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The purpose of this article is to illustrate and address idea gaps to bring improvement and add value to organizations.

A framework for ideas is developed to enhance and extend conventional management theory and practice.

It recognises the essential interaction and progression of ideas so that managers can more comprehensively encourage, promote, integrate, and advance ideas in their organisations.

A breakthrough passage is suggested and constructed.

To measure, group, and understand the role of ideas in the workplace.

The study carries out a cluster analysis of what managers say is happening to ideas in their workplace.

This pilot study finds that the role of ideas, their interaction, and progression across organizations and workplaces can be identified and measured. It provides a way of identifying group mindsets and a medium for understanding and addressing orthodoxy.

The main implication is to gain a practical understanding of the role of ideas in any group's performance. The questions about ideas are widely applicable across groups. The research was also limited by its sample size.

By identifying how ideas are currently addressed, the approach is available for a wide range of group situations in organizations and across disciplines. It can be used to recognize, harness and improve the contribution of disparate ideas to performance and outcomes.

The article's originality is in its focus on the role of ideas in organization and society. The study highlights the role of idea interaction and progression, invaluable in integrating the development of the theory and practice of leadership, organization and management.

Ideas contribute to the achievement of high performance, excellence, and competitive advantage in management and organisation. To intervene to facilitate these favourable outcomes, managers need to be aware of how ideas are affected by mindsets they use and what is actually happening to ideas in their workplaces. This paper reports on a pilot survey among managers from diverse organisations. Respondents provide their views about what happens to ideas in their organisations, with comments about the responsiveness of colleagues. It is presumed that how managers engage with ideas in the workplace will reflect the mindsets that they bring from the theory and practice of management. These mindsets are discussed. The study suggests that these mainstream management approaches as applied in our enterprises might not be adequate, and foreshadows the need for their improvement.

Successful ideas break through and get implemented in organisation and in society. This article proposes a Breakthrough Model as a new framework for understanding the passage of breakthrough ideas. It accommodates and moves beyond mainstream management thinking in identifying the factors and processes that contribute to ideas breaking through. Drawing on illustrative insights from the Darwinian revolution, and the literature, it suggests that organisations can make substantially better use of their existing human skills and improve performance through the management of the passage of breakthrough ideas

To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.

Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.

The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.

This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.

AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.

The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.

The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.