LLM-referred visitors convert to sign-ups at 1.66%, more than eleven times the 0.15% rate recorded for traditional search traffic, yet most analytics platforms are misclassifying this channel entirely, according to new analysis from Rankability, an SEO and AI visibility software company serving digital agencies.

ST. LOUIS, June 24, 2026 /PRNewswire/ — Rankability, founded in 2024 and based in St. Louis, Missouri, published the analysis in June 2026, drawing on Microsoft Clarity’s 2025 study of more than 1,200 publisher and news websites and Adobe Digital Insights’ 2025 generative AI referral traffic report. The findings document a performance gap between AI-referred visitors and every traditional acquisition channel, and a measurement failure that is systematically hiding that gap from agencies and their clients.

The conversion advantage is not the only quality signal in the data. According to Adobe Digital Insights’ 2025 report, AI-referred visitors spend 41% more time on site and bounce 23% less than non-AI traffic. Meanwhile, Microsoft Clarity’s data shows AI-driven platform traffic grew 155.6% over eight months, and Adobe Analytics found that traffic to U.S. retail websites from generative AI sources grew 1,200% between July 2024 and February 2025, the steepest acceleration of any acquisition channel Adobe tracked over that window.

The channel is small in volume, representing less than 1% of overall sessions in the publisher set studied, but its per-session quality profile is already measurable and its growth rate is compressing the volume gap faster than most planning cycles anticipate.

The reason agencies aren’t seeing any of this in their dashboards comes down to how LLM referrals are processed. When a user follows a link surfaced by ChatGPT, Perplexity, or another large language model, the referrer string passed to the destination site is often absent, malformed, or unrecognized by standard analytics tools.

Google Analytics and most tag-based platforms default to classifying unrecognized referrers as “direct” traffic, placing an AI-sourced session in the same bucket as a user who typed a URL directly into the browser. A client’s monthly report may show flat organic search performance while a higher-converting traffic source accumulates undetected inside the direct channel, and budget decisions get made on data that excludes the channel showing the best return.

Separating AI traffic from direct requires either analytics configurations or purpose-built AI visibility tools capable of recognizing referrer strings from known LLM platforms and routing those sessions into a dedicated segment. Where referrer data is absent entirely, UTM parameters on linked content can provide a partial signal.

Once isolated, AI-referred traffic can be evaluated on the same ROI framework applied to any other channel: sessions attributed, conversion rate, and downstream revenue or lead value. On a per-session basis, Microsoft Clarity’s data indicates those returns would justify significant channel investment, if the sessions were being counted at all.

The content strategy implications follow the same logic. LLMs surface content that answers specific queries with precision and authority, meaning sites that appear in AI-generated responses tend to publish structured, well-attributed, topically deep material, a set of characteristics that differs in emphasis from traditional search optimization.

Agencies that begin instrumenting AI traffic now will build the feedback loop needed to understand which content earns LLM citations. Those that don’t will continue optimizing for a channel that Gartner’s 2024 forecast models as declining 25% in volume by 2026 due to AI chatbots and virtual agents, while their strongest-performing acquisition channel remains invisible in reporting.

Rankability’s full analysis is available at rankability.com.

Media Contact
Source: Rankability
Contact: Nathan Gotch
Email: nathan@rankability.com
Location: St. Louis, Missouri

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SOURCE Rankability

NEW YORK, June 24, 2026 /PRNewswire/ — The Rockefeller University today announced that Jun Yang has been named vice president and chief investment officer (CIO) of the University. Yang, who comes to Rockefeller from Oberlin College, will assume stewardship of the biomedical university’s $3.0 billion endowment starting July 1, 2026.

Yang succeeds current vice president and CIO Paula Volent who is stepping back from her full-time role and will continue to serve the University on a part-time basis as senior advisor. During her term as CIO, Volent guided the university through unprecedented challenges while building a highly talented team and realizing strong returns while maintaining the financial flexibility needed to support our scientific mission. Drawing on decades of experience and relationships across the investment world, she also opened the door to a number of highly sought-after investment opportunities that will benefit Rockefeller for years to come.

Yang comes to Rockefeller with more than 20 years of experience, effectively leading institutional investment programs and managing multi-billion-dollar endowments at Oberlin and previously at the Weizmann Trust which manages the investments and philanthropic assets for the Weizmann Institute of Science.

“Jun is an exceptional investment leader whose experience, judgment, and track record make him uniquely suited to guide Rockefeller’s endowment into its next chapter,” said Richard P. Lifton, Rockefeller’s president. “He inherits an extraordinarily strong foundation built by Paula, whose outstanding leadership has helped position Rockefeller to thrive amid a rapidly changing landscape for biomedical research. At a moment when the funding environment for science faces growing uncertainty, the stewardship of our endowment has never been more important. We are grateful for Paula’s years of outstanding service, and are delighted to welcome Jun to the university, providing a smooth transition and continued strength in this vital role.”

Yang earned his MBA from Cornell University, a Master of Engineering from Stevens Institute of Technology, and his bachelor’s in engineering from Shanghai Jiao Tong University.

“Rockefeller occupies a unique place in the scientific world, and I am honored by the opportunity to join an institution with such an extraordinary history of discovery and impact,” said Yang. “I am excited to help steward the university’s endowment in service of Rockefeller’s mission, and I am fortunate to inherit a high-performing investment program built by Paula Volent, whose leadership has positioned the endowment for continued success. I look forward to working closely with Paula and with Rockefeller’s trustees, leadership, faculty, and staff to build on that foundation and ensure the university has the resources and financial strength to pursue groundbreaking science for generations to come.”

“Paula was already widely regarded as one of the nation’s premier chief investment officers when she joined Rockefeller, and over the past five years she has delivered outstanding results for the university,” said Bill Ford, chair of Rockefeller’s Board of Trustees. “Her leadership strengthened Rockefeller’s financial foundation at a time when support for scientific research is more important than ever, and I am grateful that she has agreed to remain involved as Senior Advisor to ensure a seamless transition. At the same time, I am equally excited to welcome Jun Yang, whose distinguished record of endowment stewardship and long-term investment success makes him an outstanding choice to lead Rockefeller’s Investment Office into the future.”

About The Rockefeller University
The Rockefeller University is the world’s leading biomedical research university and is dedicated to conducting innovative, high-quality research to improve the understanding of life for the benefit of humanity. The university’s unique approach to science has led to some of the world’s most revolutionary and transformative contributions to biology and medicine. During Rockefeller’s 125-year history, our scientists have won 26 Nobel Prizes, 26 Albert Lasker Medical Research Awards, and 20 National Medals of Science.

Media contact:
Katherine Fenz
kfenz@rockefeller.edu

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SOURCE The Rockefeller University

TORRINGTON, Conn., June 24, 2026 /PRNewswire/ — Therap Services, the national leader in HIPAA-compliant electronic documentation solutions for organizations in Long-Term Services and Supports (LTSS), Home and Community-Based Services (HCBS), and other human services industries, has launched enhanced AI Analysis tools. The release introduces two powerful capabilities: Health Care Analysis and Person Centered Discovery Analysis, designed to transform complex agency data into actionable insights.

Actionable Insights from Complex Data

Synthesizing months of documentation can overwhelm care teams. Fully integrated within the Therap platform, Therap’s new AI Analysis Tool allows providers to easily run high-level or deep-dive analyses across Case Notes, Daily Notes, and ISP Data. This tailored approach significantly reduces administrative burnout, streamlines reporting, and allows providers to keep their focus where it belongs: on the individuals they support.

Key Enhancements and New Features 

Person Centered Discovery Analysis: Aggregates up to three years of data from Personal Focus Worksheets and Charting the LifeCourse (CtLC) forms—including Life Trajectory and Integrated Supports Star forms—to better track individual goals and personal growth.Health Care Analysis: Evaluates up to three years of clinical history from Health Care Reports to track medical trends and improve care outcomes.Role-Based Security: Administrators can assign specific “Skills” to distinct staff roles via the redesigned Therap AI Skill Assignment page, ensuring secure and tailored access.

To maintain data integrity, the system alerts users if source records are updated after an analysis is run. 

Frequently Asked Questions

How do these tools benefit providers? Therap’s AI Analysis tool automates the review of massive datasets, freeing up staff to focus on direct individual care.What documents can be analyzed by Therap’s AI Analysis tool? Health Care Reports, T-Logs, Case Notes, ISP Data, Personal Focus Worksheets, and Charting the LifeCourse forms.How are user permissions managed? System admins control access by assigning specific AI skills to staff based on their roles.What happens if a source document is edited after an analysis is generated? A warning message will appear at the top of the form stating: “The Data used to generate this Analysis has changed. Please consider re-generating the Analysis.”

For more information about Therap’s AI solutions, please visit:
AI Tools Designed for Human Service Providers 

About Therap Services

Therap’s comprehensive and HIPAA-compliant software is used in human services settings for documentation, communication, reporting, EVV, and billing.

Learn more at:
https://www.therapservices.net/

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SOURCE Therap Services