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This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs.

This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result.

This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested.

The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI.

This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies.

This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.

This chapters asks: What do the Artificial Intelligence (AI) strategies of the EU, the United States under Donald Trump and China look like? It conducts a critical policy discourse analysis from a Radical Humanist Perspective. It analyses what kind of ideologies we can find in the AI strategies of the European Union, the United States under Donald Trump and China.

The analysis shows that AI and robotics are situated in a digital technology race that is indicative of an international political-economic race for the accumulation of political-economic power.

This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.

Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.

Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business’ organizational model. A key consideration is a “creative-possibility perspective,” highlighting the future potential to use AI not only for rational but also for creative thinking purposes.

This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a “creative-possibility perspective.”

A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).

This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.

This study explores how the national artificial intelligence (AI) strategies and policies in four sub-Saharan African countries – Mauritius, South Africa, Ghana and Gabon – influence the adoption of AI in journalism. In the journalistic world, AI have been mainly used for news gathering, production and distribution. Irrespective of the prospects, the pervasive nature of AI brings with it a host of challenges concerning privacy, gender, and ethnic bias. Despite its relevance to journalism, the challenges associated with using AI necessitate the need for policy frameworks that guide the development and usage of these technologies. At a global level, UNESCO has established a normative framework which lays out principles and standards regarding how member states formulate policies that ensures ethical and healthy development of AI. Using document analysis and the technological determinism theory, the study investigated how the national AI policies and strategies of these countries is impacting journalism and highlights the challenges to the adoption of the technology in the field. In lieu of the AI-specific laws, the countries seem to loosely rely on their data protection acts to govern aspects of AI use involving automated decision making. Mauritius was found to be the only country in the study with a set national AI strategy.

The purpose of this paper is to identify the potential and the challenges for different firms in adopting an AI-first strategy. The study attempts to discern if any company can prioritize AI at the forefront of their strategic plans.

Drawing from illustrative examples from well-known AI-leaders like Netflix and Spotify, as well as from upcoming AI startups and industry incumbents, the paper explores the strategic role of AI in core business processes and customer value creation. It also discusses the advent and implications of generative AI tools since late 2022 to firms’ business strategies.

The authors identify three types of AI-first strategies, depending on firms’ starting points: digital tycoon, niche carver and asset augmenter. The authors discuss how each strategy can aim to achieve data, algorithmic and execution advantages, and what the strategic bottlenecks and risks are within each strategy.

To the best of the authors’ knowledge, this paper is the first to systematically describe how companies can form “AI-first” strategies from different starting points. This study includes actionable examples from known industry players to more emerging startups and industrial incumbents.

This paper aims to explore the dimensions that foster the effectiveness of artificial intelligence (AI) within a business strategy.

The paper reviews recent contributions to AI and business success and identifies the key pillars that support the achievement of good results.

The paper proposes that there are four critical dimensions for developing an effective business strategy with AI. This research finds that AI has the potential to drive significant development when it is leveraged along four main axes: a focused strategy for AI, knowledge of the customers, effective interactions with customers and effective implementation of AI. These four dimensions are essential for nurturing the critical dimensions of AI that enable successful integration with the business strategy. To achieve this integration, the business strategy must take advantage of the insights and capabilities provided by AI while also understanding and deeply knowing the customers through effective interactions with them. The development is structured in an organizational alignment where AI helps employees and learns from them. By continuously learning from the exploitation of knowledge and big data, the organization can enrich its use of AI.

The paper identifies four pillars of AI integration with the development of business strategy as areas for further empirical analysis by business researchers.

This paper offers strategies for managers and professionals to effectively integrate AI into business strategy.

The authors provide a novel perspective on using AI in business strategy by identifying four key axes of success in the current business landscape.

The purpose of this paper is to review and analyze how the development and utilization of artificial intelligence (AI) technologies for social responsibility are defined in the national AI strategies of the USA, Russia and China.

The notion of responsibility concerning AI is currently not legally defined by any country in the world. The authors of this research are going to use the methodology, based on Luciano Floridi’s Unified framework of five principles for AI in society, to determine how social responsibility is implemented in the AI strategies of the USA, Russia and China.

All three strategies for the development of AI in the USA, Russia and China, as evaluated in the paper, contain some or other components aimed at achieving public responsibility and responsible use of AI. The Unified framework of five principles for AI in society, developed by L. Floridi, can be used as a viable assessment tool to determine at least in general terms how social responsibility is implied and implemented in national strategic documents in the field of AI. However, authors of the paper call for further development in the field of mutually recognizable ethical models for socially beneficial AI.

This study allows us to better understand the linkages, overlaps and differences between modern philosophy of information, AI-ethics, social responsibility and government regulation. The analysis provided in this paper can serve as a basic blueprint for future attempts to define how social responsibility is understood and implied by government decision-makers.

The analysis provided in the paper, however general and empirical it may be, is a first-time example of how the Unified framework of five principles for AI in society can be applied as an assessment tool to determine social responsibility in AI-related official documents.

Research of currently limited literature sees Robotic Process Automation (RPA) as an important tool at the tactical level. However, the literature has not considered its potential contribution to creating competitive advantages. This paper aims to link RPA and Resource-based view (RBV) literature, proposing a conceptual framework boosting RPA research as part of an organizational AI strategy.

This study applied a Systematic Literature Review (SRL), combining bibliometrics and content analysis. This study also built a new framework based on the updated RBV model that was transformed based on the RPA literature review results.

By bridging the two bodies of literature on RBV and RPA, this study manages to show the strategic side of the technology. Therefore, this study brought to light the most updated fundamental concepts of complementarity and scale-free fungible resources from RBV theory and AI technologies, applied to the domains of RPA, information systems and information technology (IS/IT) through the development of a new theoretical lens. Also, this study was able to elaborate on a new conceptual framework for AI strategy formulation to help organizations on their journey to AI utilization.

The authors did not find any research that has shown the strategic side of RPA, nor any that has used a theoretical lens based on the RBV theory to show this side. To the best of the author’s knowledge, this study seems to be the first to make the case for RPA's strategic potential.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

This is a thought-leadership interview with digital-era experts Thomas Davenport and Nitin Mittal on the strategic desirability and implications for legacy companies on being fully committed to becoming AI-driven businesses.

An interview with thought-leaders in the area of digital strategy.

The interview explains what it means to be an AI-driven company and how to manage the kind of transformation that legacy will need to go through to become AI-fueled leaders in their sector.

The main value is the direct dialog with digital-era experts on their latest research insights.