AI Solution Engineering practice, 8D methodology, and VALUE gates
View All TagsArtificial 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?
Financial modelling is the process of translating a business idea, investment, product, project, or company into numbers.
A financial model helps decision-makers answer questions such as:
For an ordinary software project, financial modelling usually connects customers, prices, employees, and infrastructure costs.
For an AI solution, the model must connect several additional variables:
For example:
The most important principle is:
Do not measure only cost per token, request, model call, or GPU hour. Measure cost and value per successful business outcome.
Examples of meaningful AI financial units include:
Cloud FinOps applies the same fundamental relationship to AI as to other cloud services:
However, AI introduces unusual quantities such as input tokens, output tokens, model calls, GPU time, retrieval operations, evaluation runs, agent steps, tool calls, and human-review events. FinOps therefore recommends connecting cloud costs to business-unit economics rather than viewing infrastructure spending in isolation.
Case C parent. A multi-country banking group issues an “enterprise agentic AI” RFP: aggressive timeline, outcome guarantees on cost reduction, broad liability for model outputs, vendor-shaped specs, unclear data residency. Partners want the logo. Pursuit cost is high. This parent states the decision pattern; expanded articles show qualification/independence and commercial realism.
Case C · Expand II (parent: Bid/no-bid overview). Translate Conditional pursue into commercials that are deliverable—or walk away cleanly.
Case C · Expand I (parent: Bid/no-bid overview). Run structured qualification and independence before heavy proposal investment.
This hub is the parent entry for three AI solution engineering case-study series. Each series has a short parent overview and expanded articles you can study in depth. Use the same checkpoints on every case: signal → qualify → reframe → shape → engineer → prove value → land.
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:
Case A parent. Meridian Insurance (composite) faces rising cost-to-serve. The board wants a GenAI plan this quarter. The COO sponsors; the CRO fears hallucination, privacy and audit gaps. Knowledge is fragmented; there is no enterprise evaluation framework. This overview states the decision and outcome. Expanded articles cover discovery, solution/commercial design and delivery.