New opportunity

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

Accenture · Pune, India

About this role

Join our team in Technology Strategy & Transformation for an exciting career opportunity to help our most strategic clients realize exceptional value from AI in software delivery, a boardroom priority for organizations globally and be at the forefront of shaping how enterprises adopt AI-native software engineering.

Responsibilities

Shape the next frontier of enterprise software delivery using AI by working with C-suite stakeholders.
Help enterprises adopt AI-native software delivery at scale and turn transformation ambition into measurable outcomes.
Define AI-powered software delivery transformation strategies and translate them into executable approaches.
Define target state SDLC, create platform and tooling strategy and architecture, engineering productivity measures, delivery operating model change, and measurable value realization.
Take a defined part of an engagement end-to-end as a workstream owner, serving as day-to-day client contact.

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

Skills for this role

Artificial IntelligenceSoftware EngineeringEnterprise AITechnology Strategy

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

AI-Native Software Engineering Strategy
Define and translate AI-powered software delivery transformation strategies into executable approaches for global clients, focusing on redefining how software is imagined, designed, built, tested, secured, and delivered.
C-Suite Stakeholder Management
Engage directly with CIOs, CTOs, CDOs, and engineering leaders to guide enterprise adoption of AI-native software delivery at scale and turn transformation ambitions into measurable outcomes.
Target State SDLC and Architecture Design
Create target state Software Development Life Cycle (SDLC) models, platform and tooling strategies, engineering productivity measures, and delivery operating model changes.
End-to-End Workstream Ownership
Take complete ownership of a defined part of an engagement end-to-end, serving as the day-to-day client contact for components such as agent hierarchies or use-case and data-product definitions.

Likely challenges to think through

Translating High-Level AI Ambitions into Executable Roadmaps
Clients often have broad transformation aspirations without clear technical pathways. The challenge lies in bridging executive-level vision with concrete platform architecture, target state SDLC models, and measurable productivity metrics. Prepare by reviewing frameworks for enterprise AI adoption and software engineering transformation metrics.
Managing Complex Stakeholder Expectations Across C-Suite Roles
Balancing the competing priorities of CIOs, CTOs, and CDOs requires aligning diverse viewpoints on software delivery and risk. Prepare by practicing structured communication techniques to articulate technology strategy and value realization to non-technical and technical executives alike.
Driving End-to-End Workstream Delivery as Primary Client Contact
Serving as the day-to-day client contact while managing an entire engagement workstream demands high autonomy and accountability under tight timelines. Prepare by sharpening project management methodologies, workstream scoping, and proactive client engagement practices.

Practice your answers

Technical practice

Defining AI-Powered Software Delivery Transformation Strategies

What to coverDiscuss how you approach translating high-level enterprise AI ambitions into a structured, executable roadmap for software engineering. Address how you would identify key integration points across the software development life cycle (SDLC) and align them with business outcomes.

Target State SDLC Architecture in an AI-Native Era

What to coverExplain how you would design a target-state SDLC that incorporates AI tools and autonomous agents across design, building, testing, and security phases. Address architectural considerations, tooling choices, and potential friction points during migration.

Measuring Engineering Productivity and Value Realization

What to coverDescribe the key metrics and frameworks you would use to quantify productivity and quality uplift resulting from AI-native software engineering initiatives. Address how to connect developer velocity metrics directly to overarching enterprise value realization.

Designing Agent Hierarchies and Use-Cases for Enterprise Processes

What to coverAs a workstream owner, how would you approach defining use-cases, data products, and agent hierarchies for a specific enterprise domain? Discuss your methodology for scoping requirements, managing data dependencies, and mapping out multi-agent interactions.

Platform and Tooling Strategy for Enterprise AI Adoption

What to coverHow do you evaluate and select platform components and tooling architectures to support enterprise-scale AI adoption? Address considerations such as scalability, security, integration with legacy developer workflows, and governance.

Delivery Operating Model Changes for AI Transformation

What to coverExplain how introducing AI-native software delivery impacts team structures, roles, and delivery operating models. Discuss how you would guide clients through organizational change management to ensure successful adoption.

Behavioural practice

Managing C-Suite Stakeholder Expectations and Ambitions

What to coverDescribe a situation where you had to manage differing priorities or unrealistic expectations among senior executives (such as CIOs or CTOs) regarding a technology transformation. Explain the situation, the specific actions you took to align their vision, and the result.

Owning an Engagement Workstream End-to-End

What to coverProvide an example of a time when you took full end-to-end ownership of a complex workstream as the primary day-to-day client contact. Outline the situation, how you managed day-to-day client communications and deliverables, and the ultimate outcome.

Translating Complex Technical Concepts for Non-Technical Leaders

What to coverShare an experience where you had to explain a complex technical strategy or emerging technology architecture to non-technical business leaders. Detail the situation, the approach you used to make the concepts digestible, and how it influenced their decision-making.

Navigating Resistance to Organizational and Process Change

What to coverRecall a time when stakeholders or client teams resisted changes to their established delivery operating model or tooling processes. Detail the situation, your approach to addressing their concerns constructively, and how you achieved buy-in.

Sharpen your knowledge

Topics to prepare

AI-Native Software Development Life Cycle (SDLC)
Relevance: Essential for defining target-state SDLC models and redesigning how software is imagined, designed, built, tested, secured, and delivered using AI. Exercise: Map out a traditional SDLC phase (such as testing or deployment) and redesign it to incorporate automated AI-driven agents and generative tooling, identifying specific quality and productivity inflection points.
Enterprise AI Value Strategy and Realization
Relevance: Aligns directly with helping C-suite stakeholders turn transformation ambition into measurable outcomes and enterprise value delivery. Exercise: Draft a sample value-realization framework outlining how an enterprise can measure ROI and productivity gains from adopting AI-native software engineering practices across multiple business units.
Delivery Operating Model Transformation
Relevance: Supports the requirement to manage delivery operating model change and act as a workstream owner for strategic client engagements. Exercise: Review organizational design principles for technology advisory and create a change-management outline addressing team structures, skills shifts, and governance for an AI-native transformation.
Platform and Tooling Architecture Strategy
Relevance: Required for creating platform and tooling strategies and architectures that support large-scale enterprise adoption of AI. Exercise: Analyze a hypothetical enterprise technology stack and formulate a tool evaluation matrix comparing different AI coding assistants, agentic frameworks, and integrated development environments.

Skills to revise

Technology Strategy Consulting and Advisory
Relevance: Central to engaging CIOs, CTOs, CDOs, and engineering leaders as a workstream owner and day-to-day client contact. Exercise: Prepare a mock advisory pitch deck structured for a CIO audience, translating complex AI software delivery concepts into clear, actionable enterprise strategy roadmaps.

Your preparation checklist

Prepare an Enterprise AI Strategy Framework Walkthrough
Draft a structured presentation or case study outline demonstrating how you have helped, or would help, enterprises define AI-powered software delivery transformation strategies and translate them into executable approaches.
Design a Target-State SDLC and Tooling Architecture Blueprint
Create a sample architectural diagram and evaluation framework for an AI-native Software Development Life Cycle (SDLC), highlighting platform choices, automated agents, and productivity metrics.
Map Out a Value Realization and Productivity Measurement Model
Develop a concrete model outlining how to measure productivity uplift, quality improvements, and overall enterprise value realization resulting from AI-native software engineering initiatives.
Structure Workstream Ownership and Client Management Examples
Prepare specific examples and talking points demonstrating your ability to act as a workstream owner, manage day-to-day client contacts, and handle end-to-end engagement responsibilities.
Formulate Strategic Questions for the Employer
Prepare thoughtful questions regarding how Accenture's Technology Strategy & Transformation practice engages C-suite stakeholders, scales AI-native adoption, and measures transformation success.

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…