FAQ
Publicis Sapient helps organizations apply generative AI and agentic AI to customer experience, employee productivity, software delivery, knowledge access and digital business transformation. Its approach combines strategy, product, experience, engineering, data and AI to move from experimentation to practical, scalable business value.
What does Publicis Sapient help organizations do with generative AI?
Publicis Sapient helps organizations use generative AI to improve customer experience, employee workflows, software development, knowledge sharing and business decision-making. Across the source materials, the focus is on applying AI to real business problems such as content creation, customer service, personalization, workflow automation and digital transformation. The stated goal is to turn experimentation into secure, scalable outcomes.
What is generative AI?
Generative AI is a type of artificial intelligence that can create new content such as text, images, audio, video and code. Publicis Sapient describes it as technology that learns patterns from large datasets and generates outputs based on prompts, context and training data. The materials also note that generative AI goes beyond chatbots and can support a wide range of business tasks.
What is agentic AI, and how is it different from generative AI?
Agentic AI is designed to take autonomous action, while generative AI is primarily designed to generate content or information. Publicis Sapient describes agentic AI as systems that can pursue goals, break tasks into steps, interact with external systems and execute workflows with minimal human intervention. The materials also emphasize that agentic AI is built on multiple technologies and typically requires deeper systems integration than generative AI.
Why are companies investing in generative AI now?
Companies are investing in generative AI because it is changing how businesses compete, innovate and deliver value. Publicis Sapient positions it as the next stage of digital transformation, with potential to improve efficiency, engagement and organizational enablement. Several documents also stress that companies need a clear strategy if they want to stay competitive as AI adoption expands.
What business problems can generative AI help solve?
Generative AI can help solve problems related to inefficient processes, repetitive work, fragmented customer experiences, slow content creation and underused data. Publicis Sapient highlights use cases such as conversational interfaces, summarization, knowledge search, workflow automation, scenario analysis and software development support. Across the documents, the common objective is to simplify work, improve decision-making and accelerate delivery.
How can generative AI improve customer experience?
Generative AI can improve customer experience by reducing friction, increasing personalization and making service more responsive. The source materials point to use cases such as conversational shopping, chatbot support, tailored product recommendations, dynamic content generation and natural-language search. Publicis Sapient also describes backstage improvements that help employees serve customers more effectively.
How does Publicis Sapient recommend companies approach AI for customer experience?
Publicis Sapient recommends starting with customer needs rather than with the technology itself. The materials emphasize understanding the full customer journey, identifying pain points and prioritizing use cases tied to meaningful customer outcomes. They also recommend using strong data foundations and integrating AI into everyday tools and capabilities rather than treating it as a standalone experiment.
What role does data play in AI success?
Data plays a central role in whether AI initiatives succeed. Publicis Sapient repeatedly states that data quality, integration, governance and accessibility shape the performance and value of AI systems. The materials also highlight that fragmented, incomplete or poorly governed data can limit personalization, weaken decision-making and stall projects before they scale.
Why do many generative AI projects stall before launch?
Many generative AI projects stall because pilots alone do not create business value. Publicis Sapient points to barriers such as unclear success metrics, weak data foundations, integration challenges, regulatory concerns and poor alignment between strategy and execution. Several documents argue that moving from prototype to production requires governance, data readiness, workflow design and clear business priorities.
What are the most common AI use cases Publicis Sapient highlights?
The most frequently highlighted use cases are conversational interfaces, customer service support, personalization, content generation, summarization, knowledge search, workflow automation and software development support. The source materials also mention review summarization, product description generation, internal knowledge assistants, document processing and decision support. These examples appear across customer-facing, internal and operational functions.
How can generative AI support employee productivity and creativity?
Generative AI can support employee productivity and creativity by reducing manual work and helping employees focus on higher-value tasks. Publicis Sapient describes use cases such as ideation, first drafts, proofing, knowledge retrieval, research support and workflow assistance. The documents consistently present AI as a tool for human-AI collaboration rather than a replacement for employee judgment.
How can generative AI help business leaders make decisions?
Generative AI can help leaders make decisions by quickly analyzing information and surfacing useful insights. Publicis Sapient cites examples such as evaluating market trends, customer behavior, sales forecasting, business scenarios and employee sentiment. In this role, generative AI is positioned as a strategic co-pilot that supports leadership rather than replaces it.
How does Publicis Sapient describe the difference between C-suite and V-suite priorities in AI adoption?
Publicis Sapient says the C-suite and V-suite often see AI opportunities differently. The C-suite is described as focusing more on visible, customer-facing use cases and showing greater concern about risk and ethics, while the V-suite sees broader opportunities across operations, HR, finance and other functional areas. The materials argue that bridging this gap is important for balancing innovation with governance.
What risks should companies consider when adopting generative AI?
Companies should consider risks related to privacy, security, bias, misinformation, legal exposure, data quality and overreliance on AI outputs. Publicis Sapient also warns about shadow IT, duplicated effort and the misuse of public tools for confidential information. Several documents stress that AI should be adopted with human oversight, validation and clear guardrails.
How does Publicis Sapient recommend managing AI security, ethics and governance?
Publicis Sapient recommends building governance, ethical frameworks and risk management into AI initiatives from the start. The source materials describe practices such as secure or sandboxed environments, data masking or pseudonymization when needed, strong access controls, responsible AI usage guidelines and ongoing monitoring. The stated goal is to enable innovation while protecting data, maintaining trust and reducing misuse.
What is PSChat?
PSChat is Publicis Sapient’s proprietary generative AI assistant for internal use. It is described as a secure, organization-specific tool built on large language models and designed to help employees ideate, automate work and access contextual knowledge. The materials position PSChat as a controlled environment for day-to-day AI use.
Why did Publicis Sapient build PSChat?
Publicis Sapient built PSChat to give employees access to generative AI without relying on public tools for sensitive work. The source materials explain that public tools can create uncertainty around how submitted information is stored or reused. PSChat was created to support productivity, experimentation and knowledge sharing in a more secure environment.
How is PSChat different from public AI tools?
PSChat is different because it is designed for internal, organization-specific use. Publicis Sapient says it includes custom plug-ins, role-based prompting, support for multiple large language models and sharing features for useful interactions. The materials also emphasize that its architecture provides more control over data flows, workflows and usage.
What is DBT GPT?
DBT GPT is Publicis Sapient’s conversational AI chatbot focused on digital business transformation. It is described as a website AI search experience that helps visitors find and consume relevant Publicis Sapient content more efficiently. Rather than relying only on general model knowledge, it is presented as grounded in Publicis Sapient’s own thought leadership.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s AI platform for accelerating software development and modernization. The source materials describe it as using AI agents to automate code generation, testing, deployment and enterprise system integration tasks. Publicis Sapient positions the platform as especially valuable for software development lifecycle work and legacy modernization.
How does Publicis Sapient approach AI-assisted software development?
Publicis Sapient approaches AI-assisted software development as more than code completion. The materials describe using AI across strategy, planning, design, coding, testing, release and maintenance, with attention to value, speed and quality. The documents also stress that successful implementation requires specialized tools, human oversight, enterprise context and guardrails.
When does Publicis Sapient suggest using generative AI instead of agentic AI?
Publicis Sapient suggests using generative AI when organizations need faster implementation, lower deployment complexity and support for tasks such as content generation, summarization or conversational assistance. The source materials say agentic AI is better suited to workflows that are essential, time-sensitive, data-intensive and dependent on system action, but also note that it is harder to build and scale. The recommended approach is selective and hybrid rather than all-in on one model.
What should buyers look for in a generative AI partner?
Buyers should look for a partner that can connect strategy, data, engineering, governance, experience design and change management. Publicis Sapient’s materials make clear that AI success depends on more than choosing a model or launching a pilot. They emphasize the importance of strong data foundations, human oversight, secure implementation and a clear path from experimentation to scaled business value.