164 posts tagged with "Playbook"

Before production, MonGo must show the hybrid customer-service AI is lawful, fair, secure, accountable, controllable and fit for purpose. The goal is not zero risk—it is proportionate controls, measured residual risk and evidence for every decision to continue, restrict or stop.

This article is Part VI of the Banking Customer-Service AI playbook. It follows Part I through Part V.

“Build a generative AI chatbot that reduces customer-service costs” is a common executive request. It is not yet a strategy, a problem statement or an investable use case. This article walks through Part I of the End-to-End AI Solution Engineering Playbook—strategy and discovery—using a realistic retail-banking customer-service scenario.

The worked example is MonGo Bank: a hypothetical retail and small-business bank with millions of customers, a large contact centre, mixed cloud and legacy platforms, and strict regulatory obligations. The goal is not to pick a model. It is to decide where AI should play, how the bank wins, what must be true and what evidence is required before further investment.

Updated: July 27, 2026

This guide explains the entire Artificial Intelligence Governance Professional certification syllabus in practical, exam-focused and operational detail.

It follows the IAPP AIGP Body of Knowledge version 2.1, which became effective for examinations taken on or after 2 February 2026. The Body of Knowledge is reviewed periodically and identifies the knowledge, skills, competencies and performance indicators that may be assessed in the examination.

This is an explanatory study resource rather than an official IAPP course or legal opinion.

Most AI engagements fail not because the organisation lacks frameworks, but because it has too many of the wrong kind. Teams accumulate strategy canvases, maturity models, scoring formulas and governance checklists until the methodology itself becomes the delivery risk. The practical response is not a larger catalogue. It is a core set: enough structure to run end-to-end AI solution engineering, and little enough that practitioners can actually use it.

Consulting sales is not advertising a service, sending a quotation and closing a deal. A firm such as PwC must connect market intelligence, brand and thought leadership, executive relationships, discovery, solution engineering, independence and risk controls, commercial modelling, contracting, delivery, benefits realisation and long-term account growth.

PwC UK describes consulting as combining strategy, technology and delivery, with an emphasis on outcomes rather than advice alone—spanning business operations, customer transformation, cloud, data and analytics, cyber, digital-core modernisation, risk and regulation, technology alliances and managed services.

Artificial intelligence has changed not only what organisations buy, but also how they buy. Enterprise customers rarely begin by asking for a particular model, platform or agent. They usually begin with a business concern—and the commercial challenge is to help them move from an uncertain problem to a trusted investment decision that produces measurable value.

Artificial intelligence has moved from an emerging technology into a mainstream enterprise capability. The strategic question for leadership is no longer whether to adopt AI—it is how to convert widespread access and experimentation into measurable, responsible and sustainable enterprise value.

One of the clearest differences between ordinary management and exceptional leadership is the ability to prepare an organisation for challenges and opportunities that have not yet fully arrived.

Most organisations begin recruiting, reskilling or investing only after capability gaps have become visible. A major client asks for expertise the organisation does not possess. A new technology disrupts an established service. Regulation creates an urgent compliance requirement. Competitors launch a new proposition. Delivery teams become overloaded because demand has grown faster than the available workforce.

Culture is one of the most frequently discussed and least consistently managed responsibilities of leadership.

Many organisations describe their culture through value statements, leadership principles, codes of conduct and employee campaigns. These tools can be useful, but they do not create culture by themselves.

Culture is created through repeated leadership decisions.

A successful Data and AI organisation cannot depend on a small number of experts, individual projects or external suppliers. It needs a deep and sustainable capability that allows the organisation to identify opportunities, design solutions, manage risk, deliver reliably and create measurable business value over many years.

Developing this capability is one of the most important responsibilities of an Executive Data and AI Leader.