Alliance Bank Malaysia Berhad Hiring! Full Time Digital Transformation (AI - Adoption) in Federal Territory - Ricebowl

Digital Transformation (AI & Adoption)

Own the demand side of the bank's AI and productivity agenda — what capability gets enabled, who uses it, and whether it was worth the money. Business-facing, evidence-driven, and technical enough to be taken seriously by the people building the platform.

Digital Workplace & Productivity

Own the productivity agenda as a capability, not a licence pool — the person who decides what gets switched on, in what order, and with which guardrails.

• Own the digital workplace roadmap — assess, sequence and land productivity and AI capability across the bank's collaboration and content platforms, prioritising by business impact rather than by what the licence happens to include.
• Run enablement as a controlled rollout — design pilots, define user cohorts, configure the platform, measure adoption, and decide on the evidence whether a capability scales, changes shape, or stops.
• Govern business-led automation — run the citizen-developer pathway with templates, standards and review gates, and set clear criteria for when a business-built app or workflow graduates to a supported enterprise asset.
• Keep the estate inside the bank's data boundary — work with Security, Risk and Compliance on classification, data loss prevention, retention and access controls as AI capability spreads across everyday content and collaboration.

Value Realisation & Investment Case

The half of the transformation remit that usually gets skipped. You own the number — before the build, and again afterwards.

• Baseline before anything is built — establish the current cost, cycle time or effort that a use case is meant to improve, so the benefit claimed afterwards can actually be defended.
• Own the ROI model for the AI and productivity portfolio — licence and consumption cost, implementation effort, and quantified benefit, maintained as a live model rather than a one-off slide produced for approval.
• Run post-implementation review — return to deployed capability at a defined interval, measure actual against forecast, and report honestly where the benefit did not materialise.
• Inform investment decisions — supply the evidence that supports scaling, renegotiating or discontinuing a capability, and make the recommendation even when the recommendation is to stop.

Enterprise AI Platform Adoption

Own the pipeline of use cases that flow onto the Enterprise AI Platform, and whether they land once they get there.

• Build and manage the use case pipeline — source, qualify and prioritise AI use cases from across the business, with a clear view of value, feasibility and risk tier for each.
• Steer use cases to the right build surface — help business teams tell the difference between a need met by a workflow automation, a lightweight assistant, or a platform-hosted agent. Builds on the wrong surface are the single largest source of downstream rework and unmanaged risk.
• Bring business-built AI assets under management — onboard agents and assistants created outside the core platform into its registry and entitlement model, so they are visible, owned and controlled on the same terms as everything else.
• Drive adoption after go-live — the work does not end at deployment. Track usage, remove friction, retrain where needed, and retire what nobody uses.

Governance & Assurance

Keep the portfolio ahead of governance rather than in remediation.

• Carry use cases through the governance lifecycle — risk tiering, documentation and control evidence, so initiatives arrive at governance checkpoints ready rather than scrambling.
• Maintain a live view of the portfolio — delivery status, blockers and risk across all active initiatives, escalated early to leadership rather than surfaced at the checkpoint.
• Translate policy into something business teams can follow — partner with Risk and Compliance to turn governance requirements into practical steps a business owner can actually execute.

Technical Foundation

This is a business-facing role with a real technical floor. You are not expected to arrive fluent in our specific tooling — you are expected to have gone deep on a comparable enterprise platform before and be able to do it again.

Capability we expect you to bring

• Enterprise platform ownership — you have administered a significant SaaS or cloud platform at organisational scale: environments, tenancy, access policy, and the lifecycle that moves a change safely into production.
• Low-code or configuration-led delivery — you have built and shipped working solutions on a low-code or workflow automation platform, and you understand where that approach stops being appropriate.
• Commercial and cost modelling — licensing, consumption-based pricing and benefit quantification, to a standard that survives challenge from Finance.
• Data governance in a regulated environment — access control, classification, retention and audit evidence, to the standard expected under BNM RMiT and PDPA.

Capability we expect you to build quickly

• The bank's Microsoft estate — the productivity, identity and low-code platforms the bank runs on. Prior experience is an advantage, not a requirement; the expectation is that you become the internal authority on them within your first two quarters.
• AI and agent application patterns — retrieval-augmented generation, tool and function calling, and agent orchestration, to the depth needed to assess a vendor design critically and challenge a proposed solution shape.

Qualifications

Experience

• 5–8 years in digital transformation, digital workplace or solution delivery, with a substantial hands-on period rather than a purely coordinating or programme-management track record.
• Demonstrated ownership of an enterprise platform estate — not individual project delivery, but responsibility for the standards, controls and lifecycle of a platform used across an organisation.
• Evidence of owning a benefits case end to end — you set the baseline, forecast the return, and went back afterwards to measure it. Examples where the answer was unfavourable are as valuable to us as the successes.
• Evidence of learning a platform to depth — a clear example of picking up an unfamiliar enterprise platform and becoming the person others relied on for it. This carries more weight with us than years spent on any specific product.
• Regulated-industry experience strongly preferred — financial services or an equivalent environment where data governance, auditability and change control are non-negotiable.
• Experience managing or coordinating vendor relationships and RFI/RFP processes, including technical evaluation of fintech or AI/automation solutions.

Skills & Attributes

• Adoption instinct, not just deployment skill — treats a capability as delivered when people are using it and the usage is measurable, not when it has been switched on.
• Willing to steer and to say no — redirects a business request to the right surface, or refuses it outright, and can explain the reasoning to a frustrated stakeholder.
• Numerate and honest about numbers — comfortable building and defending a value model, and equally comfortable reporting that a forecast benefit did not appear.
• Independent technical judgement — able to work deep inside a vendor's ecosystem without assuming that vendor is the answer to every problem.
• Strong written and verbal communication — produces governance-ready documentation and translates technical constraints into business terms for leadership.