New opportunity

Digital Transformation & AI Adoption, AVP

Temasek · Singapore

About this role

The Digital Transformation & AI Adoption, AVP sits within Temasek’s AI Automation & Fluency function in its Technology department. The role leads organisational change programmes that move employees from AI awareness to sustained use in investment, operations and support workflows. A particular focus is helping users delegate work to AI agents, design multi-step workflows, verify outputs, apply guardrails and maintain human oversight.

Responsibilities

Own change management for major AI deployments, from stakeholder mapping and impact assessment through adoption tracking, workflow telemetry and continuous improvement. Design campaigns with milestones, success measures and accountability, and develop an AI champion network within business units.
Partner with senior Technology, Investment and Operations leaders to secure sponsorship and address barriers involving tools, policies, workflow integration, culture or enablement.
Create role-based AI learning journeys, internal communications and feedback mechanisms tailored to different users, use cases and risk profiles. Coach employees on agentic AI practices in real workflows.
Report on adoption using dashboards, usage data, cohort patterns and qualitative feedback. Run AI fluency assessments using surveys, telemetry and role-based benchmarks; build rapid AI prototypes for workflow testing; and connect adoption, productivity, quality and risk outcomes to the business case for continued investment.

Requirements

Degree in Engineering or Computer Science, or equivalent practical experience in digital transformation, enterprise technology adoption or AI-enabled change programmes. At least 7 years of experience in enterprise technology adoption or change management, with demonstrated success moving large-scale AI or digital adoption beyond pilots into sustained, measurable use.
Technical literacy in generative AI, agentic workflows, adoption analytics and human-in-the-loop oversight. Strong communication, storytelling, programme management, measurement and cross-functional influencing skills, with an iterative approach to feedback and changing priorities.
Prior experience in software engineering, solution architecture, data or technical product delivery is advantageous. The posting lists the job type as Permanent.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI adoptionAgentic AIAI agentsGenerative AIChange managementDigital transformationProgramme managementStakeholder managementInternal communicationsAI fluencyAdoption analyticsUsage telemetryMeasurement frameworksWorkflow designHuman-in-the-loop oversightAI prototypingData analysisStorytelling

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Make AI adoption stick beyond pilots
The role owns change programmes from stakeholder mapping and impact assessment through campaigns, adoption tracking and continuous improvement. Prepare an example of moving users from awareness to sustained, measurable workflow use, with milestones and accountable owners.
Build safe agentic AI habits
The posting emphasizes helping employees delegate to AI agents, design multi-step workflows, verify outputs, apply guardrails and retain meaningful human oversight. Prepare to explain these behaviours through a concrete workflow rather than a tool demonstration.
Adapt enablement across business units
The role calls for learning journeys tailored to investment professionals, operations staff and functional specialists, supported by communications, feedback mechanisms and an AI champion network. Prepare to show how use cases, risk profiles and oversight responsibilities shape different approaches.
Connect adoption evidence to business value
The role produces dashboards and reports, assesses fluency using surveys and telemetry, and links adoption to productivity, quality and risk-management outcomes. Prepare to distinguish usage from capability uplift and realised value, including how findings would change the programme.

Likely challenges to think through

Balancing scale with workflow-specific needs
Likely challenge, not a confirmed problem: a common adoption campaign may not fit the distinct workflows and risk profiles named in the posting. Prepare a way to set shared milestones while tailoring learning, support and measures by employee group.
Increasing agent autonomy without losing oversight
Likely challenge, not a confirmed problem: multi-step agent workflows can save effort while making output verification and human responsibility harder to maintain. Prepare a workflow example showing where delegation stops, what users check and how guardrails affect use.
Showing value amid changing conditions
Likely challenge, not a confirmed problem: usage may rise without corresponding gains in productivity or quality, while tools and governance evolve. Prepare to explain how telemetry, surveys and qualitative feedback would be interpreted together, and when the evidence would warrant programme changes.

Practice your answers

Technical practice

How would you take an enterprise AI deployment from awareness to sustained use?

What to coverSuggested preparation: Outline stakeholder mapping, impact assessment, activation, habit formation and ongoing improvement. Explain how milestones, accountable owners and evidence of use would distinguish sustained adoption from a successful pilot.

How would you help employees decide what to delegate to an AI agent?

What to coverSuggested preparation: Describe how to assess a task’s workflow context, risk and required human judgment before delegation. Explain how employees could define the agent’s scope and checkpoints in a multi-step workflow.

How would you design verification and oversight for agentic work?

What to coverSuggested preparation: Address output review, appropriate guardrails and points where a human should intervene. Discuss the trade-off between useful automation and retaining meaningful oversight as tasks become more complex.

How would you design different AI learning journeys for investment, operations and support teams?

What to coverSuggested preparation: Compare their use cases, workflow contexts, risk profiles and oversight responsibilities rather than proposing one curriculum for everyone. Explain how practice in real workflows and feedback would shape each journey.

What would you put in an AI adoption dashboard, and how would you interpret it?

What to coverSuggested preparation: Consider usage data, workflow signals, cohort patterns and qualitative feedback alongside activation and sustained-use measures. Explain how you would identify a cohort needing support without treating usage alone as proof of value.

How would you assess whether AI fluency is improving?

What to coverSuggested preparation: Combine surveys, telemetry and role-based capability benchmarks, as the posting specifies. Discuss baselines, repeat assessments and how differences between stated confidence and observed use could inform support.

How would you use a rapid AI prototype to test a workflow idea?

What to coverSuggested preparation: Describe selecting a real workflow, defining what users should test and gathering early feedback on usefulness, quality and oversight needs. Explain how the findings would guide iteration rather than treating a prototype as evidence of sustained adoption.

How would you build a case for continued AI fluency investment?

What to coverSuggested preparation: Connect adoption evidence with productivity indicators, quality measures and risk-management outcomes. Acknowledge the difficulty of attributing outcomes to adoption alone and explain what evidence would make the case more credible.

How would you diagnose an adoption plateau?

What to coverSuggested preparation: Use cohort and workflow data with structured user feedback to distinguish tool friction, policy gaps, integration issues and insufficient enablement. Explain how you would prioritise an intervention and check whether it changed behaviour.

How would you adapt a change programme as AI capabilities and governance evolve?

What to coverSuggested preparation: Describe a review cadence for user feedback, adoption measures and changes to tooling or governance requirements. Explain how you would update learning, communications and guardrails while keeping milestones and accountability clear.

Behavioural practice

Tell me about securing senior sponsorship for a cross-functional change programme.

What to coverSuggested preparation: Use a real situation, the actions you took to connect the programme to different leaders’ priorities, and the result. Clarify how you handled disagreement or limited sponsorship without claiming an outcome you cannot evidence.

Describe a time you influenced peers you did not manage directly.

What to coverSuggested preparation: Use a real example involving peer advocates or cross-functional partners, with your actions and the result. Focus on how you built trust, clarified responsibilities and maintained momentum without formal authority.

Tell me about resolving a systemic barrier to technology adoption.

What to coverSuggested preparation: Set out the situation, how you established whether the barrier was technical, policy-related, workflow-related or cultural, and what you did with relevant partners. Close with the observed result and any remaining limitation.

Describe a programme you changed after feedback or disappointing adoption data.

What to coverSuggested preparation: Give the initial approach, the evidence that challenged it, your adjustment and the result. Show how you responded to resistance or a setback without relying on anecdote alone.

Tell me about managing several change workstreams with shifting priorities.

What to coverSuggested preparation: Use a real situation to explain how you set milestones, assigned accountability and made trade-offs across teams. State what you communicated when priorities changed and what the outcomes were.

Sharpen your knowledge

Topics to prepare

Adoption as sustained behaviour change
Posting: Campaigns must move employees from awareness through activation to embedded habits, beyond pilots. Revision exercise: explain what would distinguish each stage and what evidence would show sustained use rather than initial interest.
Agentic workflows and human oversight
Posting: Employees need to delegate to agents, design multi-step workflows, verify outputs, apply guardrails and retain meaningful oversight. Revision exercise: sketch a hypothetical workflow, marking what the agent does, what a person checks and when work should stop for review.
Role-based AI fluency
Posting: Learning journeys must reflect the different use cases, risks and oversight responsibilities of investment professionals, operations staff and functional specialists. Revision exercise: outline how one lesson on output verification would differ for two of these groups.
Measurement of adoption and value
Posting: The role combines surveys, telemetry, role-based benchmarks and qualitative feedback, then connects adoption with productivity, quality and risk outcomes. Revision exercise: define a small set of measures and explain what each can—and cannot—establish about realised value.
Sponsorship and adoption barriers
Posting: The role partners with senior leaders and addresses tool friction, policy gaps, workflow integration, cultural resistance and enablement needs. Revision exercise: classify hypothetical barriers by type and identify which partners would need to help resolve them.

Skills to revise

Plan an adoption programme
Posting: This role owns change management across multiple workstreams, with milestones, accountability and continuous improvement. Practice: draft a one-page plan for a hypothetical AI deployment covering stakeholder mapping, impact assessment, campaign stages, measures and review points.
Mobilise stakeholders and champions
Posting: The role secures senior sponsorship and builds an AI champion network within business units. Practice: prepare a stakeholder map and a short briefing that distinguishes what sponsors, champions and business users would each do.
Tailor AI communications
Posting: Responsibilities include executive briefings, team messaging, intranet content and success stories. Practice: write a concise executive update and a staff message about the same hypothetical adoption initiative, adjusting the evidence and call to action for each audience.
Interpret adoption signals
Posting: Dashboards and narrative reports must analyse usage, workflow signals, cohort patterns and feedback to recommend action. Practice: use a small invented dataset to identify a cohort with declining use, state what further feedback you would seek and propose one programme adjustment.
Coach safe agent use
Posting: The role coaches users in real workflows on delegation, verification, guardrails and oversight. Practice: rehearse a short coaching conversation in which a user selects a task for an agent, defines review checkpoints and decides how to handle an uncertain output.
Test a workflow concept
Posting: The role builds rapid AI prototypes so users can test concepts against real workflows and give early feedback. Practice: outline a small, hypothetical prototype, the workflow steps it would test, the user feedback to collect and the criteria for revising it; no particular tool is specified.

Your preparation checklist

Prepare a sustained-adoption case study
If you have a relevant example, build a short walkthrough showing how an AI or digital programme moved beyond a pilot: your role, stakeholder decisions, obstacles, usage measures, and documented outcomes. The posting specifically calls for sustained, measurable adoption.
Draft a change-programme plan
For a hypothetical AI deployment, outline stakeholder mapping, impact assessment, sponsorship across Technology, Investment and Operations, campaign milestones, and an AI champion network. Show how you would address tool, policy or workflow barriers.
Walk through an agentic workflow
Prepare an illustrative multi-step task delegation to AI agents. Identify where users would verify outputs, apply guardrails and retain human oversight; distinguish the exercise from any real project you have delivered.
Sketch role-based enablement
Draft brief learning-journey outlines for investment professionals, operations staff and functional specialists, plus one sample communication. Adapt each to its workflow, risk profile and oversight responsibilities.
Design an adoption-measurement example
Create a sample dashboard outline combining usage telemetry, cohort patterns, survey feedback and role-based fluency benchmarks. Explain how the findings would change the programme and how productivity, quality and risk indicators could support a value narrative.
Prepare a rapid-prototype walkthrough
If you have relevant work, select a prototype you can explain in terms of the workflow tested, user feedback and resulting iteration. Otherwise, sketch how you would test an AI concept with users without presenting it as completed experience.
Write questions for the interviewers
Ask which user cohorts and workflows are priorities, how adoption and realised value are currently measured, where control partners enter the process, and what barriers the AI champion network is expected to address.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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