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

Accenture · Gurugram, India

About this role

Accenture seeks a consultant in its Technology Strategy & Transformation Global Network practice to help clients scale AI and data initiatives through operating-model design, governance and data management. The role is listed for Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata or Hyderabad, and calls for 5–9 years of experience; the job header states 5–10 years.

Responsibilities

Design enterprise AI operating models, including AI Centres of Excellence, organizational structures, ownership models, operating cadence and ways of working across business, data, technology, risk and legal teams. Build pilot-to-scale processes for use-case prioritization, value tracking, lifecycle management, MLOps/LLMOps handoffs and adoption.
Develop AI governance and risk frameworks covering Responsible AI, privacy, security, compliance, human oversight and third-party AI. Establish risk classification, approvals, documentation, testing, monitoring and incident-response processes; advise on generative and traditional AI controls, including bias, explainability, prompt and output risk.
Design data governance, stewardship, ownership and quality frameworks, including regulatory compliance. Support master data and golden-record strategies, entity resolution, metadata management, lineage, discovery and enterprise data catalogs. Assess data capability maturity and advise on cloud data-platform strategy.
Lead client workshops and executive discussions; turn findings into recommendations, roadmaps and investment cases. Engage senior technology and business leaders, manage budgets and forecasts, and own proposal responses for AI operating-model engagements.

Qualifications

The posting specifies 3–5 years of strategy consulting experience and 2+ years of relevant project experience across data governance, management and AI operating-model topics. It also calls for at least 2+ years designing solutions for data quality, MDM, metadata management and data catalogs, plus 2+ years leading or managing teams.
Experience with a data-governance technology such as Collibra, Alation, Talend, Informatica, SAP MDG, Stibo or Alteryx, and at least two MDM technologies such as Informatica MDM, Reltio or SAP MDG. Requires workshop leadership, stakeholder engagement, data-transformation value frameworks and knowledge of enterprise data architecture.

Preferred or desirable: An MBA from a tier 1 institute, understanding of data privacy and Responsible AI, relevant industry expertise, DAMA CDMP certification, and Azure, AWS or Google Cloud Data & AI practitioner certifications. The posting mentions training and opportunities to work with major global clients.

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

Skills for this role

AI operating modelsAI governanceResponsible AIModel risk managementData governanceData stewardshipData qualityMaster data managementMetadata managementData lineageData catalogsData observabilityEntity resolutionData profilingData cleansingAI Centre of Excellence designMLOpsLLMOpsGenerative AIGDPRDPDPCCPACollibraAlationTalendInformaticaSAP MDGStiboAlteryxReltio

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 through an operating model
The role defines AI Centre of Excellence structures, ownership, operating cadence and ways of working across business, data, technology, risk and legal teams. It also covers use-case prioritization, value tracking, MLOps/LLMOps handoffs and adoption.
Make AI governance operational
The posting calls for Responsible AI and model-risk guardrails, including risk classification, approvals, testing, monitoring and incident response. Controls span traditional and generative AI, privacy, security, human oversight and third-party use.
Design trusted enterprise data foundations
Responsibilities include data ownership and stewardship, quality controls, MDM and golden records, metadata, lineage and catalogs. The role also assesses data capability maturity and advises on cloud data-platform strategy.
Turn executive discussions into decisions
The consultant leads discovery workshops and executive design sessions, aligns business and technology stakeholders, and converts findings into recommendations, roadmaps and investment cases.
Lead consulting delivery and commercial work
The posting calls for team leadership, including structuring analytical work and developing recommendations or POCs, as well as managing budgets and forecasts and owning proposal responses for AI operating-model engagements.

Likely challenges to think through

Balancing AI adoption with proportionate controls
Preparation hypothesis, not a confirmed client problem: uniform approvals could slow lower-risk use cases, while weak controls could leave higher-risk uses exposed. Prepare an example of risk-tiered decision rights, evidence requirements and monitoring that preserve a path from pilot to scale.
Resolving ownership across functions
Preparation hypothesis, not a confirmed client problem: centralized standards and domain-level accountability may pull in different directions. Prepare to explain how you would establish ownership, escalation forums and handoffs across business, data, technology, risk and legal teams.
Sequencing data foundations against near-term value
Preparation hypothesis, not a confirmed client problem: improving master data, quality and lineage can take time while stakeholders seek visible AI outcomes. Prepare a phased roadmap that links targeted data remediation to prioritized use cases, measurable value and investment decisions.

Practice your answers

Technical practice

How would you design an AI operating model that can scale beyond pilots?

What to coverThe posting calls for AI Centre of Excellence design, organizational roles, operating cadence and cross-functional ways of working. Suggested preparation (inference): explain when centralized versus federated ownership makes sense and how you would assign decisions across business, data, technology, risk and legal teams.

How would you move an AI use case from intake to sustained business adoption?

What to coverThe role covers prioritization, value tracking, lifecycle management, model ownership and MLOps/LLMOps handoffs. Suggested preparation (inference): walk through decision points from pilot to scale, including how to check value and establish operational ownership.

What processes would you put in an enterprise AI governance framework?

What to coverThe posting specifies risk classification, approvals, documentation, testing, monitoring, incident response and periodic review. Suggested preparation (inference): describe how the level of review could vary by risk while preserving clear accountability and human oversight.

How would you distinguish controls for generative AI from those for traditional AI?

What to coverThe role names data access, prompt and output risk, explainability, bias, fairness and traceability. Suggested preparation (inference): compare where controls apply across the two model types and how monitoring and escalation would work.

How would you assign enterprise data ownership and stewardship?

What to coverThe posting calls for domain-based roles, governance forums, policies, accountability and regulatory compliance. Suggested preparation (inference): explain how owners and stewards would resolve quality or access issues, and where privacy requirements such as GDPR or DPDP affect decisions.

How would you design a master-data and golden-record approach?

What to coverThe role covers MDM architecture, entity resolution, reference data and domain ownership, and asks for experience with multiple MDM technologies. Suggested preparation (inference): discuss source reconciliation, match decisions, stewardship and criteria for comparing relevant platform options without assuming a particular client stack.

How would you establish and sustain data quality at scale?

What to coverThe posting specifies profiling, cleansing, validation, monitoring and remediation. Suggested preparation (inference): describe how you would set quality measures with data owners, diagnose recurring defects and verify that remediation holds.

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

What to coverThe role includes data discovery, business glossaries, lineage and catalog capabilities, with Collibra and Alation named as examples. Suggested preparation (inference): explain how you would prioritize metadata coverage, maintain it and assess whether users can find and understand trusted data.

How would you assess a client's data-platform strategy for a cloud migration?

What to coverThe posting mentions data-on-cloud migrations, data lakes, cloud data warehouses and platforms such as BigQuery, Synapse and Redshift. Suggested preparation (inference): frame the choice around business needs, existing architecture, governance and migration trade-offs rather than prescribing one platform.

How would you turn a data capability assessment into a transformation roadmap?

What to coverThe role calls for maturity assessments, data-transformation value frameworks, roadmaps and investment cases. Suggested preparation (inference): explain how findings would lead to sequenced changes, measurable outcomes and decisions for executive review.

Behavioural practice

Tell me about a workshop where stakeholders disagreed on a data or AI priority.

What to coverThe posting calls for leading cross-functional discovery and design workshops. Suggested preparation (inference): use a real situation/action/result example showing how you structured the discussion, handled disagreement and reached a decision or clear next step.

Describe a time you influenced senior leaders on an operating-model or governance decision.

What to coverThe role involves executive design sessions and engagement with technology and business leadership. Suggested preparation (inference): give a real situation/action/result example that shows the evidence you presented, the trade-offs you explained and the outcome.

Give an example of leading a team through an ambiguous analytical assignment.

What to coverThe posting asks for team leadership, work planning, workshops and strategy recommendations. Suggested preparation (inference): use a real situation/action/result example covering how you divided the work, supported the team and turned findings into a recommendation.

Tell me about a time you translated unclear client needs into a practical roadmap.

What to coverThe role calls for hypothesis-driven problem structuring and actionable recommendations. Suggested preparation (inference): give a real situation/action/result example showing how you tested assumptions, prioritized actions and defined how progress would be measured.

Describe a time you managed a consulting engagement's commercial constraints.

What to coverThe posting includes budgeting, forecasting and ownership of proposal responses for AI operating-model engagements. Suggested preparation (inference): use a real situation/action/result example involving a budget, forecast or proposal, and explain how you balanced scope, effort and client value.

Sharpen your knowledge

Topics to prepare

Enterprise AI operating models
Suggested exercise: Compare centralized and federated AI ownership, including Centre of Excellence responsibilities, operating cadence and handoffs from pilots to production. Relevance: The posting explicitly calls for AI operating-model design and pilot-to-scale processes.
AI governance and model risk
Suggested exercise: Map a hypothetical AI use case through risk classification, approval, testing, monitoring and incident response; distinguish GenAI prompt and output risks from traditional model risks. Relevance: These controls, alongside human oversight and Responsible AI, are named in the posting.
Data governance and stewardship
Suggested exercise: Define the differences between a data owner and a steward, then explain how a governance forum would resolve a cross-domain quality issue. Relevance: The role designs ownership, accountability, policy and stewardship frameworks, including regulatory compliance.
Master data and data quality
Suggested exercise: Explain how entity resolution produces a golden record and how profiling, validation, monitoring and remediation keep it trustworthy. Relevance: The posting calls for MDM and data-quality solution design, including at least two MDM technologies.
Metadata and enterprise data architecture
Suggested exercise: Trace a data element from source to report, identifying where lineage, a business glossary and a catalog help users understand it. Relevance: The posting names metadata management, discovery, catalogs and practical enterprise data architecture.
Cloud data-platform strategy
Suggested exercise: Compare decision factors for a data-on-cloud migration and a cloud data warehouse, using BigQuery, Azure Synapse or AWS Redshift only as examples. Relevance: The posting asks for data-platform strategy and familiarity with these platforms.

Skills to revise

Draft an AI operating-model blueprint
Suggested exercise: Create a one-page design showing decision rights, an AI Centre of Excellence, use-case intake, value tracking and MLOps/LLMOps handoffs. Relevance: These are explicit design responsibilities for scaling AI initiatives.
Build a governance workflow
Suggested exercise: Draft a use-case triage checklist and approval flow with named roles, required documentation, review points and escalation for an incident. Relevance: The posting requires actionable AI governance processes, not just policy principles.
Work through a data-quality and MDM case
Suggested exercise: Given a small hypothetical customer dataset, specify profiling checks, match rules, golden-record decisions and remediation ownership; compare how two posting-named MDM technologies could support the design at a conceptual level. Relevance: The qualifications specify solution design across data quality and MDM and experience with at least two MDM technologies.
Outline a metadata and catalog solution
Suggested exercise: Sketch a business glossary entry, ownership fields and source-to-report lineage for one data element; identify what a catalog such as Collibra or Alation would need to capture. Relevance: The posting specifies metadata, lineage and catalog solution design and governance-technology experience.
Facilitate an executive design discussion
Suggested exercise: Prepare a short workshop agenda, five discovery questions and a recommendation with a phased roadmap and measurable value case. Relevance: The role leads workshops and executive sessions and turns ambiguous findings into recommendations, roadmaps and investment cases.
Structure an engagement proposal
Suggested exercise: Outline the scope, deliverables, assumptions, staffing and a simple budget forecast for a hypothetical AI operating-model engagement. Relevance: The posting explicitly includes budgeting, forecasting and ownership of proposal responses.

Your preparation checklist

Prepare an AI operating-model walkthrough
The role covers moving AI pilots to enterprise scale. If you have a relevant project, prepare a concise walkthrough showing the starting problem, Centre of Excellence or ownership choices, cross-functional ways of working, pilot-to-scale handoffs, and outcomes you can substantiate.
Draft an AI governance design exercise
The posting calls for Responsible AI and model-risk controls. As interview practice, sketch how a sample use case would move through risk classification, approval, testing, human oversight, monitoring, and incident response; distinguish generative-AI prompt and output risks.
Assemble data-management evidence
The posting specifically asks for solution-design experience in data quality, MDM, metadata management, and catalogs. Prepare examples, where available, of ownership, golden records, entity resolution, lineage, and remediation, with your contribution and results clearly identified.
Map technology choices to a data architecture
The role names governance and MDM technologies and discusses cloud data platforms. Prepare a comparison of tools you have actually used and a simple architecture sketch showing where a catalog, quality controls, MDM, and cloud platform would fit. Do not imply hands-on experience you lack.
Rehearse an executive workshop and roadmap
The role involves discovery sessions and recommendations for senior leaders. Practice a short agenda, questions that surface business pain points, a prioritization approach, and a phased roadmap with value measures and decisions requiring stakeholder agreement.
Prepare consulting leadership examples
The posting mentions team leadership, budgeting, forecasting, and proposal responses. If applicable, prepare brief examples showing how you structured team work, handled a budget or forecast, and developed a proposal; specify your own responsibilities rather than a team's collective work.
Write targeted questions for Accenture
Bring questions about how this consultant role balances AI operating-model work with data governance, how client teams decide between centralized and federated ownership, and what deliverables and decision authority are expected in engagements.

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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