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Artificial intelligence is rapidly entering pharmaceutical manufacturing through predictive analytics, process analytical technology, continuous manufacturing platforms, and data-driven quality control. These developments promise earlier detection of process deviation, adaptive optimization of critical process parameters, and more responsive assurance of critical quality attributes. Yet the dominant regulatory and quality vocabulary remains anchored in Quality-by-Design, a paradigm built around pre-defined knowledge, structured risk assessment, and validated control strategies. The central problem is that AI-assisted manufacturing systems do not behave like conventional pharmaceutical processes governed solely through fixed design spaces. Machine learning models may change performance as data distributions shift, as sensors age, as materials vary, or as feedback loops alter the operating environment. This creates a governance gap between the static logic of pre-validation and the dynamic reality of algorithmic decision-making. This critical conceptual review examines whether Quality-by-Design remains sufficient for AI-assisted pharmaceutical manufacturing. It argues that QbD is still necessary but no longer sufficient because it was designed for processes whose boundaries, models, and control logic can be defined before routine operation. AI-assisted systems require an additional governance logic capable of supervising data, models, explanations, adaptation, and accountability throughout the product lifecycle. The review develops the concept of Quality-by-Intelligence as a forward-looking governance framework. QbI does not discard QbD; rather, it extends it by embedding data stewardship, model lifecycle management, explainable decision-making, adaptive risk control, and regulatory translation into the pharmaceutical quality system. Its purpose is to govern not only the manufacturing process, but also the intelligence layer that increasingly mediates quality decisions. Quality-by-Intelligence reframes pharmaceutical quality as an adaptive, evidence-generating, and accountable system rather than a one-time design achievement. The transition will require new regulatory expectations, new validation evidence, new operator competencies, and stronger collaboration between industry, regulators, and academic experts. Without this shift, AI may be deployed into manufacturing faster than the quality systems needed to govern it.

Algorithmic tools are becoming embedded in pharmaceutical technology development, from formulation screening and excipient selection to process modelling, scale-up prediction, analytical method optimisation, and manufacturing control. These tools increasingly influence decisions that were historically made through human expert judgement, experimental iteration, and quality-system review. This shift creates a governance challenge because algorithmic recommendations may affect product quality, process robustness, patient safety, and regulatory compliance. Yet accountability for such decisions is often distributed ambiguously across data scientists, formulation scientists, process engineers, quality assurance units, regulatory teams, software vendors, and corporate management. The objective of this article is to construct an original governance framework for accountable algorithmic decision-making in pharmaceutical technology development. The framework is designed for settings where artificial intelligence, machine learning, and related computational decision-support tools influence formulation, process, analytical, or quality decisions. The proposed framework argues that accountability must be designed into algorithmic pharmaceutical development rather than retrofitted after model deployment. It defines algorithmic decision types, clarifies accountability triggers, and positions governance as part of the pharmaceutical quality system rather than as a separate digital compliance layer.