Explainable AI

The intangible premium that a business commands above the fair value of its net tangible assets, reflecting factors such as brand strength, regulatory licences, customer loyalty, and market position. Franchise value is a critical concept in financial services and regulated industries where the right to operate carries significant economic worth.

A category of artificial intelligence systems capable of creating new content — including text, images, code, music, and video — based on patterns learned from training data. Generative AI is transforming content production, product design, and software development, raising novel questions about intellectual property ownership and the valuation of AI-generated outputs.

The computational expense of running a trained AI model to generate predictions or outputs in production. Inference costs directly impact the unit economics of AI-powered products and services, and are a key consideration in pricing, margin analysis, and the financial viability of AI deployments at scale.

The minimum amount of capital that financial institutions must hold as required by regulators, serving as a buffer against potential losses. Regulatory capital requirements influence how intangible assets — particularly goodwill — are treated on bank balance sheets and affect the valuation of financial services businesses.

The ability of different information technology systems, software applications, and data formats to communicate, exchange data, and use the information that has been exchanged effectively. Interoperability is a critical design requirement in open banking, healthcare IT, and enterprise software, and is increasingly mandated by regulation.

A framework for developing and deploying artificial intelligence systems that are fair, transparent, accountable, and aligned with human values and societal well-being. Responsible AI encompasses technical practices such as bias testing and model interpretability, alongside governance processes including ethical review boards, impact assessments, and stakeholder engagement.

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

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.

How do you value a franchise agreement as an intangible asset?

Franchise agreements are valued using the income approach based on the present value of franchise fee income and royalty streams over the remaining agreement term, adjusted for renewal probability.

What is responsible AI and why does it affect enterprise value?

Responsible AI is the practice of developing and deploying AI systems that are fair, transparent, accountable, and privacy-preserving — it affects enterprise value by reducing regulatory risk and building stakeholder trust.

What does AI due diligence involve for investors?

AI due diligence evaluates a company's AI capabilities, data assets, model performance, technical debt, governance practices, talent dependency, and the defensibility of its AI competitive advantages.

What AI governance frameworks should companies adopt?

Companies should adopt AI governance frameworks covering ethical principles, risk management, transparency, accountability, and compliance — drawing from standards like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.