New opportunity

S&C GN - TS&T –Enterprise AI Value Strategy - Manager

Accenture · Gurugram, India

About this role

Accenture seeks a Manager in its Technology Strategy & Transformation Global Network practice to help enterprise clients turn AI transformation into measurable value. The role focuses on redesigning complex processes as agentic, AI-first workflows and shaping AI-native software delivery with senior technology and business leaders. The posting lists Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad as locations.

Responsibilities

Lead stakeholder discovery workshops to identify process pain points, opportunities and AI use cases. Own process-reinvention engagements from current-state diagnosis through agent architecture, delivery and value realization.
Define end-to-end agentic solutions, including agent hierarchies, target-state architectures, interaction models, value streams and transformation roadmaps. Assess data maturity, governance and technology readiness, and coordinate solution definition across business, architecture, data and engineering teams.
Build industry-specific Agentic AI value frameworks and executive business cases covering value estimates, investment, KPIs and benefits realization. Define stakeholder engagement, communications, training and adoption plans.
Help clients shape AI-powered software delivery strategies, including target-state software development lifecycles, platform and tooling architecture, engineering productivity measures and delivery operating-model changes. Lead large-scale consulting delivery and manage senior stakeholders with minimal oversight.

Requirements

At least 10 years of overall experience, including AI and data projects, and leadership of at least one end-to-end process reengineering or reinvention engagement. Enterprise-scale process modelling and requirements definition, structured problem-solving, program management and strong communication are required.
Technical depth and agentic-system build experience in at least one relevant platform or functional area. Bring deep knowledge of at least one cloud platform, data platform or foundation-model provider, with broad credibility across those categories; assess where autonomous systems provide value and advise client technology leaders on platform and model choices.
Recognized knowledge of core processes in at least one of Consumer Goods, Retail, Life Sciences or Banking; awareness of AI governance and regulation, including the EU AI Act and model risk. A bachelor's degree in a relevant discipline is required.

Preferred

An MBA; experience with enterprise data strategies, data products, AI-ready foundations, knowledge architecture, semantic layers, enterprise knowledge management, AI governance frameworks and operating models; and strong cross-functional consulting leadership. Accenture also describes opportunities to work with G2000 clients, collaborate across disciplines and access personalized training.

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

Skills for this role

Agentic AIAI strategyAgent architectureProcess reengineeringProcess modellingRequirements definitionEnterprise data strategyData governanceAI governanceChange managementStakeholder managementExecutive communicationBusiness case developmentValue estimationBenefits realizationProgram managementAWSAzureGoogle CloudDatabricksSnowflakeAnthropicOpenAIKnowledge architectureSemantic layersAI-native software delivery

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

Own end-to-end process reinvention
The posting calls for leading discovery workshops and taking a complex enterprise process from current-state diagnosis through agentic redesign, delivery and realized value. Prepare a clear account of how an engagement moves from process modelling and requirements to implementation.
Design feasible agentic solutions
The role includes defining agent hierarchies, interaction models, target-state architecture and roadmaps while assessing data, governance and technology readiness. Prepare to explain architecture and platform choices in terms of the process and its constraints.
Make the value case measurable
The posting asks for industry-specific Agentic AI value frameworks and executive business cases covering investment, KPIs and benefits realization. Prepare to connect proposed changes to assumptions, measures and a plan for tracking outcomes.
Shape AI-native software delivery
The role also involves AI-powered software delivery strategy: target-state SDLC, platform and tooling architecture, engineering productivity measures and delivery operating-model change. Prepare to explain how these elements fit into an executable transformation approach.
Lead senior stakeholders through adoption
The manager is expected to run large-scale consulting delivery with minimal oversight, engage senior leaders and coordinate business, architecture, data and engineering teams. The posting also calls for communications, training and adoption plans.

Likely challenges to think through

Choosing autonomy over simpler options
Possible difficulty, not a confirmed client problem: an agentic design may add complexity without sufficient value. Prepare to explain when autonomy is warranted, when a simpler approach is preferable, and how data readiness and model risk affect that decision.
Defending value estimates before results exist
Possible difficulty: executives may need an investment decision before benefits are proven. Prepare an approach that makes assumptions explicit, establishes baseline KPIs and distinguishes estimated value from realized benefits.
Keeping design and delivery aligned
Possible difficulty: business goals, architecture decisions and engineering constraints can diverge across teams. Prepare to show how you would maintain traceability from a process pain point through requirements, solution decisions and delivery milestones.
Balancing delivery productivity with control
Possible difficulty: AI-native software delivery may promise speed while requiring attention to quality, security and operating-model change. Prepare to discuss productivity measures alongside safeguards and the adoption changes needed to sustain results.

Practice your answers

Technical practice

How would you diagnose an enterprise process and identify credible AI use cases?

What to coverSuggested preparation: Explain how you would use stakeholder workshops, process modelling and requirements definition to establish the current state and its pain points. Describe how you would distinguish valuable opportunities from ones that do not warrant AI.

How would you design an agentic target state for a complex process?

What to coverSuggested preparation: Walk through the agent hierarchy, responsibilities, interactions and target-state architecture. Explain how the design changes the process and connects to a transformation roadmap.

Where would you use autonomous agents rather than a simpler workflow?

What to coverSuggested preparation: Compare the value of autonomy with its operational complexity and need for oversight. Describe the evidence you would seek before recommending an agentic approach at enterprise scale.

How would you assess whether a client's data is ready for scalable AI adoption?

What to coverSuggested preparation: Cover data maturity, governance and technology readiness, then explain how gaps would affect solution scope or sequencing. You could discuss data products or semantic layers as optional approaches; the posting lists these as good-to-have experience.

How would you advise technology leaders on platform and model choices?

What to coverSuggested preparation: Compare relevant cloud, data-platform and foundation-model options against the use case, existing architecture and governance needs. Show depth in one area while explaining how you would coordinate choices across the other categories.

How would you address AI governance and model risk in an agentic solution?

What to coverSuggested preparation: Explain how governance considerations would shape solution design and operating responsibilities. The posting cites awareness of the EU AI Act and model risk; avoid assuming a particular regulation applies without knowing the client's context.

How would you build an executive business case for Agentic AI?

What to coverSuggested preparation: Set out an industry-specific value framework covering estimates, investment, KPIs and benefits realization. Explain the assumptions and how you would check whether projected value was achieved.

How would you maintain traceability from business value through delivery?

What to coverSuggested preparation: Connect stakeholder needs and value streams to requirements, architecture decisions, a roadmap and implementation outcomes. Explain how business, architecture, data and engineering teams would resolve changes without losing the original value objective.

How would you shape an AI-native software delivery strategy?

What to coverSuggested preparation: Discuss the target-state software development lifecycle, platform and tooling architecture, and delivery operating-model changes. Explain how engineering productivity and quality measures would help evaluate the strategy.

How would you challenge a core process in one of the posting's priority industries?

What to coverSuggested preparation: Choose a process you know in Consumer Goods, Retail, Life Sciences or Banking and explain its current constraints. Then outline a plausible AI-first redesign, including what industry-specific evidence you would need before recommending it.

Behavioural practice

Tell me about a time you led a process reinvention from diagnosis to outcomes.

What to coverSuggested preparation: Use a real situation, your actions and the result to show how you owned the work across design and delivery. Be clear about your decisions, the stakeholders involved and how outcomes were measured.

Describe a time senior stakeholders disagreed on a transformation decision.

What to coverSuggested preparation: Give the situation, how you framed the competing priorities and what you did to reach a decision. State the result without claiming consensus if the disagreement was not fully resolved.

Tell me about a time you aligned business, data, architecture and engineering teams.

What to coverSuggested preparation: Describe a real cross-functional challenge, your actions to clarify requirements or trade-offs, and the result. Distinguish your contribution from the wider team's work.

Describe a time you helped people adopt a significant technology or process change.

What to coverSuggested preparation: Explain the situation, the engagement, communication or training actions you took, and the result. Include how you identified resistance or assessed adoption, if applicable to your example.

Tell me about a time a large engagement went off track while you were leading it.

What to coverSuggested preparation: Set out the situation, how you diagnosed the delivery issue and the actions you took with limited oversight. Explain the result and what you changed in your approach afterward.

Sharpen your knowledge

Topics to prepare

Enterprise process reinvention
Review how to move from current-state diagnosis to an AI-first target process. For one complex process, explain which steps should change, which should remain human-led, and why. The posting makes end-to-end process reinvention central to this Manager role.
Agentic architecture and technology choices
Compare an agent hierarchy, interaction model and target-state architecture for one enterprise use case; explain where autonomy adds value versus unnecessary complexity. Revisit trade-offs across cloud, data-platform and foundation-model choices, using the platforms named in the posting rather than assuming a required stack.
Data readiness and responsible AI
For a hypothetical use case, identify data-maturity and governance gaps, then outline how model risk and applicable regulation, such as the EU AI Act, could affect the design. The posting requires readiness assessment and awareness of AI governance; specific controls will depend on the client context.
Value measurement and benefits realization
Work through a sample value framework: distinguish estimated benefits, investment, KPIs and evidence needed to confirm realized value. The role calls for industry-contextual Agentic AI frameworks and executive business cases.
AI-native software delivery
Compare a current software development lifecycle with an AI-powered target state, noting implications for tooling architecture, engineering productivity, quality and the delivery operating model. These are stated areas of the transformation strategy work.
Priority-industry process knowledge
Choose one industry named in the posting—Consumer Goods, Retail, Life Sciences or Banking—and review a core process and its decision points. Practice explaining to a senior leader what would need to change before proposing agentic reinvention; the posting calls for depth in at least one of these industries.

Skills to revise

Facilitate stakeholder discovery
Draft a 45-minute workshop agenda and five questions that surface pain points, process constraints and AI use cases; finish with a prioritized opportunity list. Leading business-stakeholder discovery workshops is an explicit responsibility.
Model processes and define requirements
Create an as-is and target-state process map for a sample enterprise workflow, with handoffs, exceptions and testable requirements. The posting explicitly calls for enterprise-scale process modelling and requirements definition.
Produce an agentic solution blueprint
Sketch agent roles, interactions, human decision points, data dependencies and a phased transformation roadmap for that workflow. Trace each design element to a business outcome; the role owns solution definition across business, architecture, data and engineering teams.
Build an executive value case
Make a one-page case with transparent assumptions, value estimates, investment categories, KPIs and a benefits-tracking plan; rehearse a brief executive explanation of the uncertainties. This mirrors the posting’s business-case and value-realization responsibilities.
Shape a software-delivery transformation plan
Draft a target-state lifecycle and a short plan covering platform and tooling decisions, productivity measures and operating-model changes. The posting explicitly asks for executable AI-powered software delivery strategies.
Plan adoption and cross-functional delivery
Create a stakeholder map, communications and training outline, and a delivery plan showing dependencies between business, architecture, data and engineering teams. These artifacts rehearse the stated change-management and large-scale engagement responsibilities.

Your preparation checklist

Prepare an end-to-end reinvention walkthrough
The role calls for leadership of a process-reengineering engagement. If you have a relevant example, prepare a one-page account of the as-is diagnosis, enterprise requirements, your decisions, delivery leadership and measured outcomes; distinguish your contribution from the team’s.
Draft a stakeholder discovery plan
The posting specifies workshops with business stakeholders. Prepare a sample agenda and questions that uncover process pain points, AI use cases, constraints and competing priorities.
Sketch an agentic process redesign
For a process in Consumer Goods, Retail, Life Sciences or Banking, prepare an illustrative as-is and target-state map showing value streams, agent hierarchy, interaction models, human decision points and a phased roadmap. Label assumptions rather than presenting it as client work.
Explain technology and readiness choices
The role requires agentic-system build depth in a platform or functional area and assessment of data, governance and technology readiness. Prepare a decision matrix comparing plausible platform and model options, with dependencies and reasons to use—or not use—autonomous agents.
Build a concise executive value case
The posting calls for industry-specific value frameworks and business cases. Prepare a sample page with explicit value-estimate assumptions, investment categories, KPIs, measurement baselines and a benefits-realization approach; do not invent actual client results.
Outline an AI-native delivery strategy
The source also emphasizes software-delivery transformation. Prepare an illustrative target-state SDLC covering tooling architecture, engineering productivity and quality measures, operating-model changes and how outcomes would be tracked.
Rehearse delivery and adoption decisions
Prepare an example, if available, of managing senior stakeholders and coordinating business, architecture, data and engineering teams. Show how you handled trade-offs, maintained value-to-implementation traceability, and planned communication, training and adoption.
Bring questions about the engagement scope
Ask which work this Manager would prioritize—enterprise process reinvention, AI-native software delivery or both—and how the team defines value realization, delivery ownership and governance responsibilities.

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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