Business Continuity and Emerging AI Features
In today’s volatile business environment, the ability to maintain continuity during crises has become a fundamental requirement. Global disruptions—ranging from pandemics and supply chain failures to cyberattacks—have shown how fragile operational models can be. As organisations seek ways to build resilience, two technological advancements—Generative AI (GenAI) and Agentic AI—are shaping new possibilities for adaptation, efficiency, and decision-making. These AI-driven capabilities extend beyond automation; they embed adaptability and autonomy into core business functions.

Generative AI excels at content generation and insight extraction. Agentic AI builds on this foundation by executing tasks with autonomy aligned to human-defined goals. Together, they offer organisations tools not just for growth, but for survival and sustainability. Yet, their relevance to business continuity is often under-discussed. This article examines how these AI capabilities influence continuity strategies, workforce development, operational change, and ethical decision-making.

Generative AI: Enhancing Readiness, Responsiveness and Productivity

Capabilities and Functions

Generative AI refers to AI systems that produce outputs—text, images, audio—based on patterns in large datasets. Unlike traditional automation, which performs fixed functions, GenAI dynamically responds to input, supporting activities like summarisation, content creation, data synthesis, and scenario planning (Harvard Business Review, 2024).

Impact on Organisations

• Operational Efficiency: GenAI tools automate low-value, repetitive tasks, such as generating meeting notes, drafting reports, and monitoring incident trends. This streamlines communication and speeds up decision-making (McKinsey & Company, 2023).
• Training and Knowledge Management: Adaptive learning systems powered by GenAI create customised training paths for employees. This ensures that critical roles can continue functioning with up-to-date knowledge, even when experienced staff are unavailable (Deloitte, 2023).
• Scenario Analysis and Planning: GenAI can model hypothetical disruptions and propose recovery actions. This makes continuity planning a dynamic process that adjusts to changing internal and external variables (ScienceDirect, 2024).

Human-Centric Considerations

For all its benefits, GenAI must be used to support, not sideline, human expertise. Human-AI collaboration should be guided by governance frameworks, with employees given clear training on interpreting AI outputs and recognising limitations (Harvard Business Review, 2023).

Agentic AI: Resilience through Autonomy and Decision-Making

What Makes Agentic AI Different

Agentic AI refers to autonomous systems capable of pursuing goals with limited human intervention. They can initiate actions, adjust tactics in real-time, and maintain continuity during crises (ServiceNow, 2025). Examples include autonomous customer service agents, risk monitoring systems, and supply chain re-routing bots.

Key Organisational Effects

• Change and Decision Autonomy: Agentic AI can make operational decisions during disruptions. For instance, during a network failure, an AI agent might switch services to backup systems without awaiting human approval (Cybersecurity Dive, 2025).
• Redefining Roles: Autonomy shifts the human role from operator to supervisor or exception handler. This requires re-skilling for oversight, policy tuning, and ethical monitoring (Harvard Business Review, 2024).
• Efficiency vs. Productivity: While efficiency improves, productivity outcomes depend on alignment between autonomous tasks and strategic goals. Continuous monitoring is essential to measure whether Agentic AI is enhancing resilience or just speeding up activity (Business Insider, 2025).

Ethical and Human Implications

Agentic systems raise questions of accountability. Who is responsible when an autonomous agent fails? Ethics must be embedded by design, with oversight mechanisms, fail-safes, and transparency protocols (Financial Times, 2025).

Practical Application: Bridging AI and Business Continuity

To realise value from GenAI and Agentic AI, organisations must move from experimentation to structured deployment. This involves:

• Governance Alignment: Create AI governance frameworks that connect with continuity policies and risk management processes.
• Skills Development: Implement continuous training for human-AI collaboration, particularly in ethical decision-making and supervisory control.
• Use of Real-Time Data: AI systems must be fed with updated data to reflect current risks, disruptions, and operational changes.
• Embedding Scenario Thinking: Regularly use GenAI to simulate crises and use Agentic AI to test adaptive response mechanisms.

Examples and Future Trends in AI-Enabled Continuity

Real-world use cases are emerging that illustrate how AI, especially in its generative and agentic forms, is reshaping business continuity:

• Amazon has used machine learning systems to automatically reroute logistics operations during regional weather disruptions, helping maintain delivery timelines during crises (Forbes, 2024).
• Maersk, the global shipping company, has piloted AI agents to manage port and vessel scheduling in real-time, reducing human bottlenecks and improving resilience (MIT Sloan Management Review, 2023).
• HSBC has adopted GenAI for internal scenario stress testing, improving response speed to geopolitical and financial shocks (Financial Times, 2024).

Looking forward, several trends are likely to define the future of AI-enabled continuity:

• Context-Aware Agents: AI systems are becoming more capable of interpreting context—both environmental and operational—which improves responsiveness to nuanced disruptions.
• Integrated Continuity Dashboards: Organisations are starting to develop continuity control centres, powered by GenAI and Agentic AI, to detect, assess, and respond to incidents in real-time (Gartner, 2025).
• Explainable AI for Risk Governance: There is increasing pressure to adopt explainable AI (XAI) in continuity decision-making, especially where accountability and regulatory scrutiny are high (Harvard Business Review, 2025).
• Cross-Sector Collaboration: Continuity planning will increasingly involve cross-sector AI partnerships—such as between logistics and health, or finance and utilities—where shared AI infrastructure improves response speed and coordination (World Economic Forum, 2024).

AI as a Partner in Continuity?

Generative and Agentic AI are not just productivity enhancers—they are continuity enablers. Their roles in planning, executing, and adapting to disruptions make them essential features of modern continuity strategies. Businesses that embrace AI thoughtfully—balancing automation with human oversight—will be better prepared to face uncertainty, maintain service delivery, and protect long-term value.

Future-looking organisations must not view AI features as isolated tools, but as integrated components of strategic resilience. Investing in AI for continuity today is not just an option—it is a necessity.