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A Comprehensive Guide to Stakeholder Analysis in AI Governance (Part 2)
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A Comprehensive Guide to Stakeholder Analysis in AI Governance (Part 2)

Last Updated on December 11, 2023 by Editorial Team

Author(s): Lye Jia Jun

Originally published on Towards AI.

Understanding Interests, Influences, and Impacts for Key Stakeholders in AI Governance

*Author Note: This article is written as part two of the Comprehensive Guide to Stakeholder Analysis in AI Governance. Read Part 1 here.

Welcome back to the continuation of our ‘A Comprehensive Guide to Stakeholder Analysis in AI Governance.’ If you’re just tuning in, I highly recommend starting with the first part of this two-part series to understand the key stakeholders we’ve identified in the space of AI Governance.

U+1F680 Stay tuned to the end of this article for practical, actionable takeaways from this comprehensive AI Governance stakeholder analysis guide!

Brief Recap of Part 1 of The Guide

In Part 1, we explored the following

  1. The foundations of stakeholder analysis, its significance, and methodologies.

2. The SCIM framework for the Stakeholder Analysis Process.

3. Stakeholder Analysis application in AI Governance by identifying the key stakeholders in the space, step “S” in the SCIM framework.

In Part 2, we will continue with the subsequent steps of the stakeholder analysis process

  • With our identification of the key stakeholders in AI Governance,
    we will learn how to analyze, categorize, and prioritize these stakeholders.
  • Beyond that, we’ll delve into the methods to develop and implement effective engagement strategies, continuously monitor these relationships, and make adjustments as needed.
  • Our guide will conclude with a comprehensive wrap-up, emphasizing the key ACTIONABLE takeaways you can apply in the field of AI governance. Ready to dive deeper? Let’s embark on this next phase.

Table of Content

Part 1

(ICYMI: part 1 of the guide is available here)

  • Stakeholder Analysis
  • What is Stakeholder Analysis?
  • Why Stakeholder Analysis?
  • How Can We Perform Stakeholder Analysis?
  • Applying Stakeholder Analysis to AI Governance
  • Identification of Stakeholders

Part 2

  • Categorize and Prioritize Stakeholders
  • Develop and Implement Engagement Strategies
  • Monitor and Adjust
  • Actionable Takeaways
  • Conclusion

Recap that we have a total of 9 stakeholders in AI Governance belonging to four distinct categories.

After our identification of the key stakeholders in AI Governance, we need to categorize the stakeholders as part of our SCIM framework.

U+1F4A1Remember SCIM: Stakeholder Identification; Categorize and Prioritize; Implement Engagement Strategies; Monitor and Adjust.

Analyze, Categorize, and Prioritize Stakeholders:

Group stakeholders based on their influence, interest, or impact on the project. Common categories include internal, external, primary, and secondary stakeholders.

Determine which stakeholders have the most significant impact or influence, and consider tools like power/interest grids or stakeholder maps to visualize this information.

Power-interest Grid

The power-interest grid is a tool used in stakeholder management to categorize stakeholders based on their influence and interest in a project.

High Power, High Interest (Manage Closely):

  • Stakeholders in this quadrant possess both significant influence over and keen interest in the project. It’s vital to frequently engage and collaborate with these key players, ensuring their inputs are incorporated and concerns are addressed to gain their continuous support.

High Power, Low Interest (Keep Satisfied):

  • These stakeholders have considerable power but might not be deeply invested in the project’s minute details. It’s essential to keep them informed about major milestones and decisions, ensuring their satisfaction while avoiding overwhelming them with unnecessary details.

Low Power, High Interest (Keep Informed):

  • Stakeholders here may not wield significant influence, but they’re very interested in the project’s progress. Regular communication tailored to their concerns and interests ensures they remain informed and positively engaged.

Low Power, Low Interest (Monitor):

  • Stakeholders in this quadrant have minimal influence and interest. It’s still important to monitor their stance and keep them generally informed, but they typically require less intensive engagement than the others.

Categorizing Stakeholders in AI Governance

Given our 9 core stakeholders, we can create a Power-Interest Grid as follows:

Let us explore the process of categorizing and prioritizing these stakeholders.

High Power, High Interest (Manage Closely):

The three core stakeholders in AI Governance placed in the Manage Closely basket are the key international and governmental bodies because these entities often have high power and high interest in AI governance; they are directly responsible for setting standards and regulations and have vested interests in doing so.

Overall, Their decisions can significantly impact the development and application of AI.

Governments/Regulatory Bodies: Possessing the power to legislate and regulate, these bodies exert direct control over actions within their jurisdiction. Their influence on AI Governance is immediate and broad within their territories.

International Bodies: Influencing global norms, these bodies can impact numerous nations and corporations in the AI Governance space. Their broad scope and need for consensus among member nations sometimes mean they operate at a powerful, albeit slower pace.

Other Industry Associations and Professional Bodies: They set industry standards and have a collective voice that impacts practices, such as those in the field of AI safety. They are driven by the interests of their members and often have a more specialized focus than broader entities.

High Power, Low Interest (Keep Satisfied):

In the Keep Satisfied bracket, we have research and academic institutions They are crucial for advancing AI knowledge and informing policy through research. While they may not directly enforce regulations, their insights and research can significantly influence the field’s direction.

Given their strong focus on advancing technologies and science, however, their direct interest in AI Governance may not be high.

Research and Academic Institutions: These entities are centers of knowledge and innovation. Their influence is based on their academic credibility, and they often prioritize knowledge dissemination and research over commercial, political interests, or AI Governance.

Low Power, High Interest (Keep Informed):

End-users and ethicists are often highly interested in the safe and proper governance of AI. They are directly impacted by AI and have a vested interest in demanding AI safety.

Despite their strong interest, however, they often have low or limited power in influencing AI Governance policies.

End-Users: Consumers are directly affected by AI and their preferences and demands can shape the direction of AI. Though weak alone, their power comes from collective demand.

Ethicists and Philosophers: Offering moral and ethical perspectives, their voices can shape the broader discourse around technology and its societal implications, even if they lack the direct influence of other entities.

Low Power, Low Interest (Monitor):

The average AI Engineer typically prioritizes advancing their technical knowledge while corporations and investors are primarily interested in the monetary gain from AI.

AI Governance is often only important to them to the degree that they need to focus on compliance and regulatory requirements; a typical AI engineer, investor, or corporation generally would not have vested interests nor power to influence AI Governance.

Corporations: Driving the commercialization of technology, they have resources and market presence that can scale innovations, largely guided by profitability and market demands. AI Governance, however, is not their primary focus.

Investors: While they hold financial power, they primarily influence the direction of AI to the degree of deciding which projects get greenlit. Their primary interest is monetary return on investments, not AI Governance.

AI Engineers: The technical experts behind innovations, their power lies in their knowledge and skills, but they might lack the interest, platform, or collective voice to drive large-scale change in AI Governance.

Now that we have categorized our stakeholders based on the Power-Interest grid, we can move to the next step of developing engagement strategies.

Develop and Implement Engagement Strategies

Create tailored communication and engagement plans for each stakeholder group to address their specific needs, concerns, and expectations.

Consider the following approaches that you (or your organization) can take to engage with the various stakeholders:

High Power, High Interest (Manage Closely):

  • U+1F3DB️ Governments/Regulatory Bodies: Regular meetings and consultations to ensure alignment with current regulations and anticipate future legislative changes. Offer training sessions or informational seminars about the latest AI advancements. Transparent reporting and proactive disclosure of AI initiatives and impacts, especially regarding ethical considerations and societal implications.
  • U+1F30D International Bodies: Attend international conferences and participate in working groups or committees. Collaborate on developing international standards or guidelines for AI. Publish white papers, position statements, or research findings to inform and influence international norms and standards.
  • U+1F3E2 Other Industry Associations and Professional Bodies: Join industry-specific roundtables and work closely with these bodies to shape industry standards and best practices. Offer partnership opportunities for research or pilot projects. Share best practices, case studies, and technological advancements with these associations through journals, conferences, and seminars.

High Power, Low Interest (Keep Satisfied):

  • U+1F3EB Research and Academic Institutions: Fund and collaborate on research projects. Offer internships or scholarships to students and establish academic chairs or centers of excellence. Publish joint research findings, conduct workshops, and co-host academic conferences. Engage in knowledge exchange programs.

Low Power, High Interest (Keep Informed):

  • U+1F468 End-Users: Gather feedback through surveys, focus groups, or beta testing. Ensure user-friendly AI solutions and address their concerns promptly. Regular product updates, user manuals, FAQ sections, and hosting community forums for open dialogue.
  • U+1F989 Ethicists and Philosophers: Invite them to participate in ethical review boards or committees overseeing AI projects. Host or sponsor ethical debates or forums. Publish ethical considerations and reflections regarding AI projects. Engage in open dialogues and debates.

Low Power, Low Interest (Monitor):

  • U+1F4BC Corporations: Establish strategic partnerships, co-innovation labs, or joint ventures. Regularly review contractual agreements and explore opportunities for collaboration. Conduct quarterly business reviews, share market insights, and organize joint marketing or product launch events.
  • U+1F4B5 Investors: Regular investor meetings, financial updates, and clear demonstration of business growth and ROI. Quarterly financial reports, annual general meetings, and investor relations communications.
  • U+1F468‍U+1F4BB AI Engineers: Provide ongoing training, workshops, and opportunities for engineers to attend conferences or further their education. Foster an inclusive environment where they can voice their opinions and concerns. Internal newsletters, regular team meetings, and feedback sessions.

Monitor and Adjust

Finally, you need to ensure continuous monitoring of stakeholder engagement for AI Governance success.

Continually monitor stakeholders’ responses and feedback. Adapt engagement strategies as needed to ensure ongoing support and alignment.

Periodically review feedback and engagement metrics to gauge the effectiveness of communication and collaboration strategies. As the AI landscape evolves, stakeholders’ interests and concerns will inevitably shift.

Adapt engagement tactics accordingly to maintain alignment, trust, and mutual benefit, ensuring that AI governance remains responsive, inclusive, and dynamic.

If you made it this far, congratulations! U+1F389 We have completed the Complete Stakeholder Analysis of Key Stakeholders in AI Governance.

Now that we have completed our SCIM framework, let us explore the key takeaways from our analysis.

Actionable Takeaways U+1F680

To ensure the successful implementation of AI Governance through comprehensive stakeholder analysis, focus on the following:

  1. Empower the End-Users: While technical and regulatory stakeholders are essential, don’t underestimate the insights from end-users. They often offer real-world feedback that can lead to more ethical and efficient AI applications.
  2. Stay Updated: Regularly review international standards, guidelines, and research from organizations like the OECD and UNESCO to ensure compliance with global best practices.
  3. Prioritize Ethical Decision-making: Always factor in the ethical implications of AI. Seeking advice from multiple stakeholders can provide valuable perspectives on making the most informed choice for complex moral dilemmas.


Effective governance in the AI landscape hinges on comprehensive stakeholder management, addressing the myriad of ethical, societal, and operational challenges. Stakeholder analysis ensures that all voices are considered, risks are minimized, and collective objectives align with AI initiatives.

As AI technologies increasingly influence our world, it’s imperative to maintain robust, adaptable governance. Stakeholders, far from being mere observers, are active participants, collaboratively guiding AI towards a responsible, ethical, and universally beneficial future.

I hope you found value in this piece. Cheers!

Acknowledgement & References

Thank you for journeying with me through my introduction to “The AI Governance Journal”. As we delve deeper into the realms of AI governance, I invite you to join the conversation, challenge the status quo, and champion responsible AI. For a holistic understanding, follow me for more insightful AI Governance pieces. Stay curious and stay informed.

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