Retrieval-Augmented Generation (RAG)

A technical architecture that enhances large language model outputs by retrieving relevant information from an external knowledge base before generating a response, grounding the model's output in verified, up-to-date, and domain-specific data. RAG reduces hallucination risk, enables LLMs to access proprietary or recent information not in their training data, and provides citation capabilities.

The degradation in a machine learning model's predictive accuracy over time as the statistical properties of the input data diverge from the training data distribution. Model drift requires ongoing monitoring and periodic retraining to maintain performance, and is a key operational risk in production AI systems.

An output generated by an artificial intelligence system — particularly large language models — that is factually incorrect, fabricated, or nonsensical, yet presented with apparent confidence. AI hallucinations pose significant risks in applications such as legal research, medical advice, and financial analysis, and their mitigation through grounding, retrieval-augmented generation, and human oversight is a key challenge in enterprise AI deployment.

The process of further training a pre-trained machine learning model on a smaller, domain-specific dataset to adapt it for a particular task or industry. Fine-tuning allows organisations to leverage foundational models while creating proprietary, specialised AI capabilities that constitute identifiable intangible assets.

The documented lifecycle of data as it moves through an organisation's systems, showing its origin, transformations, dependencies, and destinations. Data lineage provides visibility into how data is created, processed, and consumed, enabling organisations to ensure data quality, comply with regulatory requirements (particularly GDPR's right to explanation), debug data pipeline issues, and assess the impact of system changes.

The process of projecting a company's future growth trajectory based on historical data, market conditions, and investment patterns. Incorporating intangible asset data and productivity trends significantly improves forecast accuracy and reduces investor uncertainty.

A tradable certificate representing the right to emit one tonne of carbon dioxide equivalent, or a verified reduction or removal of one tonne of CO2 equivalent from the atmosphere. Carbon credits are traded on compliance markets (such as the EU Emissions Trading System) and voluntary markets, and represent an emerging class of intangible asset with growing valuation complexity as climate regulation intensifies.

A financial model developed by Edward Altman that combines five weighted financial ratios to predict the probability of corporate bankruptcy. The Altman Z-Score is used by investors and creditors as an early warning system, though it can understate the financial health of intangible-intensive firms whose assets are not fully reflected on the balance sheet.

AI Integration Costs: The Hidden Expenses of AI Adoption

AI licensing fees represent only 20-30% of total AI adoption costs. The remaining 70-80% — data preparation, integration engineering, change management, and ongoing maintenance — are the hidden expenses that derail AI budgets and timelines.