Artificial Intelligence – The Present and the Future of Association Management

7th June 2023

For Boardroom, Marco Baldoli, Senior association Manager, Kellen, one of the world's largest providers of management and services to associations, delves into the transformative power of Artificial Intelligence in the realm of association management.

The growing demand for association management technology and solutions is forcing an unprecedented shift, and AI is at the forefront of this revolution. AI-based tools are transforming the association community and the work of its professionals by providing an automated and optimized experience for members and enhancing the overall effectiveness of the associations.

While the topic is clearly too vast to be examined in a short article, we will try to briefly dive into how AI is already reshaping the work and the activities of associations in five key areas, we will provide a few examples of AI tools that are currently being used by associations professionals[1], and we will look at some possible trends for the future, taking a quick glance at some of the ethical issues that are arising.

1. Predicting Attendee Preferences and engaging with sponsors: One of the core activities of many associations is organizing events, which most of the time take up lots of resources to organize. Lately, AI-based event marketing tools are being used by some associations to predict attendee preferences by analysing data through machine learning algorithms. These tools analyze past attendance data, member surveys and feedback, and member demographic data to pinpoint trends and identify potential new members, sponsors, and exhibitors. Based on this analysis, the tools can personalize event experiences, optimize marketing campaigns, and make suggestions for future events and offerings. Examples: Seventh Sense; Semrush; Numerous; MarketMuse; Jasper AI

2. Analysing Member Feedback: Some association professionals are now using Natural Language Processing (NLP)[2] algorithms to analyse member feedback and generate actionable insights. These algorithms identify patterns and trends in member feedback, allowing associations to make more informed decisions. This technology also helps associations respond to members more efficiently and effectively, with personalized and relevant responses that address their needs and concerns. From simple actions like writing replies to emails to more complex tasks such as understanding technical and scientific content, AI is providing new possibilities to enhance the range of possible solutions to increase the association’s performance, if used correctly. Examples: ChatGPT; Bing; Bard; Anthropic;  (Google/Microsoft NLP based Chatbots)

3. Communication tools: AI-driven content curation tools are helping associations to create engaging newsletters, email campaigns, and social media posts. The tools use machine learning algorithms to analyse member engagement history and content preferences, then tailor content to meet member interests, potentially strengthening member relationships and increasing membership retention. Examples: Jasper; Writer; Notion

4. Designing Visually Appealing Marketing Materials: Many AI design tools are now employed to create visually appealing marketing materials, websites, and event collateral, maintaining a consistent brand image. This technology has made it easier for associations to create high-quality visuals without needing specialized design expertise. Examples: Midjourney; Adobe Firefly; Dall·e (for images). Wix; SITE123 (for websites). Gamma; Tome (Slide decks)

5. Automating Meeting Management: AI-powered transcription services and meeting management tools have enabled associations to automate the minutes-taking process, generating meeting summaries accurately in less time. The tools use natural language processing algorithms to transcribe and analyse meeting conversations, identify action items, and create summaries that are ready to be shared. This technology saves association staff time and reduces the risk of errors in meeting minutes. Examples: Microsoft pilot (to be released); Vowel

Trick or treat?

Most of the tools we described above belong to the “Generative AI” category. ChatGPT is at the forefront of this ‎craze. OpenAI introduced ChatGPT in November 2021 and since then the tool has been ‎used to perform some of the tasks described before: drafting emails, press releases, policy papers, and social media content ‎copy. ‎

However, ChatGPT as well as other more sophisticated tools still show their limitations and need to be used with extreme care and accuracy through prompting to avoid grossly incorrect answers and poor copy proposals. 

Some of the most famous tools, such as ChatGPT, have further limitations such as limited access to recent data and references, but other similar and more advanced tools do not face a similar issue (and soon also ChatGPT will regain access to up-to-date information on the internet).

Furthermore, several concerns are emerging regarding potential legal issues linked to the use of generative AI, from privacy breaches (e.g. ChatGPT)[3] to intellectual property infringements (e.g. Midjourney)[4], several violations have been reported already. This may become a serious problem when associations need to ensure legal compliance regarding confidentiality of information, GDPR, IP rights and competition law, just to mention a few examples.

A Human Touch

Despite the potential systemic threats, AI can be regarded as an opportunity to boost innovation within associations, something to work with rather than compete with. Surely, some of the tasks the association’s staff currently performs are likely to be fully replaced and automized through the use of AI in the near future. However, new tasks and functions will arise, possibly enhancing the role of live and personal interaction, as well as the crafting and use of innovative prompts, just to mention a few cases of what added value in association management may look like in the future. 

Furthermore, as AI still is and will remain a productivity tool, human control and validation over AI-generated content is key. It is therefore imperative to ensure “successful collaboration” between AI and the association’s staff. Association executives should look at establishing resources to ensure education amongst employees in associations on how to use AI and understand its potential as well as its boundaries.

Future Trends

It is important to acknowledge that most of the things that are being written in this article will be completely outdated in a few months’ time. New tools will appear, new functions will be unlocked and, therefore, new uses will appear. 

Associations may soon massively rely on AI to fully generate detailed regulatory impact assessments or to create immersive virtual experiences for their members (site visits, enhanced visual and informative videos for a communication campaign, chatbots that may start having defined identities and roles within the association’s staff). For those associations that have an interest in public governance one of the most interesting trends is the possibility that AI may play a role in lawmaking and lobbying. While in the US this is already happening (and it is raising some concerns!), in the EU we are just at the beginning of this development. 

Some firms are indeed using AI for regulatory analysis, while others offer services for better monitoring the slightest regulatory update in any area of interest, with systems that are becoming more and more sophisticated. Soon, we may have AI tools that are able, in principle, to entirely draft legislation if well prompted.  Or, even more easily so, to create amendments based on accurate prompts, which can embed the interest of the sectors represented as well as the overall lobbying strategy.[5]  Associations should regard any future advancement of AI with a positive attitude but a balanced approach.

A Game of Ethics

While it is not within the scope of this article to investigate the ethical perspectives and challenges, significantly the European Commission already proposed to enshrine in EU law a technology-neutral definition of AI systems. The Commission has also suggested to adopt a different set of rules tailored to a risk-based approach with four levels of risks. Meanwhile, AI companies are acting in a grey area: Sam Altman, OpenAI’s (ChatGPT) CEO recently called for laws to mitigate “risks of increasingly ‎powerful AI”. While Altman implies that it becomes increasingly ‎important to address the potential risks and challenges associated with its use, which may not align with societal values or that could harm individuals or ‎communities, he also called for a “balanced approach”, as he is trying to reassure “AI Doomers”[6], ultimately trying to control those political forces that may slow down the release of new AI technology and, therefore, the uncontrollable rise of AI companies and start-ups. 

Recognizing the utmost significance lies in understanding that the majority of AI-related risks that regulators are particularly concerned about can be attributed to two factors. Firstly, it pertains to the creation and practical implementation of novel AI tools that are largely anticipated to emerge in the future. Secondly, the pace at which these advancements occur is crucial, as the pursuit of “Artificial General Intelligence – AGI,” a goal declared by many companies, remains a distant objective that encompasses both unimaginable chaos and marvels, reminiscent of Asimov’s extraordinary storytelling.

For what concerns our present and near future, it is worth mentioning that several international organizations, academic institutions, and businesses all around the world started looking at the impact of AI on more systemic and complex ‎matters, from their social impact (e.g. job losses, academic fraud, discrimination and bias) to the very functioning of our political institutions and economy. UNESCO, the aforementioned European Commission, and global universities have been producing ethics guidelines for already some years now. 

‎Ultimately, AI creates new risks while also providing astounding potential. Keeping the two in ‎balance requires careful bargaining. And it would seem that the moment to discuss the rules of the game has arrived.‎

[1] DISCLAIMER: Please note that the examples provided are dated May 2023 and are just examples based on the information gathered, my personal experience and a few interviews. They are few compared to the breadth of tools that are being released every day and by no means are intended as recommendations.

[2] “Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics—rule-based modelling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.” (reference:



[5] Leaving aside their political analysis, the MIT technology review provides some explanatory insights regarding the functioning of AI and lawmaking in the following article:


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