Posts about the AI Playbook product and practice
View All TagsCase 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.
Case A · Expand III (parent: Meridian overview). After preferred bidder: contract, land the team, measure value, expand only when earned.
Case A · Expand I (parent: Meridian overview). From market signal through discovery to a written problem reframe.
Case A · Expand II (parent: Meridian overview). Shape the offer: options, TOM, architecture, value case, pitch and negotiation.
Case B parent. MonGo Bank is the playbook’s worked retail-and-SME bank: millions of customers, a large contact centre, mixed cloud and legacy platforms, strict conduct and privacy obligations. The executive ask arrives as “build a generative AI chatbot that reduces cost.” This parent states the reframe and points to the expanded Banking CS series.
A persuasive sales story is not a rehearsed customer-success anecdote inserted into every meeting. It is a carefully selected narrative that helps a particular buyer recognise their current problem, understand the consequences of inaction, see themselves in a better future, trust that the proposed path is credible, and feel comfortable making the next commitment.
Evaluating a conventional machine-learning model is often straightforward. A classification model can be measured using accuracy, precision, recall, F1 score, or area under the curve. A regression model can be measured using mean absolute error or root mean squared error.
Large language model applications are more difficult to evaluate because they are:
A production-grade evaluation strategy therefore cannot rely on one benchmark or one quality score. It must evaluate the system at several levels:
Hugging Face Evaluate provides useful building blocks for metrics, comparisons, and measurements. However, Hugging Face currently presents LightEval as the more actively maintained toolkit for modern LLM benchmarking, while agentic applications require additional trace, tool, environment, and outcome evaluation capabilities.
Communication is not improved simply by learning more vocabulary, memorising presentation formulas or watching successful speakers. A leader must be able to communicate clearly under pressure, adjust the message to different audiences, listen carefully, establish confidence, explain difficult decisions and guide people towards action.
One practical way to develop these capabilities is shadowing.
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.