GLG positions reimagined myGLG as agent-led research workspace

GLG used its corporate LinkedIn feed to introduce a reimagined version of myGLG, the client-facing platform through which institutional researchers source experts, schedule calls and pull survey data. The post frames the redesign around a longstanding tension in primary research: teams either move fast at scale, or go deep on nuance, but rarely both. GLG's argument is that the new myGLG closes that gap with an embedded AI agent that scopes projects, suggests angles, recommends experts and gathers data across qualitative and quantitative modalities.

The firm describes the workspace as built on decades of methodology and proprietary data. It stops short, at least in the LinkedIn copy, of naming the underlying model layer, the scope of the agent's autonomy, or which client workflows it has been piloted against.

The announcement lands in a year when every major expert network has shipped some version of an AI-layered research product. AlphaSense's Generative Search and recent Tegus-powered transcript surface, Third Bridge's Forum AI summaries, and Guidepoint's Compass-style enhancements have all pushed the category toward conversational interfaces over expert databases. GLG's contribution, as described in the post, is broader in stated scope: the agent is positioned not as a search overlay but as a research-design partner that sits at the front of the workflow.

That distinction matters. Search and summarization tools accelerate what an analyst already knows to ask. A scoping agent, if it works as described, intervenes earlier, in the part of the workflow where a research associate decides which experts to call, which angles to test and which data to commission. That is closer to the moderator's job than to the librarian's.

The LinkedIn post is, by format, a marketing artifact rather than a product disclosure. The substantive questions, model stack, pilot client cohort, retention impact, compliance architecture, are absent. That is normal for the channel. What the post does establish is positioning: GLG is no longer presenting itself primarily as the world's largest network of vetted experts. It is presenting itself as an AI-mediated research workspace that happens to be backed by the world's largest network of vetted experts.

For research teams evaluating expert networks in 2026, the practical question is whether the agent meaningfully shortens the gap between question and shortlist. The methodology and proprietary data GLG cites are real moats, built over two decades. Whether the agent layer translates that moat into measurably faster analyst output, or whether it sits on top as a polished interface to the same underlying process, is the test that will play out across client renewals over the next two to four quarters.

What to watch next: a named pilot client, a published benchmark on time-to-shortlist, and any disclosure of how the agent handles compliance pre-screening. Until then, the reposition is a stake in the ground, not a product review.