Beyond National Experimentation: Inter-Parliamentary Diplomacy and the Dissemination of Artificial Intelligence in Parliaments

Written in March, 2026

Parliaments across jurisdictions are exploring the potential of artificial intelligence to enhance transparency, improve internal workflows, expand citizen accessibility, and strengthen oversight functions. Yet the implementation of artificial intelligence in parliaments does not unfold in isolation. It is embedded within pre-existing institutional architectures, asymmetries in digital maturity, and longstanding traditions of inter-parliamentary diplomacy. As a result, the diffusion of artificial intelligence in parliaments is not simply a technological trajectory, it is a cooperative one.

A high-level panel discussion held on 6 November 2025, convened by Bússola Tech and moderated by Luís Kimaid, brought together Eric Janse, the Clerk of the House of Commons of Canada, Grant Vergottini, CEO of Xcential Legislative Technologies, José Pedro Montero, President of the Association of Secretaries-General of Parliaments, Julius Kampamba, Lead of the Technical Team of the Society of Clerks-at-the-Table (Africa), and Miguel Landeros, Secretary-General of the Cámara de Diputadas y Diputados de Chile. The exchange focused on the instruments through which inter-parliamentary cooperation can advance the implementation and understanding of artificial intelligence in legislative institutions.

Building on that discussion, this article advances an analytical account of inter-parliamentary cooperation as an emerging infrastructure for artificial intelligence governance in parliaments. It first identifies the typologies of cooperation through which knowledge and practice circulate across jurisdictions. It then examines the institutional preconditions that determine whether such cooperation translates into meaningful implementation. Finally, it considers how the impact of cooperative initiatives may be assessed in the absence of fully developed evaluative frameworks. Through this analysis, the article situates artificial intelligence not as a stand-alone innovation but as a catalyst operating within a layered architecture of diplomatic engagement, administrative capacity, and institutional design.

Typologies of Inter-Parliamentary Cooperation on Artificial Intelligence

Inter-parliamentary cooperation on artificial intelligence emerges through distinct but interconnected modalities that operate at multilateral, bilateral, technical and professional levels. These modalities differ in structure, duration, and depth of engagement, yet collectively constitute the operative architecture through which knowledge on artificial intelligence circulates across legislative institutions.

One prominent modality is cooperation through formal multilateral associations and inter-parliamentary bodies. Organisations such as the Society of Clerks-at-the-Table, the Association of Secretaries-General of Parliaments, the Commonwealth Parliamentary Association, the Organization of American States, ParlAmericas, the Inter-Parliamentary Union and various bilateral parliamentary associations provide structured forums in which artificial intelligence is introduced as a subject of deliberation. Within these platforms, artificial intelligence has been incorporated into general debates, thematic sessions, professional development seminars, and annual meetings. In these settings, senior parliamentary administrators and officials present communications detailing how their respective parliaments are approaching digital transformation and artificial intelligence. The function of these forums is not technological development per se, but the exchange of institutional experience, the articulation of practical challenges, and the collective reflection on governance implications. Through repeated meetings over several cycles, artificial intelligence shifts from a peripheral innovation topic to a central administrative concern embedded within the routine agenda of parliamentary diplomacy.

A second typology is bilateral technical cooperation based on direct institutional engagement. This form of cooperation involves one parliament supporting another in the development of digitalisation processes, data structuring, or artificial intelligence applications, taking into account differences in technical capacity and institutional maturity. Such cooperation may include the transfer of technological solutions, adaptation of tools originally developed in one jurisdiction for use in another, and iterative processes whereby the receiving parliament modifies or improves upon the shared initiative. The exchange is not unidirectional; technology and experience circulate in multiple directions, particularly where development capacities differ. In this typology, cooperation is framed not merely as dissemination but as co-development, in which innovation in one parliament becomes a resource for others, and improvements made by the recipient may subsequently be reintegrated into the originating institution’s practice.

A third typology consists of professional development mechanisms and structured learning exchanges. Annual seminars, sub-regional workshops, and exchange visits are designed to facilitate both awareness and practical engagement. These mechanisms include the organisation of thematic workshops on artificial intelligence, the cascading of topics from regional to sub-regional levels, and the inclusion of both technical staff and, in some instances, members of parliament. The objective is not confined to conceptual sensitisation; it extends to enabling technical personnel to observe and engage directly with operational systems in more advanced institutions. Learning is framed as extending beyond successful practices to include challenges and implementation difficulties, thereby allowing other parliaments to navigate adoption processes with greater institutional awareness. Within this typology, reporting mechanisms provide a degree of continuity and follow-up, even where formalised metrics remain limited.

A fourth typology concerns the development and dissemination of standards and shared infrastructures. Cooperation in this domain is oriented towards the creation of common data standards and structured formats for legislative information. Rather than producing isolated, jurisdiction-specific solutions, the emphasis lies in establishing interoperable frameworks capable of supporting artificial intelligence applications. The development of standards in collaboration with academic institutions and supranational bodies reflects an understanding that artificial intelligence relies on structured, machine-readable information rather than paper-based or static document formats, such as Akoma Ntoso. The publication of specific frameworks, such as the Guidelines for AI in Parliaments developed under the leadership of Dr. Fotios Fitsilis, further illustrates how normative principles for ethical governance, transparency, and human oversight are being articulated at the inter-parliamentary level to support responsible implementation.

A fifth typology is the creation of shared repositories, case databases, and knowledge platforms. Databases collecting use cases, toolkits, and documented experiences transform isolated initiatives into collective parliamentary knowledge. Such repositories allow parliaments to identify peers experimenting with specific technologies and to initiate targeted exchanges. The inclusion of experiences that did not achieve intended outcomes is framed as equally valuable, permitting institutions to learn from implementation challenges. This form of cooperation converts episodic dialogue into cumulative institutional memory and facilitates sustained engagement beyond single conferences or events. Research initiatives dedicated to systematically documenting parliamentary artificial intelligence cases further reinforce this dynamic. For example, Bússola Tech’s repository of AI cases illustrates how dispersed experimentation can be consolidated into structured institutional knowledge rather than remaining anecdotal.

A sixth typology involves cooperative pilot projects and joint development initiatives. In this configuration, two or more parliaments formally commit to addressing a common artificial intelligence challenge through a shared team or coordinated experimentation. The logic governing this model is that parliaments are not in competitive relations, therefore, they may collaborate in testing tools, sharing code, and distributing technical expertise. Shared ownership of innovation reduces duplication and reinforces collective capacity. The objective is not simply to discuss artificial intelligence, but to embed cooperative experimentation within institutional workflows.

Across these typologies, cooperation is shaped by recognition of asymmetries in digital maturity. Consequently, cooperative modalities are adapted to varying levels of development, ranging from support for small-scale applications using existing large language models to more complex cloud-based implementations. The typology of cooperation thus incorporates differentiated pathways rather than uniform solutions.

Taken together, these modalities illustrate that inter-parliamentary cooperation around artificial intelligence operates simultaneously as diplomatic engagement, technical collaboration, professional socialisation, standards development, and shared experimentation. Each element addresses a distinct dimension of artificial intelligence adoption, thereby constituting a layered architecture of cooperation through which parliaments seek to navigate this transition.

Institutional Preconditions for Effective AI Cooperation

The cooperative modalities previously identified depend upon internal conditions within parliaments that determine whether artificial intelligence initiatives can be meaningfully exchanged, adapted, and sustained. Inter-parliamentary cooperation does not substitute for institutional readiness. Differences in digital maturity, organisational capacity, political prioritisation, and information architecture directly shape the scope and depth of collaborative engagement .

A primary condition concerns the state of digitisation and data availability. Legislative information is not uniformly digitised across jurisdictions and databases are not always complete or structured. In such environments, advanced artificial intelligence applications cannot simply be transferred from one parliament to another. Cooperation must instead be calibrated to the technical baseline of the receiving institution. Where resources, engineering capacity, and infrastructure are limited, smaller-scale tools using existing language models may constitute the only feasible entry point. By contrast, parliaments with stronger technical foundations may proceed directly to cloud-based implementations and more complex architectures. The feasibility of cooperation therefore rests upon a realistic appraisal of internal technological conditions.

Closely related to digitisation is the question of structured information. The transition from publishing documents in paper or PDF format to publishing structured data suitable for computational consumption represents a foundational shift. Artificial intelligence systems rely on consistency, standardisation, and interpretability. The development and adoption of shared standards, undertaken in collaboration with academic institutions, are central to enabling cross-jurisdictional interoperability. Without convergence in how legislative information is structured, the accuracy and reliability of artificial intelligence tools cannot be ensured across contexts. Cooperation in this domain thus requires not only technical exchange but agreement on underlying publication practices.

Institutional governance structures constitute a further precondition. The Association of Secretaries-General of Parliaments, for example, provides platforms in which administrators present communications on their respective approaches to digital transformation and artificial intelligence. These forums create continuity across meetings and embed artificial intelligence within routine administrative deliberation. Although formalised metrics may not always exist, these reporting structures function as mechanisms of follow-up and collective awareness. Where governance arrangements enable regular reporting and review, cooperative initiatives acquire durability, where they do not, engagement risks remaining episodic.

Political endorsement also emerges as decisive. Efforts to integrate artificial intelligence often require deliberate mobilisation of members of parliament and senior officials. In some instances, MPs have been included in exchange visits so that exposure to operational systems can generate internal support upon return. Institutional uptake therefore depends not solely on technical viability but on whether leadership recognises artificial intelligence as aligned with parliamentary objectives, including transparency, accessibility, and efficiency.

Human capital is an additional structural factor. Immersive exchanges, staff attachments, and joint teams presuppose the availability of personnel capable of engaging substantively with artificial intelligence systems questions. Practical learning involves observing not only technical deployment but also management decisions, procurement processes, regulatory considerations, and responses to implementation challenges. Such exchanges require parliaments to allocate time and expertise. Cooperation, in this sense, demands sustained investment in professional capacity rather than attendance at isolated events. Strategic documentation further conditions the effectiveness of cooperation. The development of staff guidance on artificial intelligence transforms dialogue into transferable instruments. Where such documentation is absent, cooperation lacks concrete reference points for adaptation.

Measuring Impact of AI Cooperation in Parliaments

Having identified the typologies of cooperation and the institutional conditions that enable inter-parliamentary cooperation initiatives for the promotion of artificial intelligence in parliaments, the question of impact becomes unavoidable. Its impact must be evaluated by the extent to which it produces durable institutional change within participating parliaments.

One dimension of impact concerns participation and continuity. The breadth of engagement across jurisdictions, political traditions, and administrative levels provides an initial indicator of relevance. Where cooperation is sustained across multiple meetings and bilateral follow-ups, artificial intelligence shifts from a peripheral topic to a recurring institutional concern. Continuity beyond a single conference cycle indicates that cooperation has moved from episodic discussion to structured engagement. The sustained recurrence of thematic events accompanied by published analytical outputs offers an additional qualitative indicator of consolidation. For example, the sequence of conferences, webinars, and research publications dedicated to artificial intelligence in parliaments organised by Bússola Tech exemplifies how iterative dialogue combined with structured documentation can deepen institutional learning over time.

A second dimension relates to practical localisation. Exchanges are not impactful merely because they transmit models, they are impactful when receiving institutions adapt tools to their own technological and political environments. The differentiation between jurisdictions with advanced infrastructure and those with limited digitisation underscores that impact cannot be uniform. In some cases, it will take the form of incremental applications using existing language models, while in others, it may involve cloud-based deployments or structured data reforms. The metric is not technological sophistication per se, but the alignment between cooperative input and local capacity.

A third dimension concerns reporting and follow-up. In some arrangements, representatives report on developments within their respective parliaments, providing a mechanism for tracking progress. While formal quantitative metrics remain limited, structured reporting to membership bodies functions as a qualitative monitoring instrument. Such mechanisms allow the cooperative network to observe whether artificial intelligence initiatives are expanding, stagnating, or encountering obstacles.

At the same time, limitations in measurement need to be recognised. There is no comprehensive parametric tool currently in place to assess the effectiveness of inter-parliamentary cooperation on disseminating artificial intelligence expertise for parliaments. Evaluation often relies on indicators such as continued interest, allocation of resources, and willingness to pursue follow-up projects. This absence of systematic metrics does not negate impact, it reveals an area requiring further institutional development.

Finally, impact must be considered in normative terms. Artificial intelligence is an instrument to enhance parliamentary openness and efficiency. Where cooperative initiatives contribute to improved access to legislative information, strengthened oversight capabilities, or more inclusive digital interfaces, they produce value beyond technical deployment. The ultimate measure of cooperation lies in whether parliamentary institutions are better equipped to exercise their work and fulfil their functions.

Conclusion

This article demonstrates that inter-parliamentary cooperation on artificial intelligence cannot be understood as a peripheral diplomatic activity or as an accessory to technological experimentation. It constitutes an emerging layer of institutional infrastructure through which parliaments collectively navigate a structural transformation in how legislative information is produced, organised, and interpreted.

Cooperation alone does not guarantee transformation. The analysis of institutional preconditions shows that artificial intelligence adoption remains contingent upon internal readiness. Digitisation, structured data, governance continuity, political endorsement, human capital, and strategic documentation form the substrate upon which cooperative exchange can produce tangible effects. Where these conditions are uneven, cooperation must assume a differentiated character, adapting its instruments to varying levels of digital maturity.

The question of impact further clarifies the structural character of cooperation. Impact is not reducible to the frequency of conferences or the symbolic inclusion of artificial intelligence on the agenda. It becomes visible when dialogue crystallises into internal sustained follow-up mechanisms. Yet the absence of systematic metrics reveals a paradox: while cooperation is increasingly central to artificial intelligence in parliaments, the evaluation of such cooperation initiatives remains under-institutionalised.

Artificial intelligence should not be treated as an autonomous driver of institutional change but as an instrument for enhancing openness, accessibility, and administrative effectiveness. Cooperation, in this sense is a collective effort to reduce asymmetries in capacity so that parliaments, irrespective of size or resources, may strengthen their functions. The non-competitive character of parliamentary institutions enables this model of shared advancement. In this respect, cooperation is the mechanism through which parliaments construct a shared pathway into the knowledge-intensive phase of legislative administration.

The views expressed in this article are derived from the analysis of the author and do not necessarily reflect the official policy or position of the represented institutions, nor should they be considered and should not be construed as an endorsement or recommendation of any kind. The information presented in this article is derived from multiple sources. We encourage readers to access official sources from the institution in question.