful visualizations and analysis.

General Requirements (All Areas):

· Scalability: Handle large volumes of data.

· Security: Secure data access and protect sensitive information across all classification levels. Maintain the security and integrity of sensitive data, adhering to all relevant security protocols and compliance requirements (specify any relevant regulations, e.g., FedRAMP, NIST).

· Explainability: Provide explanations for AI-driven insights and recommendations.

· Integration: Seamlessly integrate with existing data fabric components. Enterprise system integration (document management, collaboration platforms).

· Implementation and Support: Deployment and onboarding timelines. Training and support plans.

Success Criteria:

· Increased Efficiency & Effectiveness: Enhanced efficiency in data ingestion and analysis.

· Improved Data Quality: Better data quality and insights. Enhanced ability to identify and respond to emerging trends and opportunities.

· User Experience: Easy to use and requires minimal training for both data analysts and senior leaders. Reduction in manual data entry and analysis efforts.

· Enhanced Decision-Making: Improved decision-making based on data-driven insights at all levels of AFRL leadership.

5. CONTRACTOR CAPABILITY SURVEY

Interested businesses are invited to respond by providing the following:

Part I – Business Information:

· Company Name:

· Address:

· CAGE Code and UEI:

· Point of Contact (Name, Email, Phone):

· Website:

· Business Size and Socioeconomic Status (Small Business / Woman-Owned / Veteran-Owned / 8(a) / HUBZone / etc.)

Part II – Capabilities and Experience:

To effectively represent their ability to provide solutions, the vendor should describe their past successes in their RFI response:

· Demonstrated experience with organizations of similar scope.

· Case studies or documented impact in HR, program management applications, tasking processes, and/or scientific literature analysis.

· Data Capture, Entry & Retrieval

· Visualization & Advanced Analysis

· Scalability, Integration, & Security Practices

· Solutions Developed at All Classification Levels

· Impact of the solutions

By providing detailed descriptions and examples of these capabilities and experiences, the vendor will effectively showcase their ability to develop solutions, achieving the desired goals specified in the RFI.

6. RESPONSE INSTRUCTIONS

Responses must be submitted electronically to joshua.sawdey.1@us.af.mil no later than 3 October 2025. Responses should not exceed 20 pages, excluding title pages, table of contents or appendices. The Government anticipates no more than 5 pages per domain.

If available and applicable, please include any standard commercial price catalog or similar documentation.

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