What this appointment is intended to change
An established financial institution seeks an experienced digital-banking leader to advise on the next stage of its digital operating model and the responsible adoption of artificial intelligence. The institution must modernise around existing customer relationships, core systems, operating processes, control functions and regulatory responsibilities. It is not seeking a greenfield digital-bank concept disconnected from the realities of its current business.
The advisor's task is to identify where digital capability and AI can produce measurable customer and operating value, determine the conditions under which that value can be delivered safely, and help the leadership team make a selective investment decision. The institution does not want a catalogue of technologies or a proliferation of pilots without accountable business outcomes.
This is a six-month subject-matter advisory engagement, not a statutory Board seat or executive appointment. The sponsor, Board and authorised management retain decisions, implementation authority and all regulatory responsibilities. The advisor will work with business, operations, technology, risk, compliance, information security, legal and finance leadership.
The difficult judgments
Digital adoption is not the same as a better banking journey. The advisor will examine onboarding, servicing, payments, customer information changes, assistance, complaints and human escalation. Assess completion, abandonment, repeat contact, failed handoffs, accessibility and unresolved exceptions. A journey should not be considered successful merely because activity has shifted from a branch to an app. Recommendations must preserve fraud controls, customer understanding and access to human help.
A legacy system can constrain innovation without justifying uncontrolled workarounds. Review core interfaces, data duplication, batch timing, manual intervention, identity, entitlements and integration. Help management distinguish capabilities requiring architectural change from those that can be delivered through a controlled intermediate layer. Proposed shortcuts must include ownership, monitoring, rollback and retirement rather than becoming permanent shadow infrastructure.
An AI use case needs a decision owner before a model. Evaluate employee assistance, document processing, service support, fraud investigation, operational knowledge and selected customer interactions. Classify use cases by financial, customer, privacy and conduct consequence. Identify where AI may assist, where a professional must confirm, and where the institution should not deploy it. Do not treat generation quality as evidence that a model is authorised to make a banking decision.
Data availability is not permission to use it. Examine source authority, quality, purpose, access, retention, lineage, customer consent where relevant and third-party processing. Recommendations should make clear what information enters a model, what remains with the institution, who can inspect it and whether vendors may reuse it. Production information must not be casually copied into demonstrations or test environments.
Pilot performance must survive the operating environment. Define representative cases, uncommon but severe failures, language and accessibility needs, human review, prompt or input attacks, unsupported answers, overrides and drift. Measure whether the proposed solution improves resolution and accuracy after supervision, infrastructure, integration and ongoing monitoring costs.
How the six months will be used
The opening stage will establish a fact-based diagnosis: customer-journey failure points, operational workload, data readiness, architecture constraints, current initiatives and decision rights. The advisor will speak directly with frontline and control teams and test selected journeys rather than rely only on programme presentations.
The middle stage will convert that diagnosis into a prioritised digital and AI agenda. Each candidate intervention must have a business owner, baseline, expected benefit, affected customer population, control requirements, delivery dependencies, full cost and a clear rejection or defer case. The advisor will help choose a limited set of initiatives suitable for validation and define vendor evaluation criteria before commercial selection.
The final stage will challenge validation evidence and advise on scale, redesign or cessation. The institution should receive a practical implementation sequence extending beyond the engagement, with approved owners, funding assumptions, risk gates, capability needs and benefit tracking. The advisor is not expected to build production systems, replace independent model validation or provide legal certification.
Work products the institution must be able to use
- A digital-banking diagnostic linking customer friction, operating effort and underlying system or control causes.
- A use-case portfolio that separates attractive ideas from deployable, economically supported interventions.
- An AI governance decision framework covering scope, accountability, data, evaluation, human oversight, incident response and retirement.
- A legacy-modernisation dependency map with controlled transition options and the risks of delaying foundational work.
- A pilot evidence pack and scale decision brief for the agreed initiatives.
- A funded transformation sequence, management capability plan and benefits dashboard for handover.
Benefits should be expressed through resolved customer journeys, reduced repeat work, accuracy, safe automation, sustainable cost and trustworthy service—not demonstration quality, chatbot usage or model count. Monthly sponsor reviews will address decisions and unresolved risks; technical and control workshops will be scheduled according to the work being evaluated.
The experience that matters
Candidates should bring at least 22 years of senior experience across digital banking, banking operations, enterprise technology, transformation, AI product governance or regulated financial services. Relevant careers include digital-bank business leadership, CIO or CTO responsibility, retail or transaction-banking transformation, COO leadership and enterprise AI oversight.
The candidate must have operated within a legacy institution and understand why customer service, risk, core technology and execution do not move at the same speed. Evidence of a transformation that sustained benefits after launch, a technology programme that was redesigned or stopped, and an AI initiative with credible controls will be particularly valuable.
IICA registration is not required. Relationships with banks, model providers, cloud companies, systems integrators, technology vendors, investors or advisory firms must be disclosed. The engagement may not be used to direct procurement or generate undisclosed commercial benefit. All customer and institution information must remain within approved handling arrangements.
At six months, the institution should have better decisions, credible evidence and a team able to carry the agenda forward without dependence on the advisor.