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S&C GN - TS&T – Digital Foundations - AI Op Model & Governance - Senior Manager

Accenture · Gurugram, India

About this role

This Senior Manager role in Accenture’s Technology Strategy & Transformation Global Network focuses on helping clients scale AI and data through enterprise operating models and governance. The position is listed as full-time, with locations in Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad.

Responsibilities

Design the structural and process changes needed to move from AI pilots to enterprise-scale operations, including AI Centres of Excellence, organizational design and ways of working.
Develop AI governance frameworks covering policies and guardrails for Responsible AI, model risk, privacy, security, compliance, human oversight and third-party AI usage.
Design enterprise data governance structures, stewardship frameworks, accountability and ownership models, and policies addressing regulations including GDPR, DPDP and CCPA.
Define master data management architecture, golden-record strategies, entity resolution and data-domain ownership using leading MDM platforms.
Establish data-quality processes for profiling, cleansing, validation, monitoring and remediation. Design metadata management, lineage, discovery, business glossaries and enterprise data catalogs using platforms such as Collibra and Alation.

Experience: The detailed job description states 14–20 years; the listing header separately states 12–14 years. No education requirement is specified.

The posting highlights work with G2000 clients, collaboration with strategy and industry specialists, and personalized training in strategy and consulting.

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

Skills for this role

AI operating model designAI governanceResponsible AIModel risk managementPrivacySecurityComplianceHuman oversightData governanceData stewardshipMaster data managementEntity resolutionData qualityData profilingData cleansingData validationMetadata managementData lineageData discoveryBusiness glossaryEnterprise data catalogsCollibraAlationGDPRDPDPCCPAOrganizational design

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

Scale AI beyond pilots
The role calls for structural and process changes that support enterprise-scale AI, including AI Centre of Excellence design, organizational design and ways of working. Preparation suggestion: be ready to explain how an operating model assigns decisions and responsibilities across teams.
Make AI governance workable
The posting calls for pragmatic policies and guardrails covering Responsible AI, model risk, privacy, security, compliance, human oversight and third-party AI usage. Preparation suggestion: frame governance as decisions and controls that teams can apply in practice.
Establish enterprise data accountability
The role includes data governance structures, stewardship, ownership models and policies addressing GDPR, DPDP and CCPA. Preparation suggestion: articulate how accountability is assigned across data domains without treating the named regulations as interchangeable.
Build trusted master data and data quality
The posting specifies MDM architecture, golden-record strategy, entity resolution and domain ownership, alongside profiling, cleansing, validation, monitoring and remediation. Preparation suggestion: connect these design choices to reliable data at enterprise scale.
Enable data discovery and traceability
The role includes metadata management, lineage, discovery, business glossaries and enterprise catalogs, with Collibra and Alation named as examples. Preparation suggestion: explain how these capabilities help people find, understand and govern data.

Likely challenges to think through

Balancing AI adoption with controls
Preparation hypothesis, not a stated employer problem: moving beyond pilots may create tension between faster adoption and meaningful risk controls. Prepare to describe how decision rights, proportionate guardrails and human oversight could work together.
Resolving ownership across an enterprise
Preparation hypothesis: stewardship and domain ownership can be difficult when multiple functions use the same data. Prepare an approach for resolving accountability disputes while accommodating the different regulatory obligations named in the posting.
Turning data designs into sustained practice
Preparation hypothesis: a golden record or catalog has limited value if quality remediation and metadata upkeep are not owned. Prepare to connect architecture choices with ongoing processes, accountable owners and monitoring.

Practice your answers

Technical practice

How would you move an organization from AI pilots to an enterprise-scale operating model?

What to coverSuggested preparation: Explain how you would assess existing pilots, define repeatable decision rights and ways of working, and sequence organizational changes. Discuss the trade-off between shared standards and flexibility for different business areas.

How would you design an AI Centre of Excellence?

What to coverSuggested preparation: Describe its mandate, interfaces with business and technology teams, and accountability for governance versus delivery. Explain how you would determine which decisions belong centrally and which belong with individual teams.

What would a pragmatic Responsible AI and model-risk governance framework contain?

What to coverSuggested preparation: Cover policies, risk-based guardrails, review responsibilities and evidence of compliance across the model lifecycle. Explain how you would make controls usable without weakening oversight.

How would you govern third-party AI usage and human oversight?

What to coverSuggested preparation: Discuss how you would assess external AI use against privacy, security and model-risk concerns, then assign approval and ongoing oversight responsibilities. Explain when a human should review or override an AI-supported decision.

How would you establish enterprise data ownership and stewardship across jurisdictions?

What to coverSuggested preparation: Define the respective responsibilities of data owners, stewards and governance bodies, including how disputes are resolved. Explain how policies would account for applicable GDPR, DPDP and CCPA obligations without assuming identical requirements.

How would you design an MDM architecture and golden-record strategy?

What to coverSuggested preparation: Explain how you would choose data domains, assign ownership and determine authoritative sources and record-survivorship rules. Address trade-offs between central consistency and domain-specific needs.

How would you approach entity resolution when source records disagree?

What to coverSuggested preparation: Discuss matching criteria, confidence thresholds and review of ambiguous matches. Explain how errors would be detected and corrected without treating every apparent match as the same entity.

How would you build a data-quality framework that supports trusted data at scale?

What to coverSuggested preparation: Connect profiling, cleansing, validation, monitoring and remediation to clear ownership. Explain how you would prioritize defects by business impact and verify that fixes persist.

How would you make metadata, lineage and a data catalog useful to enterprise users?

What to coverSuggested preparation: Describe how a business glossary, discovery and lineage would help users understand and trace data. Discuss stewardship and adoption considerations when implementing catalog capabilities with platforms such as Collibra or Alation.

Behavioural practice

Tell me about a time you led a change to an operating model across teams.

What to coverSuggested preparation: Use a real situation, the changes you personally led and the result. Explain how you handled resistance and clarified responsibilities.

Describe a time stakeholders disagreed about data ownership or accountability.

What to coverSuggested preparation: Set out the competing views, how you brought the relevant owners together and the decision reached. State the result and any remaining trade-off.

Tell me about a time you had to balance a business objective with a governance risk.

What to coverSuggested preparation: Give a real example of the risk, the options you presented and your action. Explain the resulting decision and how you evaluated its impact.

Describe a time you collaborated with specialists from different disciplines on a client problem.

What to coverSuggested preparation: Identify the situation, each discipline’s contribution and how you integrated differing recommendations. Focus on your actions and the outcome rather than claiming sole credit.

Tell me about a time you discovered a significant data-quality or governance issue during delivery.

What to coverSuggested preparation: Describe how you established its impact, involved accountable stakeholders and organized remediation. State the result, including how recurrence was monitored if applicable.

Sharpen your knowledge

Topics to prepare

Enterprise AI operating models
Posting: The role defines structural and process changes to move AI from pilots to enterprise scale, including an AI Centre of Excellence, organizational design and ways of working. Suggested revision exercise: compare two possible operating-model structures and explain how each would assign decisions and coordinate teams.
AI governance and risk
Posting: Governance frameworks span Responsible AI, model risk, privacy, security, compliance, human oversight and third-party AI usage. Suggested revision exercise: outline how policy, guardrails and oversight would fit together across an AI system’s lifecycle.
Enterprise data governance
Posting: The role covers data stewardship, accountability, ownership and policies addressing GDPR, DPDP and CCPA. Suggested revision exercise: explain the distinct responsibilities of a data owner and steward, then identify where each regulation would need consideration in a governance review.
Master data management
Posting: The role calls for MDM architecture, golden-record strategy, entity resolution and data-domain ownership. Suggested revision exercise: explain the design decisions involved in reconciling conflicting customer records and assigning ownership of the resulting master record.
Data quality frameworks
Posting: The role covers profiling, cleansing, validation, monitoring and remediation to support trusted data at scale. Suggested revision exercise: trace how a recurring data-quality defect would move through those stages.
Metadata and discoverability
Posting: The role covers metadata management, lineage, discovery, business glossaries and enterprise data catalogs, including platforms such as Collibra and Alation. Suggested revision exercise: explain how a glossary term, catalog entry and lineage record would help users understand one critical data element.

Skills to revise

Draft an AI operating-model blueprint
Suggested hands-on exercise: create a one-page blueprint showing an AI Centre of Excellence, decision rights, handoffs and ways of working from pilot through enterprise operation. Relevance: the posting explicitly calls for these structural and process designs.
Build an AI governance control matrix
Suggested hands-on exercise: map a hypothetical AI use case to proposed guardrails, accountable reviewers and human-oversight points for model risk, privacy, security and third-party usage. Relevance: the role designs pragmatic governance frameworks across those areas; the matrix is a practice artifact, not a specified employer template.
Map data stewardship and accountability
Suggested hands-on exercise: draw an ownership map for two data domains, showing who sets policy, stewards definitions and resolves cross-domain disputes. Relevance: the posting calls for enterprise data governance structures and accountability models.
Work through a golden-record scenario
Suggested hands-on exercise: take a small set of conflicting entity records, propose matching and survivorship rules, and document who owns exceptions. Relevance: the posting names entity resolution, golden-record strategy and data-domain ownership.
Design a data-quality remediation loop
Suggested hands-on exercise: define profiling checks, validation rules, monitoring measures and an escalation path for one defective dataset. Relevance: the role establishes data-quality frameworks covering detection through remediation.
Sketch a catalog and lineage example
Suggested hands-on exercise: document a business term, its data owner, source-to-report lineage and a discoverable catalog entry; explain how it could be represented in Collibra or Alation if familiar. Relevance: the posting explicitly names these metadata capabilities and platforms.

Your preparation checklist

Map the role to your evidence
Create a one-page matrix covering AI operating models, AI and data governance, MDM, data quality and metadata. For each area, note a relevant example you can discuss, if available, and any gap to prepare for.
Prepare an enterprise AI scaling walkthrough
Outline an anonymized project example, if available, showing how you approached the move from pilots to enterprise operations. Explain the proposed AI Centre of Excellence, organizational changes, ways of working, trade-offs and outcomes.
Practice an AI governance design exercise
Draft a sample framework with policy owners, decision points and guardrails for Responsible AI, model risk, privacy, security, compliance, human oversight and third-party AI usage. Be ready to explain how it would work in practice.
Sketch a data operating model
Produce a simple diagram of data ownership, stewardship, accountability and escalation. Explain how you would address GDPR, DPDP and CCPA considerations without assuming the same controls fit every client.
Work through a trusted-data scenario
Prepare a short design for a sample data domain covering MDM architecture, golden records, entity resolution and domain ownership. Add a data-quality workflow from profiling through monitoring and remediation.
Show how metadata supports governance
Sketch how lineage, discovery, a business glossary and an enterprise catalog would fit the scenario. Be ready to discuss where platforms such as Collibra or Alation could help, without treating either as a required tool.
Bring focused questions for the employer
Ask how this Senior Manager balances strategy and implementation across AI and data governance, and how the Global Network collaborates with client and industry teams. Clarify the experience range: the page header says 12–14 years, while the detailed description says 14–20.

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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