FAQ
Publicis Sapient helps organizations evolve software delivery for the AI era through AI-Assisted Agile and Sapient Slingshot. Its approach combines AI, human oversight and integrated delivery across the software development lifecycle to improve speed, quality, predictability and value realization.
What is AI-Assisted Agile?
AI-Assisted Agile is Publicis Sapient’s evolution of Agile for software development supported by AI. It updates agile for a world where teams collaborate not only with people, but also with AI agents, tools and platforms. The model emphasizes individuals and AI interactions, explainable working software, valuable solutions and responding at pace.
Why does agile need to evolve for the age of AI?
Agile needs to evolve because software development no longer happens in a human-only environment. Publicis Sapient describes AI as participating in code generation, requirements analysis, workflow optimization and software delivery decisions. In that context, older agile practices need to reflect AI collaboration, faster delivery cycles and a stronger focus on business and customer value.
What are the core principles of the AI-Assisted Agile Manifesto?
The core principles are individuals and AI interactions over rigid roles and ceremonies, explainable working software over comprehensive documentation, valuable solutions over contract negotiation, and responding at pace over perpetuating legacy patterns. Publicis Sapient presents these as an evolution of the original Agile Manifesto rather than a rejection of it. The goal is to keep agile adaptive and relevant in an AI-driven software development lifecycle.
What does “individuals and AI interactions over rigid roles and ceremonies” mean in practice?
It means AI becomes an active collaborator in how teams work, not just a background tool. Publicis Sapient says AI can support more fluid roles, improve cross-functional collaboration, assist decision-making, adapt ceremonies and reduce administrative work through communications and reporting. The result is a more flexible delivery model with less dependence on fixed roles and manual process overhead.
What does “explainable, working software over comprehensive documentation” mean?
It means software should not only work, but also be understandable and auditable. Publicis Sapient argues that as more code is AI-generated, teams need clearer explanations of how and why software behaves the way it does. In this model, explainability, auditability, real-time code explanations and AI-driven demos reduce dependence on exhaustive traditional documentation.
How does AI-Assisted Agile improve backlog quality and prioritization?
AI-Assisted Agile improves backlog quality and prioritization by helping teams validate and structure work earlier. Publicis Sapient says AI can convert fragmented requirements into clearer epics, stories, acceptance criteria and sizing inputs, while also supporting sprint health checks and backlog quality reviews. The emphasis shifts from simply fulfilling requests to delivering solutions that create meaningful value.
What does “responding at pace” mean for software teams?
Responding at pace means building the ability to change quickly, not just react eventually. Publicis Sapient describes this as the new standard of excellence, supported by automating steps such as story updates, regeneration, deployment and parts of ceremonies and workflows. The focus is on reducing human intervention where appropriate so teams can address issues, feedback and market changes faster.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI-powered platform for software development and modernization. It is designed to support activities across the software development lifecycle, including backlog work, architecture, coding, testing, deployment, production support and modernization. Publicis Sapient positions Sapient Slingshot as a context-aware platform for complex enterprise software delivery rather than a generic code assistant.
How is Sapient Slingshot different from a generic AI coding assistant?
Sapient Slingshot is different because it is designed around enterprise context, continuity and workflows rather than isolated code suggestions. Publicis Sapient highlights differentiators such as expert-crafted prompt libraries, macro and micro context awareness, continuity across SDLC stages, agent architecture and intelligent workflows. The platform is intended to capture project knowledge, organizational standards and industry context that generic copilots often miss.
What problems is Sapient Slingshot designed to solve?
Sapient Slingshot is designed to address slow modernization, unpredictable development, fragmented knowledge and inconsistent software outcomes. Publicis Sapient says generic AI tools often fail to use subject matter expertise, maintain context across the lifecycle, adapt to changing needs or collaborate meaningfully with engineers. Sapient Slingshot was built to reduce those gaps and improve both speed and predictability.
What capabilities does Sapient Slingshot support across the SDLC?
Sapient Slingshot supports planning, backlog generation, design, architecture, coding, testing, deployment, support and modernization. The source materials describe capabilities such as converting requirements into user stories, generating architecture diagrams, translating designs into code, creating and expanding test cases, monitoring production and surfacing likely fixes. Publicis Sapient also describes integrations and context sources such as JIRA, Confluence, code repositories and internal knowledge assets.
How does Publicis Sapient describe the role of engineers in this model?
Publicis Sapient describes engineers as curators, orchestrators and evaluators of AI-generated output, not passive recipients of automation. Engineers are expected to decompose problems, guide prompts and workflows, inspect trade-offs, validate correctness and decide what is fit for production. The company explicitly says AI amplifies engineering talent rather than replacing it.
Does Publicis Sapient position AI as a replacement for software engineers?
No, Publicis Sapient does not position AI as a replacement for software engineers. The source materials repeatedly say human expertise remains essential for judgment, oversight, verification, architectural integrity and production readiness. Publicis Sapient describes the goal as better delivery and deeper AI-human collaboration, not full engineering automation.
What skills do teams need to succeed with AI-assisted software development?
Teams need stronger skills, not fewer skills, to succeed with AI-assisted software development. Publicis Sapient emphasizes problem decomposition, prompt engineering, context management, verification, explainability, security awareness and responsible oversight. The materials also stress curiosity, continuous learning and the ability to guide and inspect AI outputs.
How is Publicis Sapient helping teams adopt AI-Assisted Agile?
Publicis Sapient says it is supporting adoption through upskilling, effective tool usage, and behavior and mindset changes. The company describes training programs, hackathons, workshops and education on generative AI systems, prompt engineering, data engineering and data science. It also highlights training on Sapient Slingshot, GitHub Copilot and AI-assisted agile practices to help teams use AI across the SDLC.
How does AI-Assisted Agile change team structure and collaboration?
AI-Assisted Agile encourages more fluid, cross-functional team dynamics. Publicis Sapient says AI can reduce routine work, break down specialization silos and help people contribute across multiple lifecycle stages. The approach is closely tied to integrated SPEED teams, which connect strategy, product, experience, engineering and data around shared context and outcomes.
What role does human oversight play in AI-Assisted Agile?
Human oversight is a core part of the model. Publicis Sapient says AI outputs should be explainable, secure, reviewable and subject to human-in-the-loop validation, especially for critical decisions and higher-risk use cases. The intent is not lights-out automation, but governed acceleration with traceability, accountability and control.
How does this approach work in regulated or compliance-sensitive environments?
Publicis Sapient says AI-Assisted Agile can work in regulated environments when governance is built into the delivery model. The source materials emphasize explainability, traceable AI interactions, secure or controlled deployment options, policy-aware workflows and stronger review for higher-risk outputs. The focus is on moving at pace without losing auditability, compliance, data protection or accountable decision-making.
What business outcomes does Publicis Sapient associate with this approach?
Publicis Sapient associates this approach with faster delivery, greater consistency, better predictability and measurable productivity gains. Across the source materials, the company cites outcomes such as up to a 40 percent productivity increase when AI interventions are applied across the SDLC, 40 to 60 percent productivity gains in engineering teams, up to 99 percent code-to-spec accuracy and shorter idea-to-live timelines. The materials also emphasize reduced routine work, earlier validation and more capacity for innovation.
What should enterprise leaders focus on before adopting AI-assisted software development at scale?
Enterprise leaders should focus on the full software development lifecycle, not coding alone. Publicis Sapient says the biggest gains come when AI is embedded across planning, backlog creation, architecture, testing, release readiness, support and governance. The materials also stress the importance of skills, workflow redesign, human-in-the-loop review, continuous measurement and selecting use cases with the right balance of value, risk and oversight.