AI management challenges, opportunities and success factors for public and private collaborations Marc EsteveVisiting Professor, Department of Strategy and General Management Data science and artificial intelligence (AI) hold great promise for public sector organizations to improve services for citizens. But a great challenge remains: governments do not have sufficient knowledge or resources to integrate AI into public services on their own. Harnessing the potential of AI for society requires collaboration between universities and the public and private sectors. This collaborative approach is already the norm in applied AI centers of excellence around the world. But despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In our research in Philosophical Transactions, we show the opportunities and challenges of AI for the public sector and propose a series of strategies to successfully manage cross-sector collaborations. Artificial intelligence holds great promise for public sector organizations Management challenges and opportunities of AI While the challenges of collaboration across private sector organizations has been widely researched, much less attention has been paid to the difficulties of working across the public, private and non-profit sectors. The first issue that can hinder collaboration success is the different environments surrounding public and private organizations. While public organizations are accountable to their service users and the public at large, private organizations are responsible to their shareholders. This can lead to clashes when aligning the interests of the various stakeholders. Public-sector procurement of AI-based technologies presents challenges and raises questions of accountability. Who is responsible for a decision taken by an algorithm when it has an adverse impact on someone's life? Or for the potential criminal misuse of AI and data? Another central challenge of potential AI collaborations between the public and private sectors is the divergent approaches to managing risk: the political risks of governments are not easily reconciled with the market risks of business organizations. In particular, managers of collaborative ventures may find it difficult to deliver public value for money while also maximizing profits to satisfy shareholders. As future collaborations related to AI take place, there is always the inherent risk that the data used have been gamed or sabotaged to serve the opportunism of a self-interested actor. Additional challenges related to cross-sector collaborations around AI relate to skills and data. There is a significant skills gap in AI between the public sector and business and universities. Public organizations lack individuals who possess knowledge and skills in AI and require technical assistance and training Developing the digital skills needed for public sector use of AI is not a quick process. More funding is needed for PhD students in machine learning to overcome this shortfall. 7 factors for success in AI collaborations Our research findings point to the following seven managerial strategies that can contribute to the success of AI collaborations between the public and private sector: 1. Facilitative leadership In contrast to the classic idea of hierarchical leaders who impose their views on followers by relying on a position of power, facilitative leadership endorses respect and positive relationships among team members, constructive conflict resolution and candid expression of thoughts and attitudes. Our analysis concludes that leaders of collaborations should promote broad and active participation, ensure broad influence and control, facilitate productive group dynamics and extend the scope of the process. Facilitative leadership is imperative to collaboration, especially since incentives to participate can be low and resources may often be asymmetrically distributed. 2. Shared objectives Even if all the parties in a collaboration are highly aligned with the main objective of the alliance, there may be differences between the objectives of each organization. To ensure success, it is important that objectives be aligned because they act as a guide for decision-making and a reference standard for evaluating success. 3. Gathering and sharing knowledge Management activities should focus on institutional capacity-building for joint action, such as the creation of common standards for the collection and processing of data. On a technical level, organizations are challenged by the way they manage their collaborative data networks to create data-sharing across jurisdictions. Formulating common standards for data collection and improving data-sharing procedures is crucial to ensure successful collaborations. 4. Communication A communication strategy can have a direct impact on the management of a collaboration. When the collaboration is visibly producing tangible outcomes, stakeholders are more willing to invest time, energy and resources. This happens by showing the value of joint actions through quick wins. 5. Socializing When managers make the impact of collaboration efforts transparent for key players to work together, collaboration improves. Transparent results and indicators can facilitate more ideas and reforms across all levels of the collaboration when it may be more difficult to implement a top-down idea in decentralized settings. 6. Expertise Hiring tech-savvy network managers and shepherding the efforts of field experts within the network can induce trust on the basis of their competencies and improve service quality. The appropriate use of relevant technology can significantly improve performance in data quality, data integration, data analysis and visualization. 7. Sense-making In cross-sector collaborations, relationships can be asymmetric: one partner may need more cooperation than the other. In these scenarios of unbalanced reciprocity, it is effective to create strategies for trust-building and persuasion. A collaboration manager must, then, make sense of this situational need and stimulate the network structure by encouraging actors to engage themselves. About the author Marc Esteve joined ESADE Business School in 2016 as a Visiting Professor. He is a Senior Lecturer of International Public Management at the School of Public Policy at University College London. Dr. Esteve is also the director of the MPA in Public Administration and Management at UCL. Prior to joining UCL, he was a visiting research fellow at Cardiff Business School and a postdoc at the ESADE Institute of Public Governance and Management. Prof. Esteve's primary research interests have focused on understanding how individual characteristics influence decision-making, specifically in interorganizational collaborations. He is currently delving more deeply into the mechanisms and effects of personality in the context of collaboration; his present research involves a study that explores the role of personality variables on strategic decision-making by using experimental designs.

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Artificial intelligence collaborations for society

04/2019

AI management challenges, opportunities and success factors for public and private collaborations




Marc Esteve

Visiting Professor, Department of Strategy and General Management





Data science and artificial intelligence (AI) hold great promise for public sector organizations to improve services for citizens. But a great challenge remains: governments do not have sufficient knowledge or resources to integrate AI into public services on their own.


Harnessing the potential of AI for society requires collaboration between universities and the public and private sectors. This collaborative approach is already the norm in applied AI centers of excellence around the world.


But despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In our research in Philosophical Transactions, we show the opportunities and challenges of AI for the public sector and propose a series of strategies to successfully manage cross-sector collaborations.


Artificial intelligence holds great promise for public sector organizations


Management challenges and opportunities of AI


While the challenges of collaboration across private sector organizations has been widely researched, much less attention has been paid to the difficulties of working across the public, private and non-profit sectors.


The first issue that can hinder collaboration success is the different environments surrounding public and private organizations. While public organizations are accountable to their service users and the public at large, private organizations are responsible to their shareholders. This can lead to clashes when aligning the interests of the various stakeholders.


Public-sector procurement of AI-based technologies presents challenges and raises questions of accountability. Who is responsible for a decision taken by an algorithm when it has an adverse impact on someone's life? Or for the potential criminal misuse of AI and data?



Another central challenge of potential AI collaborations between the public and private sectors is the divergent approaches to managing risk: the political risks of governments are not easily reconciled with the market risks of business organizations.


In particular, managers of collaborative ventures may find it difficult to deliver public value for money while also maximizing profits to satisfy shareholders. As future collaborations related to AI take place, there is always the inherent risk that the data used have been gamed or sabotaged to serve the opportunism of a self-interested actor.


Additional challenges related to cross-sector collaborations around AI relate to skills and data. There is a significant skills gap in AI between the public sector and business and universities. Public organizations lack individuals who possess knowledge and skills in AI and require technical assistance and training


Developing the digital skills needed for public sector use of AI is not a quick process. More funding is needed for PhD students in machine learning to overcome this shortfall.


7 factors for success in AI collaborations


Our research findings point to the following seven managerial strategies that can contribute to the success of AI collaborations between the public and private sector:



1. Facilitative leadership


In contrast to the classic idea of hierarchical leaders who impose their views on followers by relying on a position of power, facilitative leadership endorses respect and positive relationships among team members, constructive conflict resolution and candid expression of thoughts and attitudes.


Our analysis concludes that leaders of collaborations should promote broad and active participation, ensure broad influence and control, facilitate productive group dynamics and extend the scope of the process. Facilitative leadership is imperative to collaboration, especially since incentives to participate can be low and resources may often be asymmetrically distributed.


2. Shared objectives


Even if all the parties in a collaboration are highly aligned with the main objective of the alliance, there may be differences between the objectives of each organization. To ensure success, it is important that objectives be aligned because they act as a guide for decision-making and a reference standard for evaluating success.


3. Gathering and sharing knowledge


Management activities should focus on institutional capacity-building for joint action, such as the creation of common standards for the collection and processing of data. On a technical level, organizations are challenged by the way they manage their collaborative data networks to create data-sharing across jurisdictions. Formulating common standards for data collection and improving data-sharing procedures is crucial to ensure successful collaborations.



4. Communication


A communication strategy can have a direct impact on the management of a collaboration. When the collaboration is visibly producing tangible outcomes, stakeholders are more willing to invest time, energy and resources. This happens by showing the value of joint actions through quick wins.


5. Socializing


When managers make the impact of collaboration efforts transparent for key players to work together, collaboration improves. Transparent results and indicators can facilitate more ideas and reforms across all levels of the collaboration when it may be more difficult to implement a top-down idea in decentralized settings.


6. Expertise


Hiring tech-savvy network managers and shepherding the efforts of field experts within the network can induce trust on the basis of their competencies and improve service quality. The appropriate use of relevant technology can significantly improve performance in data quality, data integration, data analysis and visualization.


7. Sense-making


In cross-sector collaborations, relationships can be asymmetric: one partner may need more cooperation than the other. In these scenarios of unbalanced reciprocity, it is effective to create strategies for trust-building and persuasion. A collaboration manager must, then, make sense of this situational need and stimulate the network structure by encouraging actors to engage themselves.


About the author


Marc Esteve joined ESADE Business School in 2016 as a Visiting Professor. He is a Senior Lecturer of International Public Management at the School of Public Policy at University College London. Dr. Esteve is also the director of the MPA in Public Administration and Management at UCL. Prior to joining UCL, he was a visiting research fellow at Cardiff Business School and a postdoc at the ESADE Institute of Public Governance and Management.


Prof. Esteve's primary research interests have focused on understanding how individual characteristics influence decision-making, specifically in interorganizational collaborations. He is currently delving more deeply into the mechanisms and effects of personality in the context of collaboration; his present research involves a study that explores the role of personality variables on strategic decision-making by using experimental designs.

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