New opportunity

Head - AI Enablement & Productivity Office

GREYTIP SOFTWARE PRIVATE LIMITED · Bangalore, India

About this role

Lead greytHR’s AI Transformation Office to make AI and automation adoption effective across the company. The role is responsible for measurable improvements in internal productivity, cycle time, quality and operational effectiveness, with an emphasis on reuse, governance and leader accountability.

Responsibilities

Define the office’s charter, scope and ways of working. Establish governance with Security, IT and functional leaders, and create a network with one AI Champion per function or line of business.
Own the AI initiative portfolio. Set up intake and prioritization based on ROI, feasibility, reuse and risk; maintain a register of stages, owners, baselines, targets, adoption and risks; and manage initiatives through Discovery, Pilot, Scale and Retire stage-gates.
Run weekly champion triage, biweekly leader reviews and a monthly CEO AI Council readout. Publish scorecards showing progress, delivered value and blockers.
Develop playbooks, a reuse library and reference architectures for internal RAG knowledge assistants, summarization and drafting copilots with guardrails, and workflow automation. Create evaluation templates covering accuracy, quality, safety and adoption. Work with IT and Security on tool approvals and safe-use guidance.
Require baselines, instrumentation and target metrics for initiatives; validate benefits with function owners and track realized value and costs over time. Hire and manage a lean core team of program and solutions or automation leads, supported by the champions network.

Requirements

MBA from a reputed institution and 10–18 years of experience in transformation, business operations, strategy and operations, operational excellence or intelligent automation, ideally in a technology or operations-heavy environment.
Demonstrated cross-functional change leadership without direct authority and measurable results in productivity, cycle time, quality, cost-to-serve or automation adoption. Strong portfolio and program leadership, stakeholder management and communication skills are required, along with applied understanding of LLMs, RAG, workflow automation, evaluation and monitoring, and awareness of security, data governance and auditability.

Full-time position in Bangalore.

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

Skills for this role

AI strategyAI transformationChange managementProgram managementPortfolio managementIntelligent automationWorkflow automationLarge language modelsRAGAI evaluationAI monitoringAI governanceData securityStakeholder managementBenefits realizationOperational excellenceBusiness operationsCommunication

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

Establish the AI Transformation Office
Define its charter, scope and ways of working; establish lightweight governance with IT, Security and functional leaders; and assign one AI Champion per function or line of business with clear responsibilities and time allocation.
Manage the initiative portfolio
Create intake and prioritization based on ROI, feasibility, reuse and risk. Maintain a register of owners, stages, baselines, targets, adoption and risks, with criteria for Discovery, Pilot, Scale and Retire.
Drive execution and leader accountability
Run weekly champion triage, biweekly leader reviews and a monthly CEO AI Council readout. Use scorecards to make delivered value, blockers and decisions visible, and manage a lean core team supporting the champions network.
Make AI adoption reusable and governed
Develop playbooks, a reuse library, reference architectures and evaluation templates for internal RAG assistants, guarded drafting and summarization copilots, and workflow automation. Partner with IT and Security on approvals and safe-use guidance.
Verify sustained operational value
Require baselines, instrumentation and target metrics; validate benefits with function owners; and track realized value against tool spend, engineering effort and support burden over time.

Likely challenges to think through

Accountability without direct authority
Preparation hypothesis: Functional leaders and champions may have competing priorities, while the office depends on their participation. Prepare an example of securing time commitments, resolving blockers and using constructive scorecards to sustain cross-functional change.
Proving value beyond a successful pilot
Preparation hypothesis: Early usage or time-saving estimates may not translate into sustained productivity, quality or cycle-time gains. Prepare to explain how you would establish baselines, measure adoption and outcomes, validate claims with function owners, and account for ongoing costs.
Balancing speed, reuse and safeguards
Preparation hypothesis: Teams may want quick, tailored AI solutions, while the role calls for reusable patterns, evaluation and security approval. Prepare to discuss stage-gate criteria that distinguish a worthwhile pilot from one to scale, redesign or retire.

Practice your answers

Technical practice

How would you define the AI Transformation Office’s charter and boundaries?

What to coverSuggested preparation: Explain what the office would own versus what functional leaders, IT and Security would own. Address how lightweight governance and clear decision rights could support adoption without creating unnecessary delays.

How would you prioritize proposed AI initiatives?

What to coverSuggested preparation: Describe an intake method that compares ROI, feasibility, reuse potential and risk, including how you would handle incomplete estimates. Explain what you would record so owners and leaders can revisit prioritization decisions.

What criteria would you use to move an initiative through Discovery, Pilot, Scale and Retire?

What to coverSuggested preparation: Distinguish the evidence needed at each gate, such as a defined problem, a measurable pilot result, adoption and manageable risks. Include when you would stop an initiative rather than scale it.

How would you establish and validate an initiative’s productivity or quality gains?

What to coverSuggested preparation: Cover baseline selection, instrumentation, target metrics and validation with the function owner. Explain how you would distinguish sustained realized value from a one-time pilot result.

How would you assess the ongoing cost-to-value of an automation initiative?

What to coverSuggested preparation: Compare measured benefits with tool spend, engineering effort and support burden over time. Explain how changing adoption or maintenance needs could affect a decision to continue, improve or retire the initiative.

How would you design a reusable pattern for an internal RAG knowledge assistant?

What to coverSuggested preparation: Discuss source selection, retrieval quality, access controls and ways to evaluate whether answers are grounded in the source material. Explain which components belong in a reference architecture or reuse library and which may need to vary by function.

What guardrails would you put around summarization and drafting copilots?

What to coverSuggested preparation: Address source-data handling, review of outputs and risks such as inaccurate or inappropriate content. Explain how a playbook could make the controls usable across teams without assuming every task has the same risk.

How would you evaluate a workflow-automation opportunity?

What to coverSuggested preparation: Use one of the posting’s examples—approvals, routing, CRM hygiene or reporting—to discuss process fit, exceptions and ownership. Explain how you would weigh cycle-time improvement against implementation effort and operational risk.

What would an evaluation and monitoring template for internal AI initiatives include?

What to coverSuggested preparation: Cover accuracy, quality, safety and adoption, with measures suited to the particular use case. Explain how ongoing monitoring would reveal degradation or low usage after a pilot.

How would you work with IT and Security on tool approvals and safe-use guidance?

What to coverSuggested preparation: Discuss the information needed to assess data exposure, access, auditability and intended use before approval. Explain how you would document an approved pattern and handle a useful proposal that does not yet meet the agreed safeguards.

Behavioural practice

Tell me about a time you led cross-functional change without direct authority.

What to coverSuggested preparation: Use a real situation, the actions you took to align owners with different priorities, and the result. Include a measurable outcome if you have one, without presenting an estimate as a verified benefit.

Describe a time you secured commitment from senior leaders on a stalled initiative.

What to coverSuggested preparation: Set out the blocker, how you framed the decision or trade-off, and what the leaders agreed to do. Close with the result and how you followed up on accountability.

Tell me about a time you coordinated delivery through people outside your core team.

What to coverSuggested preparation: Describe the situation, how you clarified responsibilities and time commitments, and the actions you took when capacity conflicted with other work. State the result and what you learned about sustaining a distributed network.

Describe a time you reported an underperforming program transparently.

What to coverSuggested preparation: Explain the situation, the measures you shared, and how you communicated gaps constructively rather than obscuring them. Describe the decision or corrective action that followed and its result.

Tell me about a time you built or managed a lean team for a broad mandate.

What to coverSuggested preparation: Give the situation, how you chose roles or allocated work, and how you supported delivery beyond the core team. Explain the result, including any limits the team’s capacity placed on priorities.

Sharpen your knowledge

Topics to prepare

AI Transformation Office operating model
The posting calls for a charter, scope boundaries, lightweight governance with IT and Security, a champion per function or line of business, and a lean core team. Revision exercise: explain how responsibilities and decisions would be divided among those groups.
Portfolio prioritization and stage-gates
The role owns intake based on ROI, feasibility, reuse and risk, followed by Discovery, Pilot, Scale and Retire decisions. Revision exercise: compare two hypothetical initiatives against those factors and explain what evidence would justify moving each to the next stage.
Benefits realization
The posting requires baselines, instrumentation, target metrics, function-owner validation and ongoing cost-to-value tracking. Revision exercise: explain how to distinguish a pilot's projected time savings from realized improvements in cycle time or quality.
Reusable AI and automation patterns
The office will develop patterns for internal RAG knowledge assistants, guarded summarization and drafting copilots, and workflow automation. Revision exercise: compare where each pattern fits and how a reuse library could prevent duplicate builds.
Evaluation, monitoring and safe use
The posting names accuracy, quality, safety and adoption evaluation, AI monitoring, tool approvals, data awareness and auditability. Revision exercise: outline the questions IT, Security and a function owner would need answered before scaling an internal assistant.
Change leadership and accountability
The role requires cross-functional influence without direct authority, leader accountability and a weekly, biweekly and monthly review cadence. Revision exercise: describe how to surface a stalled initiative constructively, secure an owner decision and track the unblock.

Skills to revise

Draft an initiative intake and register
Practice creating a sample entry with an owner, stage, baseline, target, adoption measure, risk and prioritization rationale. The posting calls for a single source of truth for these fields.
Set measurable initiative targets
For a hypothetical reporting automation, draft a baseline, target, measurement method and recurring benefits check with a function owner. This practices the posting's requirement to validate realized value over time.
Build an evaluation template
Draft a short template for an internal RAG assistant covering answer accuracy, quality, safety, adoption and monitoring. The posting explicitly calls for evaluation templates and applied understanding of these patterns.
Prepare a one-page executive readout
Using hypothetical initiative results, produce wins, value delivered, blockers, lagging initiatives and decisions needed. This mirrors the specified monthly CEO AI Council readout.
Write a pilot-to-scale decision
For a hypothetical guarded drafting copilot, document pilot results, reuse potential, risks, owner and a Scale or Retire recommendation. This practices the posting's stage-gate and governance responsibilities.

Your preparation checklist

Draft an AI Transformation Office charter
Prepare a sample one-page charter defining scope, decision rights, Security and IT involvement, and the responsibilities and time allocation of one AI Champion per function or line of business. Treat it as a proposal, not greytHR’s existing model.
Build a sample initiative portfolio
Create an intake rubric using ROI, feasibility, reuse and risk; a register with owners, baselines, targets, adoption and risks; and proposed criteria for Discovery, Pilot, Scale and Retire.
Rehearse a measurable transformation walkthrough
Prepare one relevant example, if available, showing how you influenced functions without direct authority, established a baseline, handled blockers and measured sustained changes in productivity, cycle time, quality, cost-to-serve or adoption. State your own contribution clearly.
Work through an internal AI design exercise
Sketch a hypothetical RAG knowledge assistant or guarded drafting copilot: data access, evaluation of accuracy, quality, safety and adoption, monitoring, auditability, and IT and Security approval points. Identify what could be reused rather than rebuilt.
Prepare a benefits and leadership readout
Draft an illustrative one-page CEO AI Council update with wins, validated value, costs, blockers and decisions needed. Show how weekly champion triage and biweekly leader reviews would feed the monthly readout.
Organize evidence against the stated requirements
If applicable, assemble concise evidence of the listed MBA, 10–18 years of relevant experience, portfolio leadership, AI and automation understanding, governance awareness, and experience managing teams and senior stakeholders. Avoid implying experience you cannot substantiate.
Write questions for the employer
Ask how the office’s initial scope and decision rights will be set, which functions have usable baselines, how champions’ time will be allocated, and who validates benefits and approves tools. These are questions to investigate, not assumptions about current practice.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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