FAQ
Publicis Sapient helps organizations evolve software delivery for the AI era through AI-Assisted Agile and its Sapient Slingshot platform. The 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 traditional agile thinking 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 the Agile Manifesto need to evolve for the age of AI?
The Agile Manifesto needs to evolve because software development no longer happens in a human-only environment. Publicis Sapient says AI now participates in generating code, analyzing requirements, optimizing workflows and supporting business 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 additions to the original agile values rather than a rejection of them. 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 clear 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 customer value and prioritization?
AI-Assisted Agile improves customer value by helping teams validate and prioritize work based on evidence, not only requests or opinions. Publicis Sapient says AI can use A/B testing, generated insights and data-driven analysis to identify which backlog items are most valuable. 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 even 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 platform for software development and modernization. It is designed to support code generation, testing, deployment, backlog work, architecture, modernization and other SDLC activities. Publicis Sapient positions Sapient Slingshot as more than a generic code assistant, describing it as a context-aware platform built for complex enterprise software delivery.
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 five differentiators: expert-crafted prompt libraries, hierarchical context awareness, context continuity across SDLC stages, agent architecture for business decisions and intelligent workflows for complex enterprise problems. 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, inconsistent outputs and fragmented knowledge across enterprise teams. 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, production support and modernization. The source materials describe capabilities such as converting requirements into user stories, generating architecture diagrams, translating designs into code, creating and executing test cases, monitoring production and suggesting fixes. Publicis Sapient also describes integrations with systems such as JIRA, Confluence and code repositories to keep outputs relevant to real delivery work.
Does Publicis Sapient position AI as a replacement for software engineers?
No, Publicis Sapient explicitly positions AI as an amplifier of engineering talent, not a replacement for it. The source materials repeatedly say human expertise remains essential for judgment, oversight, verification, architectural integrity and production readiness. Publicis Sapient describes the engineer’s role as evolving toward curator, orchestrator and evaluator of AI-generated outputs.
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 says people across strategy, product, experience, engineering and data must be able to decompose problems, evaluate AI outputs, manage risk, verify correctness and work with explainability and security in mind. The materials also emphasize training in prompt engineering, context management and continuous learning.
How is Publicis Sapient helping teams adopt this way of working?
Publicis Sapient says it is driving adoption through upskilling, effective tool usage and behavior and mindset changes. The company describes targeted training programs, hackathons, workshops and broader education on generative AI systems, 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 and help break down specialization silos, allowing people to contribute across multiple lifecycle stages and collaborate more closely with product, design and data teams. This model is also reflected in Publicis Sapient’s SPEED approach, which connects strategy, product, experience, engineering and data.
What role does human oversight play in this model?
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, regulated environments and higher-risk use cases. The intent is not lights-out automation, but governed acceleration with traceability, accountability and control.
How does this approach address regulated industries and compliance-sensitive environments?
Publicis Sapient says AI-Assisted Agile can be applied in regulated industries when paired with strong governance, security and auditability. The source materials emphasize logging AI interactions, requiring explanations for outputs, keeping sensitive data in secure environments when needed and maintaining human oversight for critical decisions. They also describe using on-premises or private cloud deployments, customizable security controls and compliance-focused workflows for sectors such as financial services, healthcare and government.
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 40% productivity improvement when AI is applied across the SDLC, 40–60% productivity gains in engineering teams, up to 99% code-to-spec accuracy and faster concept-to-product timelines. The materials also emphasize reduced routine work, improved forecasting 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, proprietary data, workflow redesign, continuous measurement and selecting AI use cases with the right balance of value, risk and oversight.