Advancing Human‐AI Collaboration in Small and Medium‐Sized Enterprises: A Systems Engineering Approach.
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| Title: | Advancing Human‐AI Collaboration in Small and Medium‐Sized Enterprises: A Systems Engineering Approach. |
|---|---|
| Authors: | Ortolano, Luis Flavio1 (AUTHOR), Gallegos, Erika E.1 (AUTHOR) erika3@colostate.edu |
| Source: | Systems Engineering. May2026, Vol. 29 Issue 3, p477-494. 18p. |
| Subjects: | Human-artificial intelligence interaction, Capability maturity model, Small business, System integration, Corporate culture, Employee empowerment, Systems engineering, Artificial intelligence & ethics |
| Abstract: | The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium‐sized Enterprises (SMEs), particularly in fostering effective human‐AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human‐AI Collaboration Maturity Model (HAIC‐MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human‐machine teaming research, HAIC‐MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis (N = 100) confirmed the model's robustness. Subsequent focus group analyses (N = 10, repeated across five sessions) further validated HAIC‐MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners (N = 3) confirmed the usability and usefulness of the final HAIC‐MM tool. HAIC‐MM provides SME leaders with a structured, human‐centered, and systematic approach to evaluate and cultivate human‐AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI‐enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource‐constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary: This paper introduces the Human‐AI Collaboration Maturity Model (HAIC‐MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium‐sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC‐MM provides a practical, step‐by‐step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC‐MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio‐technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real‐world application, HAIC‐MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy. [ABSTRACT FROM AUTHOR] |
| Copyright of Systems Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193398990 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advancing Human‐AI Collaboration in Small and Medium‐Sized Enterprises: A Systems Engineering Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ortolano%2C+Luis+Flavio%22">Ortolano, Luis Flavio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gallegos%2C+Erika+E%2E%22">Gallegos, Erika E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> erika3@colostate.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Systems+Engineering%22">Systems Engineering</searchLink>. May2026, Vol. 29 Issue 3, p477-494. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Human-artificial+intelligence+interaction%22">Human-artificial intelligence interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Capability+maturity+model%22">Capability maturity model</searchLink><br /><searchLink fieldCode="DE" term="%22Small+business%22">Small business</searchLink><br /><searchLink fieldCode="DE" term="%22System+integration%22">System integration</searchLink><br /><searchLink fieldCode="DE" term="%22Corporate+culture%22">Corporate culture</searchLink><br /><searchLink fieldCode="DE" term="%22Employee+empowerment%22">Employee empowerment</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+engineering%22">Systems engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+%26+ethics%22">Artificial intelligence & ethics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium‐sized Enterprises (SMEs), particularly in fostering effective human‐AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human‐AI Collaboration Maturity Model (HAIC‐MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human‐machine teaming research, HAIC‐MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis (N = 100) confirmed the model's robustness. Subsequent focus group analyses (N = 10, repeated across five sessions) further validated HAIC‐MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners (N = 3) confirmed the usability and usefulness of the final HAIC‐MM tool. HAIC‐MM provides SME leaders with a structured, human‐centered, and systematic approach to evaluate and cultivate human‐AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI‐enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource‐constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary: This paper introduces the Human‐AI Collaboration Maturity Model (HAIC‐MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium‐sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC‐MM provides a practical, step‐by‐step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC‐MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio‐technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real‐world application, HAIC‐MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Systems Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/sys.70031 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 477 Subjects: – SubjectFull: Human-artificial intelligence interaction Type: general – SubjectFull: Capability maturity model Type: general – SubjectFull: Small business Type: general – SubjectFull: System integration Type: general – SubjectFull: Corporate culture Type: general – SubjectFull: Employee empowerment Type: general – SubjectFull: Systems engineering Type: general – SubjectFull: Artificial intelligence & ethics Type: general Titles: – TitleFull: Advancing Human‐AI Collaboration in Small and Medium‐Sized Enterprises: A Systems Engineering Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ortolano, Luis Flavio – PersonEntity: Name: NameFull: Gallegos, Erika E. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10981241 Numbering: – Type: volume Value: 29 – Type: issue Value: 3 Titles: – TitleFull: Systems Engineering Type: main |
| ResultId | 1 |
