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.

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Situation at a glance

Fact	Value
Retail customers	~4.2 million
Service contacts / year	~3.1 million
CS employees	~2,400
Platforms	Cloud + legacy core
Weak ask	GenAI chatbot to cut cost

Reframe (parent level)

Weak framing	Engineered framing
Deploy GenAI chatbot	Reduce avoidable demand; improve agent productivity; keep consistent outcomes without raising conduct risk
Model workshop first	Journey map, process mining, assumption map before model choice
Big-bang self-service	Hybrid self-service + agent assist + human control by journey risk

Series expand (Banking CS)

Part	Focus
I Strategy & discovery	Choice cascade, horizons, JTBD, discovery
II Readiness & prioritisation	Maturity, prioritisation
III Commercial case	Benefits and investment
IV Architecture & ops model	Architecture and TOM
V Engineering & evaluation	Build and eval
VI Responsible AI & security	Controls and security
VII Delivery & operations	Change and run
VIII Portfolio & scaling	Scale and portfolio
8D integrated finale	End-to-end integration

SE role across the funnel

Qualify (kill impossible automation promises) → discover (API, auth, knowledge assumptions) → shape (assess → pilot → scale + TOM) → pitch (relevant assist demo with controls) → negotiate (protect eval and escalation) → hand over (RAID + benefit baseline).