New opportunity

S&C GN - TS&T – Enterprise Advisory - Consultant

Accenture · Hyderabad, India

About this role

Join our Technology Strategy & Transformation team at Accenture Global Network to help strategic clients realize exceptional value from AI in software delivery, shaping how enterprises adopt AI-native software engineering.

Responsibilities

Help C-suite stakeholders (CIOs, CTOs, CDOs, engineering leaders) define AI-powered software delivery transformation strategies and translate them into executable approaches.
Define target state SDLC, create platform and tooling strategy and architecture, and drive engineering productivity measures and delivery operating model changes.
Act as a workstream owner, taking a defined part of an engagement end-to-end such as agent hierarchies or use-case and data-product definitions.

Required qualifications

Experience level: 5+ years of relevant professional experience.
Strong background in technology strategy, enterprise advisory, or software engineering transformation.

Benefits

Inclusive, diverse, and collaborative global culture with opportunities to work with C-suite stakeholders.

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

Skills for this role

Artificial IntelligenceEnterprise AIAI StrategySoftware EngineeringAI-native SDLC

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

Defining AI-Powered Software Delivery Transformation Strategies
Help C-suite stakeholders including CIOs, CTOs, CDOs, and engineering leaders shape strategies that integrate AI into software engineering, translating high-level transformation ambitions into actionable, executable approaches.
Architecting Target State SDLC and Tooling
Create platform and tooling strategies, architecture, and engineering productivity measures to modernize how software is imagined, designed, built, tested, secured, and delivered using AI.
End-to-End Workstream Ownership
Take charge of a defined part of an engagement end-to-end, serving as the day-to-day client contact responsible for deliverables such as agent hierarchies, use cases, or data-product definitions.

Likely challenges to think through

Translating Abstract AI Ambitions into Measurable Outcomes
The challenge lies in aligning high-level C-suite enthusiasm for AI with concrete, measurable enterprise value and productivity uplifts. Prepare by practicing how to establish clear metrics for software delivery performance and engineering productivity.
Managing Complex Stakeholder Landscapes across the SDLC
Advising diverse executive stakeholders (CIOs, CTOs, CDOs) requires balancing competing priorities across the entire software development lifecycle. Prepare by reviewing modern AI-native SDLC patterns and framework design strategies to build credibility during advisory sessions.

Practice your answers

Technical practice

How would you design an AI-native Software Development Lifecycle (SDLC) framework for a traditional enterprise client?

What to coverDiscuss how generative AI and automation can be integrated across each phase of the SDLC, from design and coding to testing, security, and deployment. Address the architectural shifts, tooling strategies, and governance guardrails needed to maintain software quality and velocity.

What methodologies and metrics would you use to measure engineering productivity and value realization in AI transformation?

What to coverExplain how to quantify productivity uplifts and quality improvements resulting from AI adoption in software engineering. Discuss specific metrics beyond line-count, such as cycle time reduction, deployment frequency, and defect leakage, and how to connect these metrics to business outcomes for C-suite stakeholders.

How do you approach defining agent hierarchies and use-cases for enterprise domains?

What to coverDescribe your methodology for breaking down complex business processes into specialized agent roles and hierarchical workflows. Address how you identify high-impact use cases, define data products, and ensure seamless orchestration between human operators and AI agents.

How do you translate high-level executive transformation ambition into an executable roadmap?

What to coverOutline a structured approach for moving from broad vision statements to prioritized, phase-wise implementation plans. Discuss how to manage scope, align cross-functional teams, and establish clear milestones that address the specific needs of CIOs, CTOs, and CDOs.

What platform and tooling strategy considerations are crucial when scaling AI-native software delivery?

What to coverExamine the technical and operational prerequisites for deploying enterprise-grade AI tools in software engineering workflows. Address integration challenges, developer experience, security compliance, and vendor ecosystem selection.

Behavioural practice

Tell me about a time when you presented a complex technology strategy to C-level stakeholders. How did you tailor your message?

What to coverDescribe a situation where you communicated technical transformation strategies to executives like CIOs or CTOs. Explain how you focused on business value, handled probing questions, and structured your narrative using a situation-action-result format.

Describe a scenario where you owned an engagement workstream end-to-end. How did you manage day-to-day client relationships and deliverables?

What to coverProvide an example where you took full ownership of a consulting workstream or project component. Detail how you coordinated tasks, acted as the primary client contact, managed expectations, and delivered high-quality outputs under tight timelines using the situation-action-result format.

Give an example of how you aligned conflicting priorities among cross-functional engineering and business leaders during a transformation initiative.

What to coverShare a situation where stakeholder disagreement threatened project progress. Explain how you facilitated alignment, balanced competing technical and business objectives, and drove consensus using the situation-action-result format.

Sharpen your knowledge

Topics to prepare

AI-Native Software Development Lifecycle (SDLC) Transformation
Revisit conceptual frameworks for modernizing the SDLC using AI across design, building, testing, securing, and delivery. Exercise: Draft a comparative analysis mapping traditional SDLC phases to an AI-native model, highlighting productivity and quality levers. Relevance: Directly addresses the core responsibility of defining target state SDLC and tooling strategies for enterprise clients.
Value Realization and Engineering Productivity Metrics
Study methodologies for translating high-level transformation ambition into measurable outcomes and productivity uplifts. Exercise: Create a metrics framework template that connects AI tool adoption to tangible business value and developer efficiency gains. Relevance: Aligns with the requirement to drive engineering productivity measures and measurable value realization for C-suite stakeholders.
Enterprise AI Strategy and Architecture
Examine how large organizations formulate platform and tooling strategies for adopting AI at scale. Exercise: Outline a high-level enterprise architecture strategy document proposing agent hierarchies and use-case prioritization for a sample domain. Relevance: Directly matches the requirement to create platform and tooling strategy and architecture as a workstream owner.

Skills to revise

Advisory and C-Suite Communication
Practice framing complex technical transformations for executive audiences like CIOs, CTOs, and CDOs. Exercise: Role-play a 10-minute executive briefing presenting an AI-native software delivery roadmap and addressing potential risk objections. Relevance: Essential for engaging day-to-day with client stakeholders and leading advisory workstreams effectively.
Use-Case and Data-Product Definition
Sharpen practical skills in scoping specific business domains and defining data products or agent hierarchies. Exercise: Write a structured specification document detailing a specific AI use-case, including its required data inputs, agent hierarchy, and expected workflow outputs. Relevance: Directly fulfills the expectation to take defined parts of an engagement end-to-end, such as use-case and data-product definitions.

Your preparation checklist

Review Background Requirements and Experience Alignment
Examine your professional history to ensure you can clearly articulate 5+ years of relevant experience in technology strategy, enterprise advisory, or software engineering transformation.
Draft an AI-Native SDLC Framework Artifact
Prepare a sample target-state software development lifecycle document that incorporates AI-driven tools, automation, and security across design, building, testing, and delivery phases.
Develop a Productivity Measurement and Value Realization Template
Create a structured framework outlining metrics for tracking engineering productivity uplifts and translating AI transformation ambitions into measurable business outcomes for C-suite stakeholders.
Prepare an End-to-End Workstream Case Walkthrough
Document a concrete project example where you took ownership of a specific business domain, defined use cases or agent hierarchies, and acted as the primary point of contact.
Formulate Targeted Questions for the Employer
Prepare thoughtful questions regarding Accenture's Technology Strategy & Transformation practice, typical client engagement structures for AI-native software delivery, and expectations for workstream owners.

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…