The convergence of artificial intelligence and the proposed European Financial Data Access Regulation (FiDA) is reshaping financial advisory in ways that affect both client relationships and the structural composition of financial institutions. The dominant discourse has long oscillated between two extreme narratives: technological utopianism, which forecasts the displacement of human advisors, and technological scepticism, which confines algorithms to instrumental roles. This paper articulates a third narrative, that of human-machine symbiosis, in which AI capabilities and human expertise are deliberately coordinated as complementary contributors to advisory decision-making, and shows that this view carries macro-financial consequences that have received insufficient attention. The argument unfolds in four steps. We first review four interconnected literatures: AI in financial advisory, human-machine collaboration, FiDA and Open Finance, and capital structure theory in banking. We then develop a conceptual framework in which client data, algorithmic insight, and advisor expertise are coordinated as knowledge assets, deriving two implementation scenarios that distinguish AI-as-empowerment from AI-as-autonomous-interface configurations. A design-science proof-of-concept operationalises the framework on FiDA-aligned synthetic data, evaluated against alignment, suitability-augmentation, and interpretability criteria. Finally, we develop the macro-financial implications. AI-mediated advisory under FiDA can serve as a demand-side mechanism that complements the supply-side capital pressures of Basel III/IV. Through three transmission channels, ranging from direct conversion of deposits into Tier 1 or Tier 2 capital to maturity-profile improvements, depositors are progressively transformed into stakeholders, lowering institutional leverage and enhancing resilience to adverse macroeconomic shocks. The contribution is fourfold: a theoretically grounded alternative to dominant narratives; a mapping of FiDA provisions onto AI-advisory operational requirements; a development of macro-financial implications linking micro-personalisation to capital structure dynamics; and an algorithm-agnostic proof-of-concept.