New opportunity

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

Accenture · Gurugram, India

About this role

Accenture’s Technology Strategy & Transformation practice seeks a manager to lead enterprise AI value strategy and process reinvention engagements. The role focuses on redesigning complex enterprise processes as agentic, AI-first processes, from diagnosis and solution architecture through delivery and measurable value realization. The posting lists Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad as locations; the job header identifies Gurugram.

Responsibilities

Lead stakeholder discovery workshops to identify process pain points, opportunities and AI transformation use cases. Design end-to-end agentic solutions, including agent hierarchies, interaction models, target-state architecture, value streams and transformation roadmaps.
Assess data maturity, governance and technology readiness. Coordinate business, architecture, data and engineering teams, maintaining a link between business value and implementation.
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.
Own large-scale engagements and senior client relationships. Help clients shape AI-native software delivery strategies, including target-state software development lifecycles, platform and tooling choices, engineering productivity measures and delivery operating model changes.

Requirements

Minimum 10 years of overall experience, including AI and data projects, with experience delivering high-value consulting engagements at scale with minimal oversight. Must have led at least one end-to-end process reengineering or reinvention engagement and have enterprise-scale process modelling and requirements-definition experience.
Technical depth and agentic-system build experience in at least one relevant platform or functional area. Deep knowledge of at least one cloud platform, data platform or foundation-model provider, with broader fluency across those categories and the ability to advise client technology leaders on platform and model choices.
Recognized expertise in at least one of Consumer Goods, Retail, Life Sciences or Banking; senior stakeholder management, structured problem-solving, program management and communication skills; and awareness of AI governance and regulation, including model risk and the EU AI Act. A bachelor’s degree in a relevant discipline is required.

Preferred

An MBA; experience with enterprise data strategies, data products, AI-ready data foundations, knowledge architecture, semantic layers, enterprise knowledge management, AI governance frameworks and operating models; and experience leading cross-functional teams. The posting mentions work with G2000 clients and personalized 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

Agentic AIAI strategyAgentic architectureAI transformationProcess reengineeringProcess modellingRequirements definitionData governanceAI governanceChange managementBusiness case developmentValue estimationKPI developmentStakeholder managementExecutive communicationProgram managementAWSAzureGoogle CloudDatabricksSnowflakeAnthropicOpenAIEnterprise data strategyData productsKnowledge architectureSemantic layersEnterprise knowledge managementModel risk management

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 describes an engagement owner who diagnoses a complex enterprise process, leads discovery workshops, models requirements, redesigns it as an agentic, AI-first process, and runs delivery through realized value.
Design feasible agentic solutions
The role calls for agent hierarchies, interaction models, target-state architecture and roadmaps. It also requires assessing data maturity, governance and technology readiness and advising technology leaders on platform and model choices.
Make the value case measurable
The manager develops industry-specific Agentic AI value frameworks and executive business cases covering value estimates, investment, KPIs and benefits realization, with traceability from business value to implementation.
Shape AI-native software delivery
The posting also emphasizes strategies for AI-powered software delivery, including a target-state software development lifecycle, platform and tooling choices, engineering productivity measures and delivery operating model change.
Lead clients and adoption across teams
The manager runs large-scale engagements and senior client relationships, coordinates business, architecture, data and engineering teams, and defines stakeholder engagement, communication, training and adoption plans.

Likely challenges to think through

Balancing agent autonomy with readiness and control
Likely preparation challenge, not a confirmed client problem: an ambitious agent design may outpace data quality, governance or technology readiness. Prepare to explain how you would assess those constraints, narrow the initial scope and address model risk while preserving a path to scale.
Defending value estimates through delivery
Likely preparation challenge, not a confirmed client problem: projected productivity or process gains can be difficult to attribute to AI and turn into realized benefits. Prepare an example approach linking a baseline, investment assumptions, KPIs, implementation decisions and benefits tracking.
Changing software delivery without sacrificing quality
Likely preparation challenge, not a confirmed client problem: faster AI-assisted engineering may conflict with testing, security and established delivery practices. Prepare to discuss how a target-state lifecycle, tooling choices, productivity measures and operating model would be evaluated together.

Practice your answers

Technical practice

How would you run discovery for an enterprise process that may be suitable for AI reinvention?

What to coverThe posting calls for stakeholder workshops, process modelling and requirements definition at enterprise scale. Suggested answer focus: explain how you would map the current process, test competing accounts of its pain points and turn findings into prioritized use cases.

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

What to coverThe role calls for an agent hierarchy, interaction models, architecture, value streams and a transformation roadmap. Suggested answer focus: describe how you would assign responsibilities between agents and people, define their interactions and sequence delivery from the current state.

When would you advise against an autonomous agentic solution?

What to coverThe posting explicitly calls for judging where autonomous systems create real value rather than hype. Suggested answer focus: compare an agentic approach with a simpler workflow or AI-assisted step using process complexity, expected value and readiness as decision factors.

How would you diagnose an agentic solution that is not delivering the expected process outcome?

What to coverThe role links solution design and delivery to realized value. Suggested answer focus: trace the shortfall across requirements, agent interactions, data, adoption and measured outcomes before deciding whether to change the design or the value estimate.

How would you assess whether an enterprise’s data can support scalable AI adoption?

What to coverThe posting calls for assessing data maturity, governance and technology readiness; AI-ready data foundations and knowledge architecture are listed as good to have. Suggested answer focus: explain what you would examine, which gaps could block the use case and how those findings would alter the roadmap.

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

What to coverThe role asks for depth in at least one cloud platform, data platform or foundation-model provider and fluency across those categories. Suggested answer focus: compare choices against the use case, existing technology, data readiness, governance needs and investment rather than assuming one provider fits every client.

How would you keep business value traceable through solution definition and delivery?

What to coverThe posting calls for coordination across business, architecture, data and engineering teams with end-to-end traceability. Suggested answer focus: connect each process requirement and solution decision to a value stream, delivery milestone and measurable outcome, including how you would handle a change in scope.

How would you build an executive business case for an industry-specific Agentic AI use case?

What to coverThe role calls for industry-contextual value frameworks covering estimates, investment, KPIs and benefits realization, with depth in at least one named priority industry. Suggested answer focus: choose a process in Consumer Goods, Retail, Life Sciences or Banking and explain your baseline, assumptions, costs, measures and method for checking realized value.

How would AI governance and model risk affect an agentic process design?

What to coverThe posting calls for awareness of AI governance and regulation, including model risk and the EU AI Act. Suggested answer focus: explain how you would identify applicable concerns, assign oversight and adjust agent autonomy; avoid treating every use case as subject to identical obligations.

How would you define a target-state AI-native software delivery approach?

What to coverThe posting describes work on the target-state SDLC, platform and tooling strategy, engineering productivity measures and delivery operating model change. Suggested answer focus: explain how those elements fit together and how you would evaluate productivity alongside software quality and measurable enterprise value.

Behavioural practice

Tell me about a process you led from diagnosis through reinvention and delivery.

What to coverThe role calls for ownership of an end-to-end process reinvention engagement. Suggested answer focus: use situation, action and result to show your decisions, how delivery progressed and what outcome was measured; do not claim results you cannot substantiate.

Describe a time senior stakeholders disagreed on a transformation priority.

What to coverThe posting emphasizes senior client relationships and structured problem-solving. Suggested answer focus: give the situation, the actions you took to clarify trade-offs and reach a decision, and the resulting effect on the engagement.

Tell me about coordinating business and technical teams when requirements changed.

What to coverThe role calls for solution definition across business, architecture, data and engineering teams. Suggested answer focus: describe the situation, how you resolved differing interpretations and maintained the link to business value, and the result.

Describe a time you had to secure adoption of a major process or technology change.

What to coverThe posting calls for stakeholder engagement, communication, training and adoption plans. Suggested answer focus: use situation, action and result to explain the resistance or readiness gap, what you changed and how you assessed adoption.

Tell me about leading a large engagement with limited oversight.

What to coverThe role calls for large-scale consulting delivery, program management and client relationship ownership. Suggested answer focus: describe the situation, how you set priorities and managed risks and stakeholders, and the delivery result.

Sharpen your knowledge

Topics to prepare

Industry-specific process reinvention
The posting requires depth in at least one of Consumer Goods, Retail, Life Sciences or Banking, plus experience with enterprise-scale process modelling and end-to-end reinvention. Revision exercise: choose one listed industry and explain how you would diagnose a core process, challenge its current design and define a target state.
Agentic solution architecture
The role calls for agent hierarchies, interaction models, target-state architecture, value streams and roadmaps. Revision exercise: for a hypothetical enterprise process, sketch those components and explain where agent autonomy creates value versus where it does not.
Platform choices and data readiness
The posting asks for depth in at least one named cloud, data or foundation-model platform, broader fluency across those categories, and assessment of data maturity and governance. Revision exercise: compare two named platform options for a hypothetical use case and identify the data and governance questions that would affect your recommendation.
Value frameworks and benefits realization
The role requires industry-specific Agentic AI value frameworks and executive business cases covering estimates, investment, KPIs and realized benefits. Revision exercise: outline a value framework for a process in one listed industry, showing assumptions, costs, measures and how benefits would be tracked.
AI governance and adoption
The posting specifies awareness of the EU AI Act and model risk, as well as stakeholder engagement, communication, training and adoption plans. Revision exercise: list the governance questions and adoption decisions you would raise before scaling a hypothetical agentic process; distinguish regulatory questions from change-management actions.
AI-native software delivery strategy
The role also covers target-state software development lifecycles, platform and tooling strategy, engineering productivity measures and delivery operating-model change. Revision exercise: describe how you would assess a current software delivery lifecycle and propose measurable AI-enabled changes without assuming every activity should be automated.

Skills to revise

Facilitate stakeholder discovery
The posting explicitly calls for leading business-stakeholder workshops. Practice a short workshop plan with questions to surface process pain points, AI use cases, decision-makers and conflicting priorities; produce a prioritized findings summary.
Model processes and define requirements
Enterprise-scale process modelling and requirements definition are stated requirements. Practice mapping a hypothetical as-is process, its handoffs and failure points, then writing target-state requirements traceable to the identified business problems.
Articulate an end-to-end agentic design
The role requires solution definition across business, architecture, data and engineering teams. Practice presenting an agent hierarchy, interaction model and transformation roadmap, then explain how each design decision traces back to a business outcome.
Build and defend an executive business case
The posting requires value estimates, investment requirements, KPIs and benefits realization, alongside senior-stakeholder management. Practice a concise executive presentation that shows assumptions and trade-offs, and prepare answers to challenges about value and delivery feasibility.
Coordinate delivery and adoption
The manager is expected to run large engagements, maintain the link between value and implementation, and define adoption plans. Practice drafting a delivery plan with cross-functional responsibilities, decision points, benefits checkpoints and stakeholder communication and training actions.

Your preparation checklist

Assemble a leadership evidence sheet
Prepare concise examples documenting overall and AI/data project experience, an end-to-end process reinvention you led, enterprise-scale process modelling, agentic build experience, and senior client ownership. Note your specific contribution and outcomes; the posting asks for 10+ years overall experience.
Build a process-reinvention walkthrough
Choose a relevant example from Consumer Goods, Retail, Life Sciences or Banking. Show the as-is process, stakeholder discovery questions, pain points, requirements, target-state process and delivery decisions.
Sketch an agentic solution
For that process, prepare a one-page agent hierarchy, interaction model, target architecture, value stream and transformation roadmap. Explain where autonomy adds value and where it does not.
Defend technology and readiness choices
Prepare a comparison of plausible platform and model choices, grounded in deep knowledge of one platform or provider. Include how data maturity, governance and technology readiness affect implementation.
Draft an executive value case
Create a short, industry-specific case showing value-estimate assumptions, investment needs, KPIs and a benefits-realization approach. Trace proposed capabilities back to business outcomes.
Prepare an AI-native delivery exercise
The posting also covers AI-powered software delivery: sketch a target-state SDLC, tooling and platform choices, engineering productivity measures, and operating-model changes for discussion.
Rehearse adoption and governance trade-offs
Outline stakeholder communications, training and adoption measures, plus questions about model risk and applicable AI regulation. Distinguish risks you would investigate from controls you can substantiate.
Bring focused questions for the employer
Ask how this manager role balances enterprise process reinvention with AI-native software delivery, which outcomes define engagement success, and how business, architecture, data and engineering teams share decisions.

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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