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Inside the AI Adoption Lab
Machine Leadership Journal 

The Research Engine - Validating the Formulas and Best Practices that power the AFI Engine.

The Machine Leadership Journal is an ISSN publication that provides DOI research and thought leadership in the field of Machine Leadership, AI Strategy and Adoption. Our Journal is the IP factory that validates the mathematics that drive the AFI engine, talent assessments, and best practice guidelines for CIOs and CHROs. Research findings are immediately integrated into the AFI platform's LLM to dynamically generate assessment questions and calibrate the Benchmarking Database. All publications are managed by our global editorial board that relies on industry best practices for research design, citation quality, and DOI assignment.

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Our Editorial Board consists of technical experts who oversee the empirical validation of the Machine Leadership methodology, ensuring the mathematical accuracy of the AFI constant, the quality of AI adoption best practices, and the defensibility of our financial modeling. Review our research that justified the Machine Leadership Model, Moderating Function, and AFI (Index) Platform.

Peer Review Process

01.

Submit Abstract

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Proposals should be scholarly, insightful, comprehensive, and aligned with the emerging discipline of Machine Leadership. Submissions that include data analysis will require peer review to confirm reproducibility.  

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Content: 500 Words

Citation: APA Format

 02.

Proposal Approval

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Submissions authors that receive approval will be notified by The Machine Leadership Journal and a final proposal will be authorized by the IRB board to confirm the breadth, depth, and quality of the paper and citations.

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Content: Full Proposal

Signed Ethics Statement

 03.

Committee Review

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Draft submissions are reviewed by the IRB Board to ensure the paper maintains authenticity, adequate citation (where applicable), robust insights, and alignment with the emerging field of Machine Leadership.

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Content: Proposal data is subject to peer review

 04.

Publication

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Once the scholarly paper has been approved by the IRB Board, the research shall be included in The Machine Leadership Journal publication and indexed to Google Scholar and other academic databases.

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Full Publication and DOI

Indexed globally

Machine Leadership Journal_Overview
Future of AI_Game Theory

Call for Papers

Machine Leadership Journal

“How do you lead an AI tool that is better at human tasks than you are?”
 
The Machine Leadership Journal is dedicated to advancing the emerging field of AI leadership. We are focused on identifying, developing, and sharing the best practices for AI strategy and adoption. This includes understanding the unique traits and cognitive processes that predict AI leadership success, the methods for integrating machines into the workforce, the process for safely introducing AI to children, and domain specific AI leadership practices that optimize use cases across industries. For our inaugural edition, we are seeking research studies and thought leadership articles that focus on five themes. Each area addresses a specific aspect of AI leadership, while providing a foundation for additional lines of inquiry. Though our Editorial Board maintains a robust peer review process, we welcome author(s) from a variety of professional and academic backgrounds, and AI experience.
The Machine Leadership Journal is seeking contributions for the emerging field of AI leadership. Details regarding topic areas and submission requirements are listed below.
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Focus Area 1: AI Talent Assessments
 
Machines are a new species that requires leaders to have a broad set of skills. In the age of AI, leaders must be able to: 1) lead machines; 2) lead engineers that lead machines; and 3) lead hybrid organizations that adopt AI. Our Editorial Board is interested in research studies and/or thought leadership that highlights the unique competencies that are required to lead machines and AI teams. Topics may include but are not limited to: AI leadership traits, AI cognitive processes, talent assessment methods for AI leaders, and job role and readiness predictors for AI technical domains. Other focus areas could include unique AI leadership factors that impact key industries. The Board is interested in highlighting the similarities and differences between existing theory and emerging trends.
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Focus Area 2: The AI Playground
 
The safe adoption of AI with children is a critical priority for society. This includes AI skills at each child development stage such as preschool, elementary, and adolescence. Our Editorial Board is interested in research studies and/or thought leadership that highlights methods, tools, or best practices for introducing AI to children. This may include but is not limited to: AI child development plans; child data privacy and cyber protection policies; toolkits for parents; and safe adoption practices. The Board is interested in methods for balancing child development between the physical and digital environments, ensuring optimal outcomes in both.
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Focus Area 3: AI Strategy and Adoption
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The ability to maximize AI within an organization or society are important practices. Our Board is interested in research studies and/or thought leadership that analyzes the process for effective adoption of AI. This may include but is not limited to: AI strategy development; managing the balance between Machine Autonomy and Trust; building AI competencies within the workforce; handling ethical AI dilemmas; data governance and privacy policies; keeping up with the pace of AI innovation; and leading change programs that introduce AI tools, platforms, and models. The Board is interested in approaches that balance productivity and automation with human centric design. Specifically, how companies can find the optimal approach to introducing AI?
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Focus Area 4: AI Leadership for Specific Domains
 
AI leadership practices are strongly influenced by the industry and use case. Our Board is interested in research studies and/or thought leadership that highlights best practices for leading machines, engineers, and AI teams within specific contexts such as technical domains or industry use cases. This may include but is not limited to: AI leadership practices for the C-Suite; computer vision; robotics and autonomous vehicles; big data and MLOps; LLMs and Transformers; AI agents and Prompt Engineering; data governance and privacy; cyber security; and ethical AI. The Board is interested in AI leadership practices that impact the technical domain and specific industries. This includes AI leadership methods within technology, healthcare, telecom, energy, among others.
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Focus Area 5: The Future of AI
 
The Future of AI is one where machines will be fully integrated into human society. Our Board is interested in research studies and/or thought leadership that forecasts potential pathways for innovation across all AI technical domains and use cases. This may include but is not limited to: AI enhancements to existing models such as LLMs and Transformers; new AI tools that will be introduced; scenarios where AI becomes further integrated into human life such as IoT and wearables; and new use cases impacting specific industries such as healthcare, finance, energy, among others. The Board is interested in visionary thinking that anchors projections based on historical adoption patterns, the current pace of AI innovation, and the potential pathways for society to adopt new AI tools.

Contribute to Research
Help shape the best practices for AI adoption

Submit Abstract Proposals

Please select area of focus:

Abstract should clearly state the Machine Leadership topic area, the research method, proposed data analysis, and scholarly contribution to AI Leadership.

Please upload your full abstract as a PDF file. Please include the names of all researchers, alongside any academic citations used for the proposal (if needed).

Please indicate that you have read and accept our ethical research standards.
I accept the Ethical Research Standards.

To review the ethical guidelines or learn about the Editorial Board, please visit:


https://www.machineleadership.com/editorial-board

https://www.machineleadership.com/machine-leadership-journal-research-guidelines


For questions, please contact us at: research@machineleadership.com

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