40 posts tagged with "Governance"

A strong AI-focused MBA should not replace traditional management education with technical AI training. It should combine four pillars:

1. MBA fundamentals — economics, finance, accounting, strategy, marketing and operations.
2. Leadership and organisational capability — communication, negotiation, change, culture and mindful management.
3. AI and data literacy — machine learning, generative AI, analytics, experimentation and AI product management.
4. Responsible execution — governance, risk, regulation, cybersecurity, operating models and financial value.

This resembles the direction taken by programmes such as Wharton’s AI for Business major, Kellogg’s MBAi and NYU Stern’s Tech MBA. Wharton separates AI education into technical foundations and societal/ethical impact; Kellogg combines MBA, technical and integrated AI cores; NYU combines a business core, technology core and experiential projects. (Wharton OID)

Artificial intelligence leadership is often misunderstood as the ability to select the best model, approve an AI strategy or sponsor a portfolio of proofs of concept. Those activities matter, but they are not the essence of leadership.

AI leadership is the disciplined conversion of uncertain technological capability into measurable, secure, governed and socially acceptable outcomes.

A management consultant helps senior leaders solve important business problems, make difficult decisions and implement organisational change.

In an AI engagement, the consultant’s job is not simply to recommend an AI model or build a chatbot. The consultant must answer a broader set of questions:

Where can AI create measurable business value, which use cases should we invest in, how should the solution operate, what technology is required, what risks must be controlled, and how do we persuade people to adopt it?

A go-to-market strategy for an AI solution is not simply a marketing plan. It is the coordinated design of:

For AI products, the “trust” component is particularly important. A technically impressive solution can still fail because the buyer cannot establish:

• Who is accountable for its outputs.
• Where customer data is processed.
• Whether the model can hallucinate.
• Whether regulators will accept it.
• Whether employees and customers will use it.
• Whether its financial benefits exceed implementation and operating costs.

In EMEA, the challenge is greater because EMEA is not one market. An AI solution sold in the UK, Germany, the UAE, Saudi Arabia and South Africa may require different hosting, contracting, languages, regulatory controls, partner models and sales motions.

This guide explains the complete process and then applies it to a detailed hypothetical case study: an AI customer-service platform for regulated banks.

Knowledge distillation is a model-compression and capability-transfer technique in which a powerful teacher model provides training signals for a smaller student model.

Instead of requiring the student to rediscover every useful behaviour from raw internet-scale pretraining data, the student learns from the teacher’s outputs, probability distributions, internal representations, reasoning demonstrations or preferences.

A distilled model can therefore become:

• Faster at inference
• Less expensive to operate
• Smaller in memory
• Easier to deploy on limited hardware
• More specialised for a particular task
• More consistent than the original general-purpose model for a narrow workflow

However, an important distinction must be made:

The exact internal process used to train current proprietary Claude models is not publicly disclosed in full.

Anthropic publishes model reports, safety research and selected training information, but its public materials do not expose Claude’s weights, token logits, hidden states, training datasets or the complete teacher–student training recipe. Therefore, when people discuss “distilling Claude,” they may be referring to two different things:

1. Authorised distillation within an official platform, such as Amazon Bedrock’s documented Claude Sonnet-to-Haiku distillation workflow.
2. Black-box behavioural distillation, where permitted Claude outputs are collected and used to fine-tune another model.

Anthropic and AWS have publicly described an authorised workflow in which Claude 3.5 Sonnet generates synthetic training data, Claude 3 Haiku is trained and evaluated using that data, and the resulting distilled model is hosted for inference. This is primarily output-based behavioural distillation rather than traditional access to Claude’s internal logits or hidden layers.

Enterprise generative AI engineering is no longer limited to writing prompts and connecting an application to a large language model. A production AI system must combine software engineering, governed data access, model routing, retrieval, tool execution, evaluation, monitoring, security, cost control and continuous delivery.

Databricks addresses these requirements through an integrated set of capabilities covering the complete GenAI lifecycle: querying foundation models and agents, building custom and low-code agents, connecting agents to governed tools, preparing structured and unstructured data, implementing retrieval with AI Search, deploying agents and applications, governing traffic through Unity AI Gateway, tracing with MLflow, evaluating and monitoring quality, operationalising through LLMOps, and delivering governed experiences through Genie One.

Deploying a large language model is not simply a question of choosing between an open-source model and a commercial API. It is an enterprise risk decision involving:

• What information the system will process.
• Where that information will travel.
• Who can access the model, prompts, outputs and logs.
• What actions the model can perform.
• How the organisation will detect failures or attacks.
• Which party is accountable when something goes wrong.
• What evidence can be presented to auditors, regulators, customers and executives.

The 8D AI Solution Engineering Framework turns an unclear AI ambition into a valuable, secure, governed and operational service. For MonGo Bank, it transforms “build a chatbot to cut cost” into a trusted hybrid customer-service capability—and maps every framework from Parts I–VIII into one controlled learning cycle.

A funded hybrid AI programme still fails if MonGo ships a strong model inside a weak system. Architecture and operating design must cover channels, authentication, banking APIs, knowledge, retrieval, models, guardrails, evaluation, escalation, monitoring, governance, cost and ownership.

This article is Part IV of the Banking Customer-Service AI playbook. It follows Part I, Part II and Part III.

Strategic fit and readiness do not fund a programme. MonGo Bank must still prove what the hybrid AI service will cost, which benefits are cash versus capacity, who owns them and when to continue, expand or stop.

This article is Part III of the Banking Customer-Service AI playbook: commercial case, benefits and investment. It follows Part I: Strategy and Discovery and Part II: Readiness, Maturity and Prioritisation.