New opportunity

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

Accenture · Gurugram, India

About this role

Accenture is hiring a full-time consultant in its Technology Strategy & Transformation Global Network 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. The posting specifies 5–9 years of experience; its job header lists 5–10 years.

Responsibilities

Design enterprise AI operating models, including AI Centres of Excellence, organizational structures, ownership models, operating cadence and ways of working. Build pilot-to-scale processes for use-case prioritization, value tracking, model ownership, MLOps/LLMOps handoffs and business adoption.
Develop AI governance policies and controls covering Responsible AI, model risk, privacy, security, human oversight and third-party AI. Establish risk classification, approvals, testing, monitoring, incident response and review processes for generative and traditional AI.
Design data governance, stewardship, ownership and quality frameworks. Support master data and golden-record strategies, entity resolution, metadata management, lineage, discovery, business glossaries and catalogs. Conduct data capability maturity assessments and address regulatory requirements including GDPR, DPDP and CCPA.
Advise on cloud data platform strategy and engage executives through discovery workshops, recommendations, roadmaps and investment cases. Manage budgeting, forecasting and proposal responses for AI operating model engagements.

Qualifications and experience:

The posting requests 3–5 years of strategy consulting experience; 2+ years of relevant data governance, data management or AI operating model project experience; at least 2+ years designing data quality, MDM, metadata management and data catalog solutions; and 2+ years leading or managing teams.
Requires workshop facilitation, stakeholder alignment, data transformation value frameworks, enterprise data architecture knowledge, experience with a data governance platform, and experience with at least two MDM technologies such as Informatica MDM, Reltio or SAP MDG.
An MBA from a tier 1 institute, industry expertise, DAMA CDMP certification and cloud Data & AI certifications are preferred or desirable. Foundational knowledge of data privacy and Responsible AI is desired. The posting highlights work with G2000 clients and personalized training.

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 managementData governanceData stewardshipData qualityMaster data managementMetadata managementData lineageData catalogData observabilityEntity resolutionMLOpsLLMOpsCloud data platformsGoogle BigQueryAzure SynapseAWS RedshiftCollibraAlationTalendInformaticaSAP MDGStiboAlteryxReltioGDPRDPDPCCPA

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

Move AI from pilots to enterprise operations
The role designs AI Centres of Excellence, ownership models, operating cadences and pilot-to-scale processes covering use-case prioritization, value tracking, model ownership, MLOps/LLMOps handoffs and adoption. Prepare to explain how these pieces form a workable target operating model.
Turn AI risk principles into operating controls
The posting calls for governance across Responsible AI, model risk, privacy, security, human oversight and third-party AI. Focus preparation on risk classification, approvals, testing, monitoring, incident response and review for both generative and traditional AI.
Establish trusted enterprise data foundations
Duties span data stewardship and ownership, quality monitoring and remediation, MDM and golden records, metadata, lineage and catalogs. Prepare to connect these capabilities to accountability and data capability maturity assessments, rather than treating tools as the outcome.
Connect platform strategy to governance design
The role requires an understanding of cloud data platforms, migrations, data lakes and cloud data warehouses, alongside practical data architecture knowledge. Prepare to show how platform choices affect ownership, quality, lineage and access controls.
Lead executive decisions and engagement delivery
The consultant leads discovery and design sessions, develops recommendations, roadmaps and investment cases, and engages CIO, CTO, CDAO and business leaders. The posting also assigns budgeting, forecasting and proposal responses; prepare to communicate decisions and value clearly at that level.

Likely challenges to think through

Scale without obscuring accountability
Preparation hypothesis, not a confirmed client problem: centralized standards may help consistency while federated teams need room to deliver. Prepare a decision framework for ownership, governance forums and handoffs that keeps model and business accountability clear.
Balance adoption speed with proportionate controls
Preparation hypothesis: extensive reviews can slow AI use cases, while weak controls leave model, privacy or output risks unmanaged. Prepare to explain risk-tiered approvals and monitoring, including where human oversight belongs.
Sequence data work against near-term value
Preparation hypothesis: quality, master data and metadata gaps may compete with pressure to demonstrate AI value quickly. Prepare a phased roadmap that ties priority use cases to specific data remediation, platform dependencies and measurable value.
Align stakeholders under uncertain scope
Preparation hypothesis: business, technology, data and risk leaders may disagree on the target state or investment order. Prepare to structure a discovery workshop around explicit decisions, trade-offs, roadmap options and an investment case rather than assuming consensus.

Practice your answers

Technical practice

How would you design an enterprise AI operating model that balances a Centre of Excellence with business-unit ownership?

What to coverSuggested preparation: Explain how you would assign decision rights, model ownership and responsibilities across business, data, technology, risk and legal teams. Compare centralized and federated approaches, including how operating cadence and ways of working would change at scale.

How would you move an AI use case from pilot to enterprise-scale operation?

What to coverSuggested preparation: Cover intake and prioritization, value tracking, lifecycle ownership, MLOps or LLMOps handoffs, and business adoption. Explain what evidence would justify scaling and how you would handle a pilot that does not deliver its expected value.

How would you classify AI use-case risk and turn policy into workable approvals?

What to coverSuggested preparation: Describe a triage approach that considers Responsible AI, model risk, privacy, security, human oversight and third-party usage. Show how classification would determine documentation, testing, approval roles and review frequency without creating unnecessary friction.

What governance controls would you adapt for generative AI versus traditional AI?

What to coverSuggested preparation: Compare relevant controls for data access, prompt and output risk, explainability, fairness, traceability and lifecycle accountability. Explain how monitoring, incident response and periodic review would feed back into the governance process.

How would you establish data ownership and assess data-governance maturity?

What to coverSuggested preparation: Define the roles of domain owners, stewards and governance forums, then describe how you would assess current capabilities and prioritize gaps. Include how privacy requirements such as GDPR, DPDP or CCPA would be translated into ownership and operating processes, without assuming one design fits every client.

How would you design a golden-record and entity-resolution strategy?

What to coverSuggested preparation: Discuss domain ownership, matching and survivorship decisions, reference data, and how exceptions would be resolved. Explain how you would evaluate MDM platform fit, including trade-offs among technologies named in the posting such as Informatica MDM, Reltio and SAP MDG.

How would you improve data quality across a large organization?

What to coverSuggested preparation: Walk through profiling, quality rules, cleansing, validation, monitoring and remediation ownership. Explain how you would prioritize issues by business impact and measure whether the data has become more trustworthy.

How would you make metadata, lineage and a data catalog useful to business and technical teams?

What to coverSuggested preparation: Connect a business glossary and data discovery to lineage, stewardship and practical use of a catalog. Discuss adoption and maintenance trade-offs when implementing capabilities with platforms such as Collibra or Alation.

How would you advise on a cloud data-platform strategy for an AI and data transformation?

What to coverSuggested preparation: Explain how you would assess the current architecture, migration objectives and fit of a cloud warehouse or lake approach. Compare relevant considerations such as integration, governance and sequencing rather than assuming BigQuery, Azure Synapse or AWS Redshift is always the right choice.

How would you turn discovery findings into a funded Data and AI roadmap?

What to coverSuggested preparation: Show how you would structure hypotheses, prioritize opportunities, define value measures and sequence operating-model and technology changes. Explain how assumptions, costs and risks would appear in an investment case, forecast or proposal.

Behavioural practice

Tell me about a time you led a cross-functional discovery workshop amid unclear requirements.

What to coverSuggested preparation: Use a real example to set out the situation, how you structured the discussion and drew out differing perspectives, and what decisions or recommendations resulted. Focus on your facilitation actions rather than only the final presentation.

Tell me about a time stakeholders disagreed over ownership or governance controls.

What to coverSuggested preparation: Describe the competing priorities, the action you took to clarify decision rights or trade-offs, and the resulting agreement or unresolved issue. A relevant example could involve business, data, technology, risk or legal stakeholders.

Tell me about a time you led a team through an analytical workstream or proof of concept.

What to coverSuggested preparation: State the objective, how you divided and reviewed the work, and the result. Highlight a concrete decision you made when the team encountered an obstacle or new evidence.

Tell me about a time you translated an ambiguous data or AI problem into an executive recommendation.

What to coverSuggested preparation: Explain the initial uncertainty, the hypotheses and evidence you used, and how you communicated options to senior stakeholders. Close with the decision or measurable outcome, while distinguishing your contribution from the wider team's.

Tell me about a time you had to balance scope, budget and expected value in a client engagement.

What to coverSuggested preparation: Give the situation, the forecast or proposal trade-offs you identified, the action you took, and the result. Be clear about how you communicated assumptions and handled changes to the plan.

Sharpen your knowledge

Topics to prepare

Enterprise AI operating models
Compare centralized and federated ownership for a hypothetical AI Centre of Excellence, then explain how use-case intake, value tracking, model ownership and MLOps/LLMOps handoffs support pilot-to-scale adoption. These are explicit operating-model responsibilities.
AI governance and model risk
Trace a hypothetical generative AI use case through risk classification, approval, testing, monitoring, incident response and periodic review; contrast its prompt and output risks with those of traditional AI. The posting calls for controls covering Responsible AI, privacy, security, human oversight and third-party AI.
Data ownership, stewardship and quality
For a hypothetical data domain, distinguish owner and steward accountabilities, then explain how profiling, validation, monitoring and remediation fit into a governance framework. The role explicitly covers these disciplines and data capability maturity assessments.
Master data and entity resolution
Explain how reference data, matching rules, entity resolution and domain ownership contribute to a golden record; compare design considerations for two MDM technologies named in the posting, such as Informatica MDM and Reltio. The role requests MDM solution-design experience and experience with at least two MDM technologies.
Metadata and data architecture
Sketch how lineage, discovery, a business glossary and a catalog help users understand a data asset, then relate that picture to Data Mesh or API-first architecture. The posting explicitly names these capabilities and enterprise architecture concepts.
Cloud data platform strategy
Compare when a data lake or cloud data warehouse might serve a hypothetical migration, using BigQuery, Azure Synapse or Redshift only as platforms named in the posting. The role calls for understanding data-on-cloud strategy and cloud data platforms, not a specific migration project.

Skills to revise

Facilitate a discovery workshop
Practise a 20-minute mock session: elicit a business pain point, ask business, data, technology and risk stakeholders to clarify ownership, and close with agreed decisions and open questions. Cross-functional workshops and stakeholder alignment are explicit requirements.
Draft an AI operating-model blueprint
Create a one-page hypothetical design showing an AI Centre of Excellence, decision rights, operating cadence, use-case intake and model handoffs. This rehearses the target operating-model design the consultant would advise on.
Build an AI governance control matrix
For one hypothetical AI use case, map risks to an accountable role, approval, evidence of testing, monitoring trigger and incident path. This is a practice exercise for the posting’s governance processes, not a prescribed Accenture template.
Design a data-quality and MDM solution
Given a hypothetical duplicate-customer-data problem, write profiling checks, match and merge decisions, golden-record ownership, quality measures and remediation steps. The posting specifically requests solution design across data quality and master data.
Map a catalog and lineage workflow
Draw a source-to-report lineage example and draft glossary terms, catalog fields and stewardship actions; explain how Collibra or Alation could support the workflow without assuming access to either. Metadata management and catalog design are explicit requirements.
Present a phased recommendation
Turn a hypothetical discovery finding into a short executive presentation with a target state, sequenced roadmap, value measures, investment assumptions and delivery risks. This rehearses the posting’s executive advisory, value-realization, budgeting and proposal responsibilities.

Your preparation checklist

Prepare an AI operating-model walkthrough
The role covers moving AI from pilots to enterprise scale. Prepare a real, anonymized project walkthrough if available, or a clearly labeled hypothetical design showing Centre of Excellence structure, ownership, operating cadence, use-case intake, value tracking, and MLOps/LLMOps handoffs.
Draft an AI governance design exercise
The posting calls for controls across Responsible AI, model risk, privacy, security, human oversight, and third-party AI. Sketch a sample use-case path from risk classification and approval through testing, monitoring, incident response, and periodic review; distinguish GenAI-specific controls.
Assemble data-management evidence
Prepare concise examples, where available, of data stewardship, quality profiling and remediation, MDM and golden-record design, and metadata or catalog work. Be ready to explain design decisions and results without disclosing confidential client information.
Map your platform experience to the stated requirements
The posting requests experience with a data governance platform and at least two MDM technologies. Make a factual comparison of tools you have used, their roles in the architecture, and your own contribution; leave gaps explicit rather than implying hands-on experience.
Practice a cloud and data-architecture discussion
The role includes cloud data platform strategy and enterprise architecture. Prepare a short, hypothetical target-state diagram covering data flows, ownership, lineage, quality controls, and a cloud platform choice, with trade-offs and assumptions labeled.
Rehearse an executive workshop and investment case
The posting emphasizes discovery workshops, stakeholder alignment, roadmaps, value realization, budgeting, and proposals. Outline questions for a CIO or CDAO, a way to prioritize opportunities, a phased roadmap, and the inputs you would need to estimate investment.
Select consulting and team-leadership examples
The posting specifies strategy consulting and team-management experience. Prepare factual examples, if available, showing how you structured ambiguous work, facilitated cross-functional decisions, planned a team's analysis, and turned findings into recommendations.
Write questions for the employer
Ask which AI operating-model and data-governance challenges dominate current engagements, how consultants divide responsibility across strategy and implementation, and what outcomes matter most for this consultant role.

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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