New opportunity

S&C GN - TS&T – Digital Foundations - AI & Data Strategy - Manager

Accenture · Gurugram, India

About this role

Accenture seeks a Manager in its Technology Strategy & Transformation Global Network practice to help clients develop enterprise AI and data strategies, identify high-value opportunities, and scale AI-enabled business transformation. The role is listed for Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata or Hyderabad. The posting states 10–15 years of experience in its detailed requirements; its job header lists 10–12 years.

Responsibilities

Assess AI opportunities and data readiness through use-case identification, value mapping, industry benchmarking, data maturity reviews, infrastructure analysis and gap assessments.
Design enterprise AI and data strategies covering AI foundations, platform and cloud architecture, data architecture, product development lifecycle and scalable solutions. Evaluate existing AI and data products and frameworks.
Develop AI roadmaps, value frameworks, prioritisation approaches and quantitative and qualitative business cases. Lead discovery workshops, design sessions and executive discussions.
Establish data governance frameworks, ownership, standards and policies; assess data capability maturity and design data operating models. Advise on cloud migration, data lakes and cloud data warehouses.
Address Responsible AI, regulatory compliance and ethical considerations. Manage engagement budgets, forecasts and financial proposals, and guide teams developing strategy recommendations and proofs of concept.

Experience and qualifications:

The posting calls for 5–7 years of strategy consulting experience; 3+ years on projects involving AI opportunity and value assessments, data and AI readiness, AI architecture, roadmaps, business cases and executive engagement; 3+ years designing end-to-end enterprise AI and data solutions; and 3+ years leading or managing teams.
Requires practical knowledge of IT and enterprise architecture and trends such as cloud, API-first, Data Mesh and Digital Core. Candidates should understand traditional, generative and agentic AI, advanced cognitive methods, the data supply chain and AI value-realisation frameworks. Experience with data governance technologies and an industry such as financial services, retail, consumer goods, telecom or life sciences is sought.
An MBA from a tier 1 institute and DAMA CDMP certification are preferred. Cloud AI/data practitioner certifications are desirable but not essential; foundational understanding of data privacy and Responsible AI frameworks is desired.

The role offers work with G2000 clients and personalised strategy and consulting training.

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

Skills for this role

AI strategyData strategyAI opportunity assessmentAI use case identificationAI value assessmentAI readiness assessmentData maturity assessmentAI architectureData architectureCloud architectureData governanceData qualityData platform strategyCloud migrationBusiness case developmentStrategic roadmappingStakeholder engagementResponsible AIGenerative AIAgentic AIData MeshGCPVertex AIBigQueryAzure AIAWS BedrockSnowflakeDatabricksPalantirCollibra� 天天中彩票网站

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

Identify measurable AI opportunities
The role calls for use-case identification, value mapping, industry benchmarking and AI value diagnostics. Prepare to explain how you would distinguish high-value opportunities from appealing but weakly evidenced ideas.
Assess readiness and design scalable foundations
The posting pairs data maturity, infrastructure and gap assessments with enterprise AI and data architecture, platform strategy and cloud architecture. Prepare a clear approach for moving from current-state findings to architecture choices.
Build a fundable transformation roadmap
The manager develops prioritisation approaches, value frameworks, roadmaps and quantitative and qualitative business cases for executive stakeholders. Focus preparation on sequencing investments and making assumptions explicit.
Embed governance and Responsible AI
The role covers data ownership, standards, policies, operating models, data quality, regulatory compliance and ethical AI considerations. Prepare to show how these shape strategy and delivery rather than sit apart from them.
Lead client and team delivery
The posting calls for discovery workshops, design sessions and C-level discussions, alongside guiding teams, managing budgets and forecasts, and building financial proposals. Prepare examples of structuring analytical work and communicating recommendations.

Likely challenges to think through

Value versus readiness
A likely challenge is that a promising use case may depend on data or infrastructure that is not ready. Prepare to explain how you would validate value, expose dependencies and adjust the roadmap without losing sight of the business outcome.
Scale versus current-state constraints
Enterprise-wide architecture must account for existing products, frameworks and technology landscapes. Prepare to compare platform options against integration, governance and operating-model needs, rather than treating a preferred technology as the answer.
Executive ambition versus a defensible plan
Board-level strategy and financial proposals may require decisions before every benefit or cost is certain. Prepare to communicate assumptions, qualitative benefits, risks and sequencing clearly while keeping recommendations actionable.

Practice your answers

Technical practice

How would you identify and rank enterprise AI use cases?

What to coverPreparation suggestion: Explain how you would combine stakeholder discovery, value mapping, industry benchmarks and feasibility to identify measurable opportunities. Discuss how you would distinguish a promising use case from one that is not ready to scale.

How would you assess a client's data maturity and AI readiness?

What to coverPreparation suggestion: Describe a current-state review of data quality, infrastructure, ownership and gaps against the intended AI use cases. Explain how findings would shape recommendations rather than become a standalone assessment.

What would an enterprise AI and data foundation need to support scale?

What to coverPreparation suggestion: Cover the relationship among AI architecture, data architecture, platform components, cloud architecture and the product development lifecycle. Explain the trade-off between reusable foundations and the needs of individual products.

When would you recommend traditional, generative or agentic AI?

What to coverPreparation suggestion: Compare the kinds of insights or actions each approach can support, along with its data, evaluation and oversight needs. Explain why business value and operational risk should guide the choice rather than novelty.

How would you assess options for a client's data platform strategy?

What to coverPreparation suggestion: Discuss migration constraints and the fit of large-scale data lakes, cloud data warehouses and existing platforms. Address architecture trade-offs such as interoperability, scalability and the implications of API-first or Data Mesh approaches where relevant.

How would you design data governance for a multi-speed transformation?

What to coverPreparation suggestion: Cover ownership, standards, policies, quality processes and a workable data operating model across teams moving at different speeds. Explain how you would assess governance capability and connect it to the data supply chain.

How would you turn an AI opportunity assessment into a fundable roadmap?

What to coverPreparation suggestion: Explain prioritisation, sequencing, dependencies and a value-realisation framework with quantitative and qualitative measures. Show how you would make assumptions and uncertainty visible in a board-level business case.

How would you incorporate Responsible AI into an enterprise recommendation?

What to coverPreparation suggestion: Discuss privacy, regulatory and ethical considerations alongside governance and practical oversight of the proposed use cases. Explain how identified risks could change architecture, controls or rollout priorities.

How would you use industry benchmarking without copying a peer's AI strategy?

What to coverPreparation suggestion: Explain how research and peer comparisons could inform an opportunity or maturity assessment in a relevant industry. Address how you would test whether a benchmark fits the client's objectives, constraints and technology landscape.

How would you evaluate an existing AI product or framework before recommending a solution?

What to coverPreparation suggestion: Compare options against the intended use case, enterprise architecture, data readiness and ability to scale. Explain what a proof of concept could validate and what evidence would still be needed for a strategy recommendation.

Behavioural practice

Tell me about a discovery workshop that surfaced conflicting AI or data priorities.

What to coverPreparation suggestion: Use a real situation/action/result example showing how you elicited pain points, handled disagreement and reached a useful outcome. Clarify your own role and what changed after the session.

Describe a time you presented a complex technology recommendation to executives.

What to coverPreparation suggestion: Use a real situation/action/result example showing how you framed the decision, explained value and uncertainty, and addressed challenging questions. State the outcome without overstating your influence.

Tell me about a time you led a team through an ambiguous strategy engagement.

What to coverPreparation suggestion: Use a real situation/action/result example covering how you structured analytical work, guided collaboration and reviewed recommendations or a proof of concept. Explain the team's result and what you would improve.

Describe a time you managed financial constraints on a client engagement.

What to coverPreparation suggestion: Use a real situation/action/result example involving a budget, forecast or proposal and a decision you had to make. Explain the trade-off and its effect on delivery or the client recommendation.

Tell me about a time a data privacy or ethical concern changed your recommendation.

What to coverPreparation suggestion: If you have a real example, describe the situation, your action and the result, including how you engaged the relevant stakeholders. Focus on how you balanced potential value with the concern rather than claiming that risk was eliminated.

Sharpen your knowledge

Topics to prepare

AI value and prioritisation
Posting: The role includes AI value diagnostics, value frameworks and use-case prioritisation. Suggested revision exercise: compare three hypothetical use cases by measurable benefit, feasibility and dependencies; explain why one should be funded first.
Enterprise AI architecture
Posting: The role covers AI foundations, enterprise and data architecture, platform strategy, cloud architecture, PDLC, API-first, Data Mesh and Digital Core. Suggested revision exercise: sketch how these concepts fit into an enterprise architecture and explain two design trade-offs.
Data platforms and supply chain
Posting: The role calls for understanding the data supply chain and platform options including cloud migration, data lakes and cloud data warehouses. Suggested revision exercise: trace data from source to AI-enabled decision and identify where quality, access and scalability could fail.
Governance and operating models
Posting: The role defines data ownership, standards, policies, processes and data operating models. Suggested revision exercise: draft a one-page ownership and decision-rights model for a hypothetical cross-functional data product.
AI approaches and Responsible AI
Posting: The role seeks understanding of traditional, generative and agentic AI, plus Responsible AI, privacy, regulatory compliance and ethical considerations. Suggested revision exercise: choose an appropriate approach for one business use case and identify its principal controls and limitations.
Industry benchmarking
Posting: The role involves benchmarking peers and demonstrating understanding of a specific industry. Suggested revision exercise: select one industry named in the posting and outline the evidence you would seek to compare its AI and data maturity across organisations.

Skills to revise

Run a readiness assessment
Posting: Managers conduct stakeholder-led data maturity and AI readiness reviews, infrastructure analysis and gap assessments. Suggested revision exercise: prepare ten discovery questions and turn hypothetical answers into a current-state, gap and next-step summary.
Build a fundable roadmap
Posting: The role develops AI roadmaps and quantitative and qualitative business cases for board-level decisions. Suggested revision exercise: sequence three hypothetical initiatives, state dependencies and assumptions, and calculate a simple benefit-versus-cost scenario.
Present architecture options
Posting: The role designs enterprise AI and data solutions and evaluates existing products and frameworks; named platforms include GCP, Azure, AWS, Snowflake, Databricks and Palantir. Suggested revision exercise: draw two solution options using only platforms you can explain, then compare their fit against stated requirements rather than claiming one is universally best.
Facilitate executive decisions
Posting: Managers lead discovery workshops, design sessions and C-level discussions. Suggested revision exercise: rehearse a brief workshop opening, three questions to expose competing priorities, and a two-minute recommendation with a clear decision request.
Plan and manage an engagement
Posting: The role includes leading teams, structuring analytical work, developing recommendations and proofs of concept, and managing budgets, forecasts and financial proposals. Suggested revision exercise: create a hypothetical workplan with team responsibilities, milestones, a proof-of-concept decision point and a budget forecast.

Your preparation checklist

Build a role-evidence sheet
Map examples to the posting’s strategy consulting, enterprise AI and data solution design, and team leadership requirements. Note your role, duration, deliverables and outcomes for each; distinguish preferred certifications from required experience.
Prepare an opportunity-assessment walkthrough
Use one project to show how you identified AI use cases, mapped measurable value, benchmarked an industry and assessed data maturity, infrastructure and readiness gaps. Bring a one-page summary with your methods and recommendations.
Practice an enterprise architecture exercise
Sketch a target AI and data architecture from a stated current state. Explain platform and cloud choices, data flows, API-first integration, product lifecycle considerations and how the design could scale; present these as choices for the exercise, not facts about a client.
Draft a board-level roadmap and business case
Create a sample prioritisation matrix, phased roadmap and quantitative and qualitative value case. State assumptions, dependencies, costs, risks and how benefits would be measured; the posting calls for fundable AI strategies.
Prepare a governance and Responsible AI review
Outline data ownership, quality standards, policies and operating-model decisions for a sample use case. Add privacy, regulatory and ethical risks, with proposed controls and open questions rather than assumed compliance.
Rehearse how you lead an engagement
Prepare a concise example covering discovery workshops, executive discussions, team work planning and strategy or proof-of-concept delivery. Include how you handled a budget, forecast or financial proposal if you have a relevant example.
Write questions for the employer
Ask how this manager role balances opportunity assessment, architecture and delivery; which industries and platforms are most relevant; and how teams measure AI value and address Responsible AI in client engagements.

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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