This report analyses organisational transformations driven by the integration of AI into the contemporary economy and assesses the ability of institutions to adapt to end-to-end automation. It highlights the transition from isolated applications to integrated platforms that position AI as a cross-cutting service at the core of operations, aligned with innovation cycles, global investment flows, and increasing state involvement.
The study explores the consolidation of regulatory frameworks and their implications for governance, addressing issues such as traceability, human oversight, technical accountability, algorithmic fairness, and digital sovereignty. It also examines the technological and organisational models that underpin intelligent automation, including AI agents capable of setting objectives, planning tasks, and working alongside human colleagues in a co-piloting model that supports broad-based skills development.
Through sector-specific use cases - spanning supply chains, finance, healthcare, automotive, retail, and telecommunications - the report identifies key success factors: robust data governance, transparency, resource sharing, and effective change management. It further incorporates an analysis of valuation methods and ROI models, while outlining prospective trajectories for 2025-2035, including adaptive workflows, edge computing, and hybrid quantum technologies.
The study explores the consolidation of regulatory frameworks and their implications for governance, addressing issues such as traceability, human oversight, technical accountability, algorithmic fairness, and digital sovereignty. It also examines the technological and organisational models that underpin intelligent automation, including AI agents capable of setting objectives, planning tasks, and working alongside human colleagues in a co-piloting model that supports broad-based skills development.
Through sector-specific use cases - spanning supply chains, finance, healthcare, automotive, retail, and telecommunications - the report identifies key success factors: robust data governance, transparency, resource sharing, and effective change management. It further incorporates an analysis of valuation methods and ROI models, while outlining prospective trajectories for 2025-2035, including adaptive workflows, edge computing, and hybrid quantum technologies.
Table of Contents
1. Executive summary2. Strategic context
2.1. Innovation cycles and investment
2.2. State involvement in AI development worldwide
2.3. Global financial impact
2.4. AI perspectives
3. Use cases and value drivers
3.1. Sectors currently mature for investment
3.2. Automation of internal processes
3.3. Agentic AI
3.4. Customising AI demand
3.5. Use cases in the automotive sector
3.6. Use cases in the retail sector
3.7. Use cases for telecommunications operators
4. Organisational and human transformation
4.1. Redefining roles
4.2. New organisational models
4.3. The imperative of massive upskilling
4.4. Internal training
5. Economic assessment and ROI models
5.1. Advanced valuation methods
5.2. Framework for quantifying benefits
5.3. Macroeconomic impact
6. Governance, risks and ethics
6.1. Algorithmic fairness and operational justice
6.2. Global regulatory compliance
6.3. Sovereignty, legal responsibility and resilience
7. Outlook and development trajectories
7.1. Strategic overview of the future of AI
7.2. Adaptive workflows and intelligent edge
7.3. Hybrid quantum and augmented work
7.4. Forward-looking analysis
