Accenture
Global professional services and technology consulting
AI SDLC Manager - Technology Strategy and Transformation
About the job
- A Bachelor's degree is required; an MBA or equivalent postgraduate qualification is a plus.
- At least 10 years of experience in consulting, technology strategy, software engineering transformation, engineering productivity, and AI or generative AI advisory in software delivery.
- Strong experience working with large enterprise software engineering organizations and advising senior technology leaders on software development life cycle transformation, engineering productivity, platform strategy, and operating model change.
- Experience leading current-state assessments, target-state designs, use-case prioritization, value cases, benefits frameworks, transformation roadmaps, and operating model design.
- Practical understanding of artificial intelligence, generative artificial intelligence, and agentic artificial intelligence concepts and the emerging AI-native software development life cycle tool ecosystem, with the ability to engage architects and engineering leaders with technical credibility.
- Experience across technology strategy, engineering productivity, DevOps or DevSecOps, and AI-assisted software engineering.
- At least 4 years of experience leading or managing large teams, including structuring analytical work, facilitating workshops, and developing engineering transformation recommendations.
- Strong facilitation capability with experience in leadership workshops, design thinking sessions, value stream mapping, architecture discussions, engineering operating model design, and transformation governance forums.
- Strong understanding of enterprise software delivery, including Agile, DevOps, DevSecOps, continuous integration and continuous delivery, test automation, secure software development life cycle, release management, platform engineering, and site reliability engineering concepts.
- Ability to assess software development life cycle maturity, engineering practices, delivery bottlenecks, toolchains, governance, and productivity across large, distributed engineering organizations.
- Strong understanding of engineering productivity, developer experience, software quality, technical debt, application complexity, and delivery value streams.
- Ability to design future-state software development life cycle processes, governance models, and transformation roadmaps aligned to business and engineering outcomes.
- Strong understanding of generative artificial intelligence, large language models, and agentic software engineering concepts, including prompt engineering, context engineering, retrieval-augmented generation, model context protocol, agent orchestration, guardrails, and evaluations.
- Practical understanding of AI-assisted software development life cycle use cases across requirements, backlog, architecture, coding, code review, refactoring, testing, security, documentation, release support, and operations handover.
- Familiarity with leading AI coding and software development life cycle tools such as GitHub Copilot, OpenAI Codex, Claude Code, Cursor, Windsurf, GitLab Duo, Amazon Q Developer, Gemini Code Assist, Sourcegraph Cody, and Atlassian Rovo or equivalent tools.
- Familiarity with AI platforms and agentic orchestration tools such as Microsoft Azure AI Foundry, Amazon Bedrock, Google Vertex AI, LangGraph, LlamaIndex, Semantic Kernel, AutoGen or CrewAI, and LangSmith or MLflow or equivalent platforms.
- Understanding of enterprise context and knowledge architectures using code indexing, embeddings, vector databases, enterprise search, knowledge graphs, retrieval-augmented generation, and model ecosystems including OpenAI, Anthropic, Gemini, Llama, and open-source models.
- Ability to define toolchain architecture, vendor evaluation criteria, integration patterns, pilot approaches, rollout considerations, and governance required for scalable AI-native software development life cycle adoption.
- Ability to formulate AI-native software development life cycle value cases across productivity, cycle time, quality, risk, cost, developer experience, and business agility outcomes.
- Strong understanding of productivity measurement approaches including DORA, SPACE, flow metrics, adoption telemetry, developer surveys, quality metrics, and release metrics.
- Ability to assess AI economics, including license cost, token consumption, model usage, cost to serve, utilization, chargeback or showback, and benefit realization governance.
- Ability to connect AI adoption choices to measurable business outcomes and executive-level investment decisions.
- Strong consulting toolkit across structured problem solving, hypothesis-led analysis, benchmarking, assessment design, facilitation, executive storytelling, business case development, and roadmap definition.
- Ability to drive C-suite conversations with credibility, influence senior stakeholders, and align business, engineering, architecture, security, finance, and technology teams.
- Ability to independently lead client discussions, develop thought leadership, create compelling transformation narratives, and shape opportunities in ambiguous environments.
- Experience leading proposals, strategic pursuits, senior client relationships, and high-performing consulting teams.
- Lead current-state software development life cycle maturity, AI readiness, and engineering productivity assessments across large engineering organizations.
- Define target-state AI-native software development life cycle ambition and strategy across all stages of software delivery and software maintenance.
- Conduct application landscape assessments to identify applications, products, and teams best suited for AI-native and agentic software development life cycle adoption based on value, feasibility, risk, and readiness.
- Identify, structure, and prioritize artificial intelligence and agentic use cases across the software delivery life cycle, linking them to engineering productivity, quality, and measurable outcomes.
- Facilitate senior stakeholder alignment on adoption priorities, transformation choices, and approach.
- Design AI-native delivery workflows and agentic patterns across all stages of the software development life cycle.
- Define AI-native software development life cycle platform reference architecture across application life cycle management, integrated development environments, repositories, continuous integration and continuous delivery, DevSecOps, testing, observability, knowledge systems, model gateways, retrieval-augmented generation or context layers, model context protocol or tool interfaces, and guardrails with responsible artificial intelligence frameworks built into the design.
- Advise clients on AI platform, toolchain, and vendor strategy, including integration approach, security, data and intellectual property considerations, cost, and developer adoption.
- Define architecture principles, reusable patterns, guardrails, governance constructs, and agentic evaluation criteria required to scale AI-native engineering responsibly.
- Work with architecture, platform, security, and engineering teams to connect strategy with implementation realities while maintaining an advisory and transformation-planning focus.
- Build AI-native software development life cycle value cases covering productivity, quality, risk, experience, and cost.
- Define benefits frameworks and productivity measurement approaches using baselines, key performance indicators, telemetry, DORA, SPACE, flow metrics, adoption metrics, and executive reporting.
- Define AI value economics and cost governance across licensing, token consumption, model usage, chargeback or showback, cost guardrails, and benefit realization.
- Design the target engineering operating model covering product and platform ownership, an AI software development life cycle center of excellence, roles and responsibilities, governance forums, enablement, and change adoption.
- Translate target-state architecture and operating models into phased transformation roadmaps covering quick wins, foundational enablers, pilots, scaling waves, and governance milestones.
- Lead C-suite-level conversations on AI-native software development life cycle strategy, engineering productivity, platform choices, tooling strategy, value realization, governance, and operating model change.
- Create expert content and points of view and use executive storytelling, presentation, and communication skills for C-level discussions.
- Support strategic pursuits, client account development, and growth initiatives through differentiated advisory propositions, points of view, solution narratives, and proposal leadership.
- Collaborate across capabilities and teams to shape holistic client solutions and connect AI-native software development life cycle transformation with broader enterprise reinvention priorities.
- Develop thought leadership, market perspectives, offering assets, and reusable modernization frameworks.
- Lead and mentor teams on artificial intelligence, modern engineering, software delivery, and engineering productivity measures.
- Prior experience in consulting, technology strategy, or client-facing advisory roles is strongly preferred.
About the company
Accenture is a global professional services firm that helps companies navigate technology-driven change. It offers strategies and services across consulting, digital, technology, and operations, with a strong emphasis on cloud, artificial intelligence, security, and enterprise reinvention. Accenture works by delivering end-to-end solutions, combining advisory work with implementation, technology platforms, and managed services to transform how organizations operate and compete in today’s digital world. The company differentiates itself through its long history as a dedicated tech-advisory arm that gained independence in 2001, its scale, and its active acquisitions—particularly since 2013—to expand capabilities in digital, cloud, and security. Its goal is to help the world’s largest corporations rethink and reshape their operations to stay ahead of rapid technological shifts.
Company Size
10,001+
Company Stage
IPO
Headquarters
Dublin, Ireland
Founded
1989
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Simplify's Take
What believers are saying
- Q3 fiscal 2026 revenue reached $18.72 billion, and operating margin widened to 17%.
- Accenture logged 104 bookings above $100 million through Q3 fiscal 2026, up 13%.
- Cybersecurity revenue hit $10 billion in fiscal 2025, and Edge expands that momentum.
What critics are saying
- Q3 fiscal 2026 bookings fell 3% locally, signaling softer demand despite 2025 AI hype.
- Consulting grew only 1% locally in Q3 fiscal 2026; federal spending and geopolitics dragged results.
- June 2026 securities investigation and repeated restructuring costs pressure sentiment and distract management.
What makes Accenture unique
- Accenture combines strategy, delivery, and managed services across 40-plus industries globally.
- June 2026 launch of Accenture Edge targets mid-market firms with AI-ready, prebuilt solutions.
- August 2026 COMWARE acquisition and Dragos stake deepen SAP and critical-infrastructure cybersecurity capabilities.
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Benefits
Health Insurance
Professional Development Budget
401(k) Retirement Plan
401(k) Company Match
Company News
Accenture trades 37% below its 52-week high after falling 27% over the past year. In fiscal Q3 2026, new bookings declined and some managed services deals slipped into fiscal 2027. The company generated $73.1 billion in revenue over the past twelve months, with operating margins widening to 17% in Q3. Managed services revenue grew 5% in local currency. In the first nine months of fiscal 2026, clients made 104 quarterly bookings exceeding $100 million, up 13% year-on-year. Consulting revenue grew just 1% in local currency in Q3. Management attributes the slowdown to the Middle East conflict affecting regional and discretionary spending. The CEO acknowledged that client budgets have not been growing, with spending simply being redirected rather than increased.
Accenture has appointed Emma Chalwin as Chief Marketing Officer, effective 1 October 2026. She will oversee global marketing and communications, reporting to Chair and CEO Julie Sweet. Chalwin joins from Workday, where she served as CMO. She previously held senior marketing roles at Salesforce, Adobe, McAfee, and Macrovision, bringing over 30 years of global marketing leadership experience. Sweet said Chalwin is a "proven growth leader" who understands how to translate breakthrough technologies into client value. Chalwin expressed excitement about helping shape Accenture's next chapter as organisations reimagine growth in the age of AI. Chalwin has been recognised on Forbes' World's Most Influential CMOs list and is a member of the Fortune Most Powerful Women network.
Accenture is shifting its growth strategy, de-emphasising LearnVantage, a learning and training business it built and promoted heavily in 2024. The company mentioned LearnVantage only once on its June 2026 earnings call, listing it amongst AI enablers rather than highlighting it as a standalone growth driver. Meanwhile, Accenture is redirecting resources toward cybersecurity, which has grown from $700 million in fiscal 2016 to $10 billion in fiscal 2025. The company is acquiring a majority stake in an operational technology security specialist, alongside two smaller firms, combining them into a platform for critical infrastructure. These assets generate $208 million in annual recurring revenue, growing at 48%. Accenture's acquisition budget for fiscal 2026 is approximately $9 billion against $73.1 billion in trailing twelve-month revenue. The company has also launched Accenture Edge, targeting mid-market companies. The stock is down 24.3% over the past year.
Central banks in Asia and Europe are tightening policy, yet cash deposits offer limited returns. This creates opportunities for dividend-focused investors seeking reliable income streams. Three stocks from the Dividend Powerhouses screener currently offer yields above 5% with covered, growing payouts. The screener identifies 1,851 companies with compelling dividend profiles. Accenture, a global consulting and technology services firm, offers a 3.48% yield backed by strong free cash flow. The company generates revenue primarily from Products clients (US$22.3 billion), Health & Public Service (US$14.9 billion), and Financial Services (US$13.8 billion). Its market capitalisation stands at approximately US$111 billion. The firm is reshaping itself for the AI era through acquisitions and partnerships with Google Cloud and ServiceNow. Key risks include whether AI and slower IT spending will reshape consulting economics faster than Accenture can adjust.
Accenture has agreed to acquire COMWARE Co., Ltd., a Tokyo-based provider of end-to-end technology services for mid-market companies.