Table 2 Application of AI to Cynefin... | Emerald Publishing

Table 2

Application of AI to Cynefin domains

Cynefin domain/dominant VUCA element	Strategic planning characteristics	Contribution of AI	Examples of AI solutions
Simple domain/volatility	Setting clear and simple strategic goals	By providing insights about organisational results, competitor movements, employee performance, industry developments and regulatory changes, AI provides a comprehensive overview essential for crafting competitive strategic goals (Von Krogh et al., 2021; Chowdhury et al., 2022).	Waste reduction and recycling goals: by analysing waste generation patterns, AI can help a waste management company to set realistic waste reduction goals for businesses. AI-powered systems monitor waste processing in real-time, optimizing recycling processes and reducing landfill waste. Sales and marketing performance goals: AI algorithms analyse historical sales data, market trends and customer behaviour to set achievable sales targets.
	Application of established procedures and best practices	AI-driven process automation ensures routine tasks and decisions consistently follow established procedures and thus reduces errors and improves efficiency (Braganza et al., 2017).	Supply chain optimization: use AI to automate inventory and logistics, minimizing stock issues. By processing large volumes of real-time data from various sources, AI can identify patterns and inefficiencies in the supply chain, suggesting further optimizations. Automated Customer Service Systems: Implement AI chatbots that provide 24/7 service, with natural language processing to understand and respond to customer inquiries accurately ensuring consistent quality of service and effectively reducing response times.
	Continuous monitoring for deviations	AI-powered analytics and monitoring tools can track key performance indicators (KPIs) in real-time and send alerts or notification if deviations occur or are likely (Overgoor et al., 2019; Seyedan and Mafakheri, 2020). Moreover, by analysing emerging trends, customer feedback and current product performance, AI uncovers market opportunities for future development (Huang and Rust, 2021; Ledro et al., 2022).	Analytics dashboard: a company creates a dashboard that uses machine learning to analyse market data and customer behaviour in real-time, helping it adjust strategies quickly. Retail chain optimization: implement AI analytics to monitor real-time sales and inventory, enabling swift adjustments to stock and promotions. Operational efficiency optimization: a manufacturing company uses machine learning predictions to adjust in real time production schedules and manage inventory, optimizing operational efficiency. Investment strategy adjustment: financial institutions use machine learning in real time to predict market fluctuations, adjusting investment strategies to maximize short-term gains.
Complicated domain/uncertainty	Meaningful data collection and predictive analytics	AI leverages big data analytics to process vast amounts of information and provide actionable insights. Machine learning algorithms analyse complex data sets, helping inform decisions (Selz, 2020; Seyedan and Mafakheri, 2020).	Market analysis and forecasting: businesses can use AI to analyse market trends, customer preferences and economic indicators to forecast future market conditions and plan product launches or expansions accordingly. Customer behaviour analysis: companies can leverage machine learning algorithms to analyse customer data, identifying patterns in purchasing behaviour to tailor marketing strategies, improve customer engagement and enhance product offerings. Risk management: financial institutions can use AI for risk assessment, analysing vast datasets to identify potential risks and vulnerabilities in their investment portfolios and adjust their strategies to mitigate them. M&A investment strategies: AI techniques can be used to create predictive quantitative models on M&A targets that assist decision-maker in estimating potential synergies and evaluating deal value.
	Access to expertise and insights	AI can act as a knowledge repository, providing access to vast amounts of expert knowledge through natural language processing (NLP) and chatbots. This supports decision-makers in navigating complicated issues (Patel and Trivedi, 2020).	Healthcare decision support: implementing AI to analyse medical data and literature, aiding in the diagnosis and treatment planning for complex diseases. Market data repository and analyses: developing AI chatbots to systematically categorizes and analyses available internal and public market data. Innovation and research development: applying AI to sift through extensive research materials and patents to identify new product development and innovation opportunities.
	Scenario planning and risk assessment	AI-powered predictive analytics can construct different scenarios and assess risks associated with them. This allows organisations to prepare for multiple potential outcomes (Noriega et al., 2023; Spaniol and Rowland, 2023).	Retail: utilize AI to forecast consumer behaviour and economic impacts, aiding in inventory management, store placement and targeted marketing. This helps in adapting to consumer preferences and supply chain challenges. Healthcare: apply AI to simulate patient demand under pandemics or policy changes, ensuring resource efficiency and emergency preparedness through better capacity planning and contingency strategies. Financial services: use AI to model market fluctuations and economic conditions to assess risks like loan defaults or investment losses, guiding strategies for risk mitigation through diversified investments and credit policies. Manufacturing: leverage AI to predict supply chain disruptions or demand shifts, facilitating risk assessment for production and inventory management and creating adaptive strategies for business continuity.
Complex domain/complexity	Real-time data collection and analysis	AI can analyse large amounts of data from a variety of sources, including customer feedback, market research and social media in real time, to identify new trends and opportunities (Davenport, 2018). AI predictive analytics can forecast immediate consequences (Shancang et al., 2018).	Agriculture: by monitoring satellite and soil data, AI optimizes crop management and sustainability, suggesting precise farming techniques and crop rotation strategies. Finance: utilizing market trends and investor behaviour analysis, AI predicts stock movements, aiding in personalized investment advice and risk management. Manufacturing: by analysing production and market demand data, AI optimizes processes and implements predictive maintenance, enhancing supply chain efficiency and reducing costs.
	Cross-functional collaboration and diverse perspectives	AI can facilitate collaboration by providing data-sharing platforms and collaborative tools using natural language processing and sentiment analysis which allows alternative stakeholder perspectives to be captured and considered (Tan et al., 2023).	Global project management: implement an AI-driven platform integrating slack for real-time translation and summary of discussions across languages. Use sentiment analysis to identify concerns, improving project management across global teams. Product development insights: utilize an AI tool with sentiment analysis to gather consumer feedback from social media on product prototypes, guiding feature prioritization based on user preferences for a technology startup.
	Emergent and adaptive strategies	AI can provide real-time analysis of the situation and simulate various response scenarios enabling decision-makers to understand the potential impact of different strategies (Aldoseri et al., 2023).	Customer service: AI chatbots powered by machine learning algorithms and natural language processing analyse in real-time customer requests and suggest products customers are most likely to need or want and therefore buy. E-commerce: by applying sentiment analysis and NLP techniques, AI can identify emerging issues, concerns and sentiments towards the retailer's brand or products. This real-time feedback allows the company to adapt its strategies rapidly and address customer needs and preferences effectively. Banking: AI predictive analysis is used to understand the relationship between equity capital markets deals and investors based on the equity offering details, historical deal participation, trading and client touch point information, and market data, allowing the bank to make very targeted investor pitches.
Chaotic domain/ambiguity	Rapid response and crisis management protocols	AI can trigger alerts when specific thresholds are breached, facilitating immediate action. Machine learning algorithms can help in automatic detection of anomalies and escalate to the right stakeholders for immediate response, thereby increasing the speed of crisis management (Baryannis et al., 2019).	Cybersecurity: AI uses machine learning to spot abnormal activities or breaches and sends immediate alerts, enabling companies to take prompt action against potential cyber threats. Mental health: AI analyse text conversations and identify patterns. The learnings are then used to evaluate different interventions and enhance crisis response for future interactions. Manufacturing: AI creates “cognitive supply chains,” which predict potential shortages or disruptions, automate inventory management and recommend alternative suppliers or delivery routes, ensuring minimal disruption in times of crisis.
	Trial-and-error learning	AI can provide decision support through the generation of virtual models or simulations, automated analysis of a large variety of data and propose multiple courses of action to support experimentation in real-time (Phillips-Wren, 2012).	Predictive toxicology: AI models predict potential toxicity of compounds early in the drug development process. By analysing historical data on molecular structures and their effects, AI can forecast adverse reactions, reducing the risk of late-stage failures in drug development. Business model innovation: NLP can process and analyse customer feedback, expert opinions and market commentary, providing qualitative insights about alignments between a company's value proposition and customer expectations, prompting changes to value creation and delivery components.
	Real-time communication and collaboration	AI machine learning algorithms classify and sort emails in real-time based on their content and importance, decluttering inboxes and ensuring that crucial communication gets timely attention, while NLP can suggest responses to messages or emails by understanding the context, allowing swift and efficient communication (Mca, 2020).	Remote working: AI-powered analytics tools use machine learning and natural language processing to analyse meeting data and provide insights into how to optimise team productivity. Video conference providers: AI-powered video conference can automatically divide cloud recordings into smart chapters for easy review, highlight important information, create next steps for attendees to take action or write a summary of the meeting. Real-time chat: through chat sentiment analysis, teams can gauge the emotional tone of conversations, while automated response suggestions streamline communication processes. Project management: AI real-time progress tracking can monitor each team member's contributions, track project timelines, identify bottlenecks and ensuring timely interventions when necessary.

Cynefin domain/dominant VUCA element	Strategic planning characteristics	Contribution of AI	Examples of AI solutions
Simple domain/volatility	Setting clear and simple strategic goals	By providing insights about organisational results, competitor movements, employee performance, industry developments and regulatory changes, AI provides a comprehensive overview essential for crafting competitive strategic goals (Von Krogh et al., 2021; Chowdhury et al., 2022).	Waste reduction and recycling goals: by analysing waste generation patterns, AI can help a waste management company to set realistic waste reduction goals for businesses. AI-powered systems monitor waste processing in real-time, optimizing recycling processes and reducing landfill waste.Sales and marketing performance goals: AI algorithms analyse historical sales data, market trends and customer behaviour to set achievable sales targets.
	Application of established procedures and best practices	AI-driven process automation ensures routine tasks and decisions consistently follow established procedures and thus reduces errors and improves efficiency (Braganza et al., 2017).	Supply chain optimization: use AI to automate inventory and logistics, minimizing stock issues. By processing large volumes of real-time data from various sources, AI can identify patterns and inefficiencies in the supply chain, suggesting further optimizations.Automated Customer Service Systems: Implement AI chatbots that provide 24/7 service, with natural language processing to understand and respond to customer inquiries accurately ensuring consistent quality of service and effectively reducing response times.
	Continuous monitoring for deviations	AI-powered analytics and monitoring tools can track key performance indicators (KPIs) in real-time and send alerts or notification if deviations occur or are likely (Overgoor et al., 2019; Seyedan and Mafakheri, 2020). Moreover, by analysing emerging trends, customer feedback and current product performance, AI uncovers market opportunities for future development (Huang and Rust, 2021; Ledro et al., 2022).	Analytics dashboard: a company creates a dashboard that uses machine learning to analyse market data and customer behaviour in real-time, helping it adjust strategies quickly.Retail chain optimization: implement AI analytics to monitor real-time sales and inventory, enabling swift adjustments to stock and promotions.Operational efficiency optimization: a manufacturing company uses machine learning predictions to adjust in real time production schedules and manage inventory, optimizing operational efficiency.Investment strategy adjustment: financial institutions use machine learning in real time to predict market fluctuations, adjusting investment strategies to maximize short-term gains.
Complicated domain/uncertainty	Meaningful data collection and predictive analytics	AI leverages big data analytics to process vast amounts of information and provide actionable insights. Machine learning algorithms analyse complex data sets, helping inform decisions (Selz, 2020; Seyedan and Mafakheri, 2020).	Market analysis and forecasting: businesses can use AI to analyse market trends, customer preferences and economic indicators to forecast future market conditions and plan product launches or expansions accordingly.Customer behaviour analysis: companies can leverage machine learning algorithms to analyse customer data, identifying patterns in purchasing behaviour to tailor marketing strategies, improve customer engagement and enhance product offerings.Risk management: financial institutions can use AI for risk assessment, analysing vast datasets to identify potential risks and vulnerabilities in their investment portfolios and adjust their strategies to mitigate them.M&A investment strategies: AI techniques can be used to create predictive quantitative models on M&A targets that assist decision-maker in estimating potential synergies and evaluating deal value.
	Access to expertise and insights	AI can act as a knowledge repository, providing access to vast amounts of expert knowledge through natural language processing (NLP) and chatbots. This supports decision-makers in navigating complicated issues (Patel and Trivedi, 2020).	Healthcare decision support: implementing AI to analyse medical data and literature, aiding in the diagnosis and treatment planning for complex diseases.Market data repository and analyses: developing AI chatbots to systematically categorizes and analyses available internal and public market data.Innovation and research development: applying AI to sift through extensive research materials and patents to identify new product development and innovation opportunities.
	Scenario planning and risk assessment	AI-powered predictive analytics can construct different scenarios and assess risks associated with them. This allows organisations to prepare for multiple potential outcomes (Noriega et al., 2023; Spaniol and Rowland, 2023).	Retail: utilize AI to forecast consumer behaviour and economic impacts, aiding in inventory management, store placement and targeted marketing. This helps in adapting to consumer preferences and supply chain challenges.Healthcare: apply AI to simulate patient demand under pandemics or policy changes, ensuring resource efficiency and emergency preparedness through better capacity planning and contingency strategies.Financial services: use AI to model market fluctuations and economic conditions to assess risks like loan defaults or investment losses, guiding strategies for risk mitigation through diversified investments and credit policies.Manufacturing: leverage AI to predict supply chain disruptions or demand shifts, facilitating risk assessment for production and inventory management and creating adaptive strategies for business continuity.
Complex domain/complexity	Real-time data collection and analysis	AI can analyse large amounts of data from a variety of sources, including customer feedback, market research and social media in real time, to identify new trends and opportunities (Davenport, 2018). AI predictive analytics can forecast immediate consequences (Shancang et al., 2018).	Agriculture: by monitoring satellite and soil data, AI optimizes crop management and sustainability, suggesting precise farming techniques and crop rotation strategies.Finance: utilizing market trends and investor behaviour analysis, AI predicts stock movements, aiding in personalized investment advice and risk management.Manufacturing: by analysing production and market demand data, AI optimizes processes and implements predictive maintenance, enhancing supply chain efficiency and reducing costs.
	Cross-functional collaboration and diverse perspectives	AI can facilitate collaboration by providing data-sharing platforms and collaborative tools using natural language processing and sentiment analysis which allows alternative stakeholder perspectives to be captured and considered (Tan et al., 2023).	Global project management: implement an AI-driven platform integrating slack for real-time translation and summary of discussions across languages. Use sentiment analysis to identify concerns, improving project management across global teams.Product development insights: utilize an AI tool with sentiment analysis to gather consumer feedback from social media on product prototypes, guiding feature prioritization based on user preferences for a technology startup.
	Emergent and adaptive strategies	AI can provide real-time analysis of the situation and simulate various response scenarios enabling decision-makers to understand the potential impact of different strategies (Aldoseri et al., 2023).	Customer service: AI chatbots powered by machine learning algorithms and natural language processing analyse in real-time customer requests and suggest products customers are most likely to need or want and therefore buy.E-commerce: by applying sentiment analysis and NLP techniques, AI can identify emerging issues, concerns and sentiments towards the retailer's brand or products. This real-time feedback allows the company to adapt its strategies rapidly and address customer needs and preferences effectively.Banking: AI predictive analysis is used to understand the relationship between equity capital markets deals and investors based on the equity offering details, historical deal participation, trading and client touch point information, and market data, allowing the bank to make very targeted investor pitches.
Chaotic domain/ambiguity	Rapid response and crisis management protocols	AI can trigger alerts when specific thresholds are breached, facilitating immediate action. Machine learning algorithms can help in automatic detection of anomalies and escalate to the right stakeholders for immediate response, thereby increasing the speed of crisis management (Baryannis et al., 2019).	Cybersecurity: AI uses machine learning to spot abnormal activities or breaches and sends immediate alerts, enabling companies to take prompt action against potential cyber threats.Mental health: AI analyse text conversations and identify patterns. The learnings are then used to evaluate different interventions and enhance crisis response for future interactions.Manufacturing: AI creates “cognitive supply chains,” which predict potential shortages or disruptions, automate inventory management and recommend alternative suppliers or delivery routes, ensuring minimal disruption in times of crisis.
	Trial-and-error learning	AI can provide decision support through the generation of virtual models or simulations, automated analysis of a large variety of data and propose multiple courses of action to support experimentation in real-time (Phillips-Wren, 2012).	Predictive toxicology: AI models predict potential toxicity of compounds early in the drug development process. By analysing historical data on molecular structures and their effects, AI can forecast adverse reactions, reducing the risk of late-stage failures in drug development.Business model innovation: NLP can process and analyse customer feedback, expert opinions and market commentary, providing qualitative insights about alignments between a company's value proposition and customer expectations, prompting changes to value creation and delivery components.
	Real-time communication and collaboration	AI machine learning algorithms classify and sort emails in real-time based on their content and importance, decluttering inboxes and ensuring that crucial communication gets timely attention, while NLP can suggest responses to messages or emails by understanding the context, allowing swift and efficient communication (Mca, 2020).	Remote working: AI-powered analytics tools use machine learning and natural language processing to analyse meeting data and provide insights into how to optimise team productivity.Video conference providers: AI-powered video conference can automatically divide cloud recordings into smart chapters for easy review, highlight important information, create next steps for attendees to take action or write a summary of the meeting.Real-time chat: through chat sentiment analysis, teams can gauge the emotional tone of conversations, while automated response suggestions streamline communication processes.Project management: AI real-time progress tracking can monitor each team member's contributions, track project timelines, identify bottlenecks and ensuring timely interventions when necessary.

Source(s): Authors' own elaboration