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 help enterprise clients realize measurable value from AI. The role combines ownership of process-reinvention engagements with strategy for AI-native software delivery, working with senior business and technology leaders from initial diagnosis through implementation and value realization.

Responsibilities

Lead stakeholder discovery workshops to identify business pain points, AI opportunities and transformation use cases. Redesign complex enterprise processes as agentic, AI-first processes, defining agent hierarchies, target-state architectures, interaction models, value streams and transformation roadmaps.
Assess data maturity, governance and technology readiness. Coordinate business, architecture, data and engineering teams, maintaining traceability from business value to implementation.
Develop industry-appropriate Agentic AI value frameworks and executive business cases covering estimated value, investment, KPIs and benefits realization. Define stakeholder engagement, communication, training and adoption plans.
Help clients shape AI-powered software delivery strategies, including the target-state software development lifecycle, platform and tooling architecture, engineering productivity measures and delivery operating-model changes. Lead large-scale delivery and manage senior client relationships with minimal oversight.

Requirements

At least 10 years of overall experience, including AI and data projects; the posting also lists 10–12 years of experience. Candidates 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 are required in at least one relevant platform or functional area. The role calls for depth in at least one cloud platform, data platform or foundation-model offering, with broader credibility across those categories; examples include AWS, Azure, Google Cloud, Databricks, Snowflake, Anthropic and OpenAI. Candidates should bring structured problem-solving, program management, senior-stakeholder communication, awareness of AI governance and regulation, and substantial knowledge of at least one of Consumer Goods, Retail, Life Sciences or Banking. 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, operating models and organizational readiness. The posting lists Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad as locations. It highlights work with major clients, cross-disciplinary collaboration 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 transformation strategyAgent architectureProcess reengineeringProcess modellingRequirements definitionEnterprise data strategyData governanceAI governanceEU AI ActModel riskChange managementStakeholder managementProgram managementBusiness case developmentValue estimationKPI developmentAWSAzureGoogle CloudDatabricksSnowflakeAnthropicOpenAISoftware development lifecycleEngineering productivityExecutive communication

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 role owns engagements from stakeholder discovery and as-is diagnosis through enterprise process modelling, agent hierarchies, target-state architecture, roadmaps and realised value. It calls for depth in at least one named industry to challenge existing processes credibly.
Make the value case measurable
Develop industry-appropriate Agentic AI value frameworks and executive business cases covering estimated value, investment, KPIs and benefits realisation. Keep a clear line from the business problem to implementation and measured outcomes.
Test feasibility and shape technical choices
Assess data maturity, governance and technology readiness before proposing scalable adoption. The posting expects technical depth in an agentic-system build and the ability to discuss platform and model choices with client technology leaders.
Shape AI-native software delivery
Help enterprises define AI-powered software delivery strategies, including a target-state development lifecycle, platform and tooling architecture, engineering productivity measures and delivery operating-model changes.
Lead delivery and adoption across stakeholders
The manager leads large-scale, cross-functional solution definition with minimal oversight, manages senior client relationships and defines communication, training and adoption plans alongside the technical transformation.

Likely challenges to think through

Choosing useful autonomy without outrunning readiness
A likely challenge is balancing an ambitious agentic design against data limitations, governance and model risk. Prepare to explain how a process diagnosis would inform where agents add value, what readiness checks matter and when a less autonomous design may be preferable.
Making projected value defensible
Executive value estimates may be difficult to connect to investment and benefits realised after implementation. Prepare a method for stating assumptions, baselines, KPIs and ownership of measurement without treating estimated gains as guaranteed.
Coordinating two connected transformations
The posting spans enterprise process reinvention and AI-native software delivery, each involving business, architecture, data and engineering decisions. Prepare to show how you would sequence work, resolve competing stakeholder priorities and preserve traceability from the agreed business outcome to delivery and adoption.

Practice your answers

Technical practice

How would you turn a discovery workshop into a prioritized set of AI transformation use cases?

What to coverThe role calls for identifying stakeholder pain points, opportunities and use cases. Suggested preparation: explain how you would distinguish a measurable process problem from an appealing AI idea, then prioritize by potential value, feasibility and readiness.

How would you model an enterprise process before redesigning it as AI-first?

What to coverThe posting specifies as-is diagnosis, enterprise-scale process modelling and requirements definition. Suggested preparation: describe how you would capture handoffs, exceptions and decision points, then trace target-state requirements back to the business problem.

How would you design an end-to-end agentic solution for a complex process?

What to coverThe role calls for agent hierarchies, interaction models, target-state architecture, value streams and a transformation roadmap. Suggested preparation: explain where agents would act, where people would retain decisions, and how you would phase implementation rather than assume full autonomy.

How would you assess whether an enterprise is ready to scale an agentic use case?

What to coverThe posting names data maturity, governance and technology readiness as assessment areas. Suggested preparation: explain what evidence you would gather about data quality, access and existing systems, and how gaps would change the solution or roadmap.

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

What to coverThe role seeks depth in at least one cloud platform, data platform or foundation-model offering and credibility across those categories. Suggested preparation: compare choices against the client's architecture, data needs, governance constraints and operating costs without assuming one vendor fits every use case.

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

What to coverThe posting calls for an industry-appropriate value framework covering estimated value, investment, KPIs and benefits realization. Suggested preparation: show how you would state assumptions, establish a baseline and distinguish projected benefits from measured outcomes.

How would AI governance and model risk affect a proposed agentic architecture?

What to coverThe role requires awareness of AI governance and regulation, including the EU AI Act and model risk, and lists governance frameworks as a good-to-have area. Suggested preparation: discuss how risk classification, oversight and monitoring would influence autonomy and deployment decisions, while qualifying any jurisdiction-specific conclusion.

What would you change in a target-state software development lifecycle to support AI-powered delivery?

What to coverThe page explicitly calls for an AI-powered software delivery strategy, target-state SDLC, tooling architecture and delivery operating-model change. Suggested preparation: walk through where AI could alter design, build, test or secure activities and what controls would preserve quality.

How would you measure whether AI-native software delivery creates value?

What to coverThe posting names engineering productivity measures, quality uplift and measurable value realization. Suggested preparation: propose a small set of baseline-to-outcome measures and explain how you would avoid treating faster activity or greater tool use as proof of better delivery.

How would you challenge a process in an industry you know well?

What to coverThe role seeks depth in at least one of Consumer Goods, Retail, Life Sciences or Banking and the ability to engage senior industry leaders. Suggested preparation: choose one relevant core process, explain its constraints, and identify a specific redesign hypothesis whose value and feasibility could be tested.

Behavioural practice

Tell me about an end-to-end process reinvention you led.

What to coverThe role calls for ownership from as-is diagnosis through implementation and realized value. Suggested preparation: use situation, action and result to explain your scope, the decisions you owned and how outcomes were assessed.

Describe a time senior business and technology stakeholders disagreed on a transformation direction.

What to coverThe posting emphasizes senior client relationships and executive communication. Suggested preparation: use situation, action and result to show how you surfaced the disagreement, structured the trade-offs and reached or escalated a decision.

Tell me about coordinating business, architecture, data and engineering teams through a difficult delivery decision.

What to coverCross-functional solution definition and traceability from business value to implementation are stated duties. Suggested preparation: use situation, action and result to show how you clarified ownership, resolved dependencies and kept the decision tied to the intended value.

Describe a time you addressed resistance to a major change in how people worked.

What to coverThe role includes stakeholder engagement, communication, training and adoption planning. Suggested preparation: use situation, action and result to explain how you learned the cause of resistance, adapted the plan and assessed adoption.

Tell me about leading a large-scale engagement when priorities or assumptions changed.

What to coverThe posting expects program management and large-scale delivery with minimal oversight. Suggested preparation: use situation, action and result to show how you restructured the problem, managed risks and communicated the revised path to stakeholders.

Sharpen your knowledge

Topics to prepare

Enterprise process reinvention
Posting: The manager owns end-to-end reinvention of complex processes, from as-is diagnosis to realized value, and needs enterprise-scale process-modelling and requirements-definition experience. Revision exercise: Choose a process in one of the listed industries and explain its current pain points, target state, and why particular steps should or should not become AI-first.
Agentic solution design
Posting: The role defines agent hierarchies, interaction models, target-state architecture, value streams, and roadmaps, with technical depth and agentic-system build experience in at least one platform or functional area. Revision exercise: Talk through an agentic design for a process, including agent responsibilities, handoffs, human decisions, and a phased roadmap.
Data readiness and platform choices
Posting: The manager assesses data maturity, governance, and technology readiness; the role asks for depth in at least one cloud platform, data platform, or foundation-model offering and credibility across the categories. Revision exercise: Compare two plausible platform or model choices against a client's data constraints and explain what evidence would change your recommendation.
Industry-specific AI value
Posting: Business cases must reflect industry context, and the role calls for depth in at least one of Consumer Goods, Retail, Life Sciences, or Banking. Revision exercise: For one of those industries, identify a core process, its value drivers, investment considerations, KPIs, and how benefits would be verified after implementation.
AI governance and model risk
Posting: Awareness of AI governance and regulation, including examples such as the EU AI Act and model risk, is expected; governance-framework design is listed as good to have. Revision exercise: Review an agentic use case and identify governance questions, risk owners, and decisions that need resolution before scaling. Treat framework design as a preparation suggestion, not a basic requirement.
AI-native software delivery
Posting: The role also shapes AI-powered software delivery strategies covering the target-state SDLC, platform and tooling architecture, engineering productivity measures, operating-model change, and value realization. Revision exercise: Explain how an AI-native delivery approach could change design, build, test, and release work, and distinguish measurable improvement from assumed productivity gains.

Skills to revise

Run discovery and define requirements
Posting: The manager leads stakeholder workshops to find pain points and AI use cases. Practice exercise: Draft a workshop agenda and questions, then turn hypothetical answers into an as-is process map and prioritized requirements. This rehearses discovery through solution definition.
Create a traceable solution design
Posting: Solution definition spans business, architecture, data, and engineering teams, with traceability from business value to implementation. Practice exercise: Produce a one-page target-state diagram and map each proposed agent and integration to a requirement, owner, and expected value.
Build an executive business case
Posting: Agentic AI value frameworks and executive cases must address estimated value, investment, KPIs, and benefits realization. Practice exercise: Build a short case with explicit assumptions, a baseline, investment categories, KPI definitions, and a plan to check realized versus estimated value.
Assess readiness and defend choices
Posting: Data and technology readiness assessments inform scalable adoption, and the manager helps tech leadership make platform and model choices. Practice exercise: Create a readiness checklist for a hypothetical client and present a recommendation using one platform or model area you know in depth, while stating gaps and trade-offs across the other categories.
Define software-delivery measures
Posting: The role defines engineering productivity measures and delivery operating-model changes for AI-powered software delivery. Practice exercise: Draft a target-state SDLC workflow with baseline and follow-up measures for productivity and quality, plus the team responsibilities needed to collect them.
Plan adoption and communicate decisions
Posting: The manager defines stakeholder engagement, communication, training, and adoption plans and manages senior client relationships. Practice exercise: Prepare a brief executive decision note and a stakeholder-by-stakeholder adoption plan for a hypothetical process rollout, including concerns, communications, training, and adoption indicators.

Your preparation checklist

Build an end-to-end reinvention walkthrough
The posting calls for leadership of a process-reengineering engagement. If available, prepare an anonymized example showing your role, as-is process, enterprise-scale requirements, redesign, delivery decisions and measured outcomes; distinguish your work from the team's.
Rehearse an industry-specific discovery workshop
The role involves finding pain points and AI use cases with stakeholders. For one listed industry—Consumer Goods, Retail, Life Sciences or Banking—draft a workshop agenda, diagnostic questions and criteria for prioritizing opportunities.
Sketch an agentic target state
The posting specifies agent hierarchies, interaction models, architecture, value streams and roadmaps. Prepare a hypothetical process redesign covering those elements, human decision points and traceability from business requirements to implementation; label assumptions.
Prepare a technology and readiness assessment
The role assesses data maturity, governance and technology readiness. Draft an assessment checklist and a platform-choice explanation grounded in one cloud, data or foundation-model area you can discuss deeply, with trade-offs across the other categories and relevant AI-governance risks.
Draft an executive value case
The posting asks for industry-contextual Agentic AI value frameworks. Create a one-page example with explicit assumptions, estimated value, investment, KPIs and a benefits-realization approach; avoid presenting hypothetical figures as actual results.
Outline an AI-native software delivery strategy
The role also covers software delivery transformation. Prepare a concise design exercise showing a target-state SDLC, platform and tooling choices, engineering productivity measures and delivery operating-model changes.
Document delivery and adoption leadership
The posting emphasizes large-scale delivery, senior stakeholders and cross-functional coordination. Prepare examples, where available, of resolving business–engineering trade-offs, managing a program and planning stakeholder communication, training and adoption.
Write questions for the employer
Bring questions about how this manager role balances process reinvention with AI-native software delivery, what value measures clients prioritize, and how engagement owners coordinate architecture, data, engineering and change-management work.

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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