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 practice to help enterprise clients realize measurable value from AI. The role owns consulting engagements that redesign complex processes as agentic, AI-first processes and helps clients adopt AI-native software delivery at scale.

Responsibilities

Lead stakeholder discovery workshops to identify process pain points, transformation opportunities and AI use cases. Diagnose current processes, then define agent hierarchies, target-state architectures, agent interaction models, value streams and transformation roadmaps.
Assess data maturity, governance and technology readiness. Coordinate business, architecture, data and engineering teams so proposed solutions remain traceable from business value through implementation.
Develop industry-specific Agentic AI value frameworks and executive business cases covering estimated value, investment, KPIs and benefits realization. Define stakeholder engagement, communication, training and adoption plans.
Work with senior technology and business leaders on AI-powered software delivery strategies, including the target-state software development lifecycle, platform and tooling architecture, engineering productivity measures and delivery operating-model changes. Manage large-scale delivery and client relationships with minimal oversight.

Requirements

Minimum 10 years of overall experience, including AI and data projects; experience leading at least one end-to-end process reengineering or reinvention engagement. Enterprise-scale process modeling and requirements definition, senior-stakeholder management, structured problem-solving, program management and strong communication are expected.
Technical depth and agentic-system build experience in at least one relevant platform or functional area. Deep expertise in at least one cloud platform, data platform or foundation-model category, with broad credibility across all three; ability to advise client technology leaders on platform and model choices. Knowledge of AI governance and regulation, including model risk or the EU AI Act, is required. Recognized expertise in at least one of Consumer Goods, Retail, Life Sciences or Banking is sought. 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. The posting lists Bangalore, Gurugram, Mumbai, Pune, Chennai, Kolkata and Hyderabad as locations and mentions personalized training and opportunities to work with G2000 clients.

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

Skills for this role

Agentic AIAI transformationAgent architectureAWSAzureGoogle CloudDatabricksSnowflakeAnthropicOpenAIEnterprise data strategyData governanceAI governanceKnowledge architectureSemantic layersProcess modelingRequirements definitionProcess reengineeringChange managementStakeholder managementExecutive communicationProgram managementBusiness case developmentValue estimationBenefits realizationSoftware development lifecycleAI-native software engineering

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, diagnosing complex processes, and designing agent hierarchies, interaction models, target-state architecture and roadmaps. Prepare a clear example of how process findings shaped an implementable redesign.
Connect AI design to enterprise readiness
The role assesses data maturity, governance and technology readiness, then coordinates business, architecture, data and engineering teams to keep implementation traceable to business value. Prepare to explain how readiness findings would change a proposed design or rollout.
Make the industry-specific value case
The manager develops Agentic AI value frameworks and executive business cases covering estimated value, investment, KPIs and benefits realization. Prepare to articulate assumptions and measures in the context of a core process in Consumer Goods, Retail, Life Sciences or Banking.
Shape AI-native software delivery
The posting also calls for strategies covering a target-state software development lifecycle, platform and tooling architecture, engineering productivity measures and delivery operating-model change. Prepare to connect technical choices to measurable delivery outcomes.
Lead delivery and adoption with senior stakeholders
This is an engagement-owner role involving large-scale delivery, client relationships and change plans for communication, training and adoption. Prepare an example showing how decisions, delivery coordination and stakeholder engagement stayed aligned.

Likely challenges to think through

Choosing autonomy only where it earns its place
Likely preparation challenge, not a stated client problem: an agentic redesign may promise more autonomy than the process or technology can support. Prepare to compare agentic and simpler approaches using process value, technical feasibility and required human oversight.
Defending value estimates before benefits are realized
Likely preparation challenge: executive cases need credible estimates even when adoption and productivity gains remain uncertain. Prepare to show baseline measures, investment assumptions, KPIs and how benefits would be tracked after implementation.
Balancing scalable adoption with governance
Likely preparation challenge: data readiness and model risk could constrain the pace or scope of an AI rollout. Prepare to explain how governance and regulatory considerations would affect architecture, sequencing and stakeholder decisions without treating them as reasons to stop by default.
Aligning operating-model change across teams
Likely preparation challenge: an AI-native delivery strategy can require business and engineering leaders to agree on new workflows and productivity measures. Prepare to describe trade-offs between speed, quality and adoption, and how a cross-functional roadmap would make decisions explicit.

Practice your answers

Technical practice

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

What to coverSuggested preparation: Explain how you would use discovery workshops, process models and requirements to locate pain points and establish an as-is baseline. Describe how you would prioritize opportunities by business value, feasibility and readiness rather than assume every step needs AI.

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

What to coverSuggested preparation: Walk through the agent hierarchy, interaction model, target-state architecture and value stream, including where people retain control. Explain how those design choices become a transformation roadmap.

When does an autonomous agent create value rather than add complexity?

What to coverSuggested preparation: Compare an agentic approach with a simpler workflow or assisted process using the task's variability, required judgment and consequences of error. Describe how you would test the value claim before expanding deployment.

How would you assess whether enterprise data is ready for scalable AI adoption?

What to coverSuggested preparation: Cover data maturity, governance, access and technology readiness, and explain how gaps would affect the proposed solution. Where relevant, discuss data products or semantic layers as possible foundations; the posting lists these as good-to-have experience, not universal requirements.

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

What to coverSuggested preparation: Show depth in one relevant cloud, data-platform or foundation-model category while comparing options across all three. Frame trade-offs around the use case, integration, governance, scalability and investment rather than asserting that one named provider is always best.

How would you keep business value traceable through solution delivery?

What to coverSuggested preparation: Describe a chain from stakeholder needs and process requirements to architecture decisions, implementation work and measurable outcomes. Explain how business, data, architecture and engineering teams would resolve changes without losing that traceability.

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

What to coverSuggested preparation: Choose a core process in Consumer Goods, Retail, Life Sciences or Banking and explain the assumptions behind estimated value and investment. Include KPIs and a benefits-realization approach that distinguishes projected benefits from observed results.

How would AI governance affect an agentic solution's design?

What to coverSuggested preparation: Discuss how you would identify applicable regulatory and model-risk considerations, assign oversight and incorporate controls into the operating model. Use the EU AI Act or model risk as examples named in the posting, without assuming either applies to every client.

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

What to coverSuggested preparation: Explain what you would examine across design, build, test, security and delivery, then connect proposed changes to platform and tooling architecture. Address the delivery operating-model changes needed to make the approach workable at enterprise scale.

How would you evaluate whether AI-powered software delivery improves performance?

What to coverSuggested preparation: Propose engineering productivity and quality measures with a baseline, and explain how you would check whether improvements translate into enterprise value. Discuss trade-offs between faster delivery, quality and risk rather than relying on a single metric.

Behavioural practice

Tell me about an end-to-end process reengineering engagement you led.

What to coverSuggested preparation: Give the situation and your ownership from as-is diagnosis through redesign and delivery. Specify your actions, the decisions you made and the results you can substantiate.

Describe a time senior stakeholders disagreed on a transformation direction.

What to coverSuggested preparation: Set out the competing priorities and how you structured the decision or communicated trade-offs. Explain your actions and the resulting decision or next step without implying consensus if there was none.

Give an example of coordinating business and technical teams on a complex solution.

What to coverSuggested preparation: Describe the situation, each team's responsibilities and a specific handoff or requirement that needed resolution. Explain what you did to maintain alignment and what resulted.

Tell me about a change initiative that required people to adopt a new way of working.

What to coverSuggested preparation: Identify the affected stakeholders and the adoption challenge. Describe your communication, training or engagement actions and the evidence you used to judge the result.

Describe how you managed a large delivery engagement with limited oversight.

What to coverSuggested preparation: Explain the situation, delivery risks and client-relationship responsibilities you held. Show how you set priorities, managed escalation and tracked the outcome.

Sharpen your knowledge

Topics to prepare

Enterprise process reinvention and agentic design
The posting calls for as-is diagnosis, end-to-end process reinvention, agent hierarchies, interaction models and target-state architecture. Suggested revision: explain how a complex process would move from its current state to an AI-first design, including where agent autonomy would and would not add value.
Platform choices and data readiness
The role requires depth in at least one cloud platform, data platform or foundation-model category and credibility across all three, alongside assessment of data maturity and technology readiness. Suggested revision: compare choices using AWS, Azure or Google Cloud; Databricks or Snowflake; and Anthropic or OpenAI as examples, stating the data and governance questions that would affect a recommendation.
Agentic AI value and benefits realization
The posting requires industry-specific value frameworks and executive business cases covering estimated value, investment, KPIs and benefits realization. Suggested revision: work through the assumptions behind a value estimate and explain how its KPIs would show whether benefits were realized.
AI governance and regulation
Awareness of AI governance and regulation, such as model risk or the EU AI Act, is stated in the role summary. Suggested revision: explain the governance questions an enterprise should resolve before scaling an autonomous process, distinguishing regulatory considerations from implementation decisions.
AI-native software delivery transformation
The role also covers AI-powered software delivery strategy, including a target-state SDLC, platform and tooling architecture, engineering productivity measures and operating-model change. Suggested revision: contrast a current and target SDLC and identify which measures would test productivity and quality claims.
Priority-industry process knowledge
The posting seeks recognized depth in at least one of Consumer Goods, Retail, Life Sciences or Banking. Suggested revision: choose one of those industries and explain a core process, its pain points and where an AI-first redesign might—or might not—make sense.

Skills to revise

Run a discovery workshop
The posting calls for leading stakeholder workshops to identify pain points, opportunities and AI use cases. Suggested practice: draft a workshop agenda, probing questions and a concise readout that separates observed process problems from proposed use cases.
Model requirements and traceability
Enterprise-scale process modelling and requirements definition are stated expectations, with traceability from business value to implementation. Suggested practice: create an as-is process map and a target-state requirements table linking each proposed change to an owner, value outcome and implementation dependency.
Articulate an agentic solution
The role calls for end-to-end agentic solution design and technical depth with agentic-system build experience in a platform or functional area. Suggested practice: build or reconstruct a bounded process example using one platform named in the posting, then present its agent hierarchy, interactions, architecture choices and limitations.
Present an executive business case
The posting requires value estimation, investment requirements, KPIs and benefits realization. Suggested practice: prepare a short executive case for an industry-specific use case, show the calculation assumptions and explain what evidence would confirm or challenge the estimate.
Plan delivery and adoption
The manager is expected to coordinate business, architecture, data and engineering teams, run delivery and define stakeholder engagement, communication, training and adoption plans. Suggested practice: draft a phased roadmap with cross-team decisions, dependencies, stakeholder messages and adoption measures.

Your preparation checklist

Build a process-reinvention walkthrough
The posting calls for end-to-end process reengineering. Prepare a real example, if available, showing the as-is process, requirements, redesign, delivery decisions and measured results; distinguish your own role from the team’s.
Rehearse a stakeholder discovery exercise
Draft a workshop agenda and sample questions to uncover pain points, AI use cases and constraints in a process from Consumer Goods, Retail, Life Sciences or Banking. Treat the exercise as hypothetical unless it reflects actual work.
Sketch an agentic target state
Prepare a one-page design with an agent hierarchy, interaction model, value stream and transformation roadmap. Explain where autonomous agents add value, where they do not, and how you would choose platforms and models.
Prepare a readiness and governance assessment
Use a short checklist covering data maturity, technology readiness, governance and model risk or relevant regulation. Show how findings would change the proposed solution; label any example findings as hypothetical.
Draft an executive value case
The role includes value estimation, investment, KPIs and benefits realization. Build a sample one-page business case with explicit assumptions and a way to track outcomes, rather than presenting estimates as facts.
Outline an AI-native software delivery strategy
Prepare a brief target-state SDLC covering platform and tooling choices, engineering productivity measures and delivery operating-model changes. Be ready to explain trade-offs to technology leaders.
Document delivery and adoption leadership
Select real examples, if available, of coordinating business, architecture, data and engineering teams and managing senior stakeholders. Note decisions, value-to-implementation traceability, and communication, training or adoption plans.
Bring questions for the employer
Ask which enterprise processes and industries are priorities, how this Manager role balances process reinvention with AI-powered software delivery, and how clients measure realized value and adoption.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…