Artificial
Intelligence (AI)

Real-World Impact, Accelerated by Responsible AI

Organizations are under increasing pressure to understand where AI can create measurable value, how to apply it responsibly, and how to modernize operations without compromising trust, security, or rigor. RTI leverages AI—combined with domain expertise, human oversight, and evidence-based standards—to accelerate discovery, strengthen decision-making, and drive meaningful impact.

Related Work

Impact

Accelerating AI-Enabled Discovery to Deliver Real-World Impact

Through our TechWerx partnership intermediary, RTI administers and operates the U.S. Department of Energy Genesis Mission Consortium, an effort to reimagine how science is conducted—leveraging AI to accelerate discovery and address complex national challenges.

Insights

RTI SmartReview: Using Data Science to Improve Federal Public Comment Review

To help federal agencies manage the influx of public comments that comes with rulemaking, we created RTI SmartReview. This system uses an automated data pipeline to ingest public comments, identifying the most relevant and speeding up the process of review.

Insights

4 Ways AI is Enhancing Survey Research in 2026

Our survey science experts share the key ways in which AI can help clients achieve quality results despite rising data collection costs, declining budgets, and falling participation rates.

Impact

Using Human-in-the-Loop AI to Accelerate Economic Mobility Research

We leveraged AI-powered screening and data extraction with expert oversight to synthesize multi-sectorial research, creating an evidence-based framework for understanding the factors that influence economic mobility. This framework can help policymakers and program leaders make informed decisions that support economic opportunities.

Impact

Integrated Dynamic Risk Assessment for Community Supervision (IDRACS)

We built an improved tool that helps probation and parole officers predict an individual's risk of recidivism. Using advanced machine learning alongside traditional statistical methods, we designed a system that accounts for changes in risk status over time.