What Is Knowledge Brokering in the Age of AI?

Knowledge brokers are the individuals and organisations that connect research capability with practical decision-making environments, enabling knowledge to move across institutional boundaries.

Research is a foundational pillar of modern society. It converts knowledge into capability, informs public policy, drives technological development and shapes institutional decision-making across every sector. The scale of that contribution is significant. So is the gap between what research produces and how effectively it reaches the decisions it should inform.

That gap is not incidental. The relationship between knowledge production and knowledge application is structurally complex by nature.

Scientists, medical researchers, engineers, legal professionals, policymakers and industry practitioners operate within different institutional environments, shaped by different incentives, timelines and measures of success. Academic research often prioritises disciplinary contribution, methodological rigour and publication. Governments and industry operate within operational, political and commercial constraints that demand timely and applicable judgement. Even when both groups are attempting to address the same problem, they frequently lack the mechanisms required to effectively connect expertise with need.

This gap between knowledge production and knowledge application is well documented across public administration and policy research. Knowledge brokering emerged in response to that structural divide. Knowledge brokers are the individuals and organisations that connect research capability with practical decision-making environments, enabling knowledge to move across institutional boundaries.

Knowledge Brokering in Principle

Knowledge brokers act as mediators between knowledge producers and knowledge users, helping research move into environments where it can inform policy, practice and decision-making. They perform the more difficult task of translation between institutional communities that often operate with different assumptions, vocabularies and forms of authority.

Knowledge brokers leverage their unique ability to understand the contexts and needs of both research and practice. The ability to "speak the languages of multiple parties" grants them the power to act "as a negotiator and translator...working to create equivalence in understandings" (Williamson and Leat, 2021, p. 6). This intermediary role matters because evidence rarely moves directly from publication into practice. Research must be interpreted, contextualised and connected to operational realities before it becomes usable within decision-making environments.

For that reason, effective knowledge brokering depends as much on institutional understanding and relationship-building as it does on technical expertise. Brokers require the ability to navigate sectoral norms, establish trust across organisational boundaries and understand how different communities evaluate evidence and legitimacy. As trusted intermediaries, knowledge brokers can enable the reflexivity and innovative thinking necessary to promote new mindsets and approaches to evidence production and use (Isett and Hicks, 2020).

Knowledge Brokering Models

Knowledge Translation: Knowledge brokers act as mediators between knowledge producers and knowledge users and address the intellectual divide by "translating research and other evidence into different vocabularies" (Ward et al., 2009, p. 269). They bridge communities that operate with different assumptions, vocabularies and forms of authority.

Capability and Impact: This model focuses on strengthening the ability of institutions and practitioners to engage with research effectively, improving how evidence is interpreted, applied and integrated into decision-making environments.

Structural Matching and Collaboration: Rather than treating researchers as producers and practitioners as recipients, this model focuses on developing relationships and opportunities for collaboration (Orr and Bennett, 2012). Knowledge is not merely transferred between communities. It is jointly produced through interaction.

The effectiveness of knowledge brokering depends heavily on the surrounding evidence ecosystem. Research capability alone is insufficient. Systems also require the institutional infrastructure that allows expertise, evidence and relationships to move across organisational boundaries.

Structural Limitations and a Changing Landscape

Research and innovation ecosystems across Australia and New Zealand are undergoing significant policy reform. As governments grapple with fiscal pressure, complex policy challenges and the growing demand to translate research into impact, the structural conditions that enable knowledge exchange become critical.

Yet in practice, knowledge exchange still depends heavily on personal networks rather than system-wide visibility. Expertise remains fragmented across groups and institutions, making it difficult for organisations, governments and external partners to identify the full landscape of capability available to them.

This is not simply a communication problem. It is an infrastructure problem.

Across all forms of brokering, one condition remains foundational: expertise must be discoverable before it can be connected. Collaboration cannot occur around capability that institutions cannot identify, and capability gaps cannot be addressed without visibility into where expertise already exists.

Knowledge Brokering in the Age of AI

The growing adoption of large language models introduces a new complexity into research systems. AI creates the impression that access to knowledge has been solved because information retrieval is now immediate, conversational and available at scale. Yet information access and knowledge infrastructure are not the same thing.

Information without provenance, context and identifiable expertise does not automatically become reliable knowledge. As informational abundance expands, the ability to distinguish trusted expertise from informational noise becomes increasingly important.

Large language models can generate lists of researchers, publications and areas of expertise within seconds. What they cannot reliably provide is the institutional context that gives knowledge practical meaning: how expertise is connected across systems, how research has influenced practice, or how capability moves between institutions and sectors.

This distinction between information and knowledge is becoming strategically significant for research systems. Effective knowledge exchange depends not only on access to information, but on infrastructure capable of making expertise discoverable, interpretable and institutionally legible across organisational boundaries.

AcademicFellows operates within this structural gap. It is built on the recognition that research systems already contain substantial intellectual capability, but that capability often remains difficult to identify, connect and mobilise across institutions and sectors. Its objective is not simply visibility, but the creation of stronger pathways between researchers, governments, industry and organisations seeking specialised expertise.