STRATEGEN.AI Research Papers

Abstract: As artificial intelligence increasingly automates critical resource allocation decisions across energy, water, and food systems, organisations and policymakers face an emerging paradox: algorithmic optimisation promises unprecedented efficiency while simultaneously obscuring profound questions about equity, governance, and democratic accountability. Drawing on the Decoded podcast episode "Resource Optimisation AI" and contemporary case studies from Australia and globally, this paper examines how AI-driven resource systems redistribute power and risk across populations. Rather than viewing optimisation as a purely technical problem, this research reframes it as a governance challenge requiring transparency mechanisms, participatory design, and institutional guardrails that align algorithmic efficiency with human values. The paper provides actionable recommendations for business leaders and policymakers seeking to deploy resource optimisation AI responsibly.

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Abstract: The Bureau of Meteorology’s 2025 website rollout highlights a critical truth: digital transformation fails without effective change management. Despite advanced technology, poor stakeholder engagement, timing, and communication eroded trust and usability. This case study illustrates the escalating need for structured, human-centred change leadership in the digital era.

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Abstract: Noosa Council, the local government authority for the Noosa region in Queensland, Australia, was recently defrauded in a sophisticated attack that revealed how generative AI now industrialises social engineering, defeating traditional financial and cybersecurity controls. This paper examines the governance, process, and cultural vulnerabilities across local government, identifies emerging AI risk roles, and proposes a standards-aligned maturity model to strengthen resilience, accountability, and public trust in AI-enabled systems.

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Abstract: A global BCG study exposes a widening AI value gap, with only 5% of firms realising transformative returns. For Australia, lagging adoption threatens economic competitiveness. This paper outlines the five pillars of AI success, case studies of local leaders, and urgent steps Australian enterprises must take to survive.

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Abstract: This paper explores why digital experience has become the core brand experience, requiring organisations to embrace human-centred design, inclusivity, and accessibility. It examines emerging technologies like AI, personalisation, nudges, and AEO, alongside governance, privacy, workforce transformation, and efficiency—showing how frictionless digital experiences drive trust, loyalty, and competitive advantage.

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Abstract: This paper explores AI-powered personalisation, highlighting its dual impact: delivering measurable business growth and enhanced consumer engagement, while simultaneously enabling manipulation through dark patterns, hypernudging, and disinformation. It underscores regulatory challenges, ethical imperatives, and the urgent need for governance to balance innovation, consumer trust, and democratic integrity.

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Abstract: This paper analyses digital transformation in Australian private health insurance, highlighting AI, telehealth, IoT, and emerging technologies. It explores member service enhancement, workforce productivity, regulatory compliance, and trust frameworks, offering a strategic roadmap for insurers to transition from reactive claims processing to proactive, preventive, and predictive healthcare ecosystems.

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Abstract: This paper explores how AI-driven CMS platforms are revolutionising MarTech stacks. By enabling natural language publishing, automating design, and enhancing personalisation, AI democratizes content creation, reducing reliance on technical specialists. For CMOs, this shift offers cost savings, productivity gains, and resource repurposing while reshaping marketing team structures and strategy.

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Abstract: This white paper explores how cultural institutions can resist the algorithmic flattening of creativity by reimagining AI as a disruptive, emotional, and irreverent collaborator. It introduces the “Anarchy Stack,” a strategic framework for using AI to amplify cultural risk-taking, provoke engagement, and lead with mystery

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Abstract: This paper examines the emergence of AI-powered executive leadership, from experimental AI CEOs today to speculative AI presidents of the future. It explores potential benefits (impartiality, efficiency, consistency) and ethical concerns, value misalignment, concentration of power, and accountability. Drawing on Gawdat and Hinton, it outlines pathways to responsible AI governance.

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Abstract: This research explores technical approaches to privacy-preserving machine learning including federated learning, differential privacy, and homomorphic encryption applications. We demonstrate practical implementations that maintain model performance while ensuring GDPR compliance and user privacy protection.

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Abstract: The proliferation of artificial intelligence tools, particularly conversational agents and autonomous systems, has generated considerable concern about cognitive offloading and the potential deterioration of human thinking abilities. However, emerging empirical evidence suggests a counterintuitive phenomenon: daily users of AI systems are demonstrating enhanced cognitive capabilities, particularly in critical thinking, metacognitive awareness, and structured problem-solving. This paper synthesises quantitative and qualitative research from multiple domains, including neuroscience, workplace productivity studies, and educational interventions, to examine how AI interaction, specifically through prompting and iterative refinement, serves as cognitive scaffolding that strengthens rather than weakens human intellectual capacities. Analysis of data from over 14,000 participants across diverse professional and educational settings reveals consistent patterns of cognitive enhancement, with productivity gains averaging 29.6% and significant improvements in metacognitive skills. The evidence indicates that AI tools, when used iteratively and critically, function as cognitive partners that demand clearer thinking, more precise communication, and enhanced analytical skills from users.

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Abstract: This paper examines AI-powered digital twins as “city brains” for governments, enabling scenario-based urban planning, environmental monitoring, and visitor economy forecasting. It assesses cost-benefit considerations, explores governance structures emphasising data sovereignty, and draws on global and Australian examples to highlight strategic, operational, and societal value.

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Abstract: This paper examines AI’s role in workplace transformation, dispelling myths of full human replacement. Drawing on recent research and global case studies, it explores AI’s decision-making limits, human-AI complementarity, productivity optimisation, and four-day workweek feasibility. Findings emphasise strategic collaboration, ethical governance, and human oversight as keys to success.

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Abstract: This paper framework addresses the critical challenges of implementing artificial intelligence systems in enterprise environments while maintaining ethical standards, transparency, and accountability. We present a structured approach to bias mitigation, algorithmic transparency, and regulatory compliance across multiple jurisdictions, including GDPR, CCPA, and emerging AI governance frameworks.

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Abstract: This paper presents implementation strategies for data mesh principles in large enterprises, addressing domain ownership and federated computational governance. Our research demonstrates how organisations can transition from centralised data platforms to distributed, domain-oriented data ownership models while maintaining data quality, security, and analytical capabilities.

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Abstract: This paper argues that organisations must establish ethical, privacy-respecting technology infrastructure today to safeguard future data-led decision-making. Drawing on global frameworks and privacy-preserving techniques, it highlights governance, compliance, and risk mitigation strategies that prevent automation harms, uphold public trust, and ensure sustainable, lawful AI integration across sectors.

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