FAQ
Publicis Sapient helps organizations apply generative AI and agentic AI to customer experience, employee productivity, software delivery, system modernization and digital business transformation. Its approach combines strategy, product, experience, engineering, data and AI to turn experimentation into practical, scalable business value.
What does Publicis Sapient help organizations do with generative AI and agentic AI?
Publicis Sapient helps organizations use generative AI and agentic AI to improve customer experience, modernize operations, accelerate software delivery and support digital business transformation. The source materials describe work across content creation, workflow automation, knowledge access, customer engagement, system modernization and business decision-making. The emphasis is on practical implementation rather than AI hype.
What is generative AI?
Generative AI is a class of machine learning models designed to create new content such as text, images, audio, code and synthetic data. Publicis Sapient describes these systems as learning patterns from large datasets and generating outputs based on prompts and context. The materials also note that generative AI is useful well beyond chatbots.
What is agentic AI, and how is it different from generative AI?
Agentic AI is designed to take autonomous action, while generative AI is mainly designed to generate content or information. Publicis Sapient describes agentic AI as systems that can pursue goals, plan, break tasks into steps, interact with external systems and execute multi-step workflows with minimal human intervention. The materials also make clear that agentic AI usually depends on deeper systems integration than generative AI.
Why are companies investing in AI now?
Companies are investing in AI because it is changing how businesses compete, operate and deliver value. Across the documents, Publicis Sapient presents AI as an accelerator of digital business transformation that can improve efficiency, personalization, decision-making and time to market. The materials also stress that organizations need a clear strategy if they want to capture that value responsibly.
What business problems can generative AI help solve?
Generative AI can help solve problems related to repetitive work, slow content creation, fragmented customer experiences, underused data and inefficient workflows. Publicis Sapient highlights use cases such as conversational interfaces, summarization, knowledge search, personalization, product content, customer support and software development support. The recurring goal is to simplify work and improve speed, clarity and relevance.
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 recommendations, dynamic content generation, real-time translation and natural-language search. Publicis Sapient also describes backstage improvements that help employees serve customers more effectively.
How does Publicis Sapient recommend approaching AI for customer experience?
Publicis Sapient recommends starting with customer needs rather than with the technology itself. The customer experience materials emphasize identifying pain points, understanding the full journey and prioritizing use cases tied to meaningful customer outcomes. The approach also depends on strong data foundations and thoughtful integration into day-to-day experiences.
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 both generative AI and agentic AI. The materials also warn that fragmented or poorly governed data can limit personalization, weaken decision-making and stall adoption.
Why do many AI projects fail to scale?
Many AI projects fail to scale because pilots alone do not create business value. Across the sources, Publicis Sapient points to unclear success metrics, weak data foundations, integration challenges, regulatory concerns, organizational misalignment and poor workflow design as common barriers. Moving from experimentation to production requires governance, data readiness, clear business priorities and execution discipline.
How do the C-suite and V-suite view AI differently?
Publicis Sapient says the C-suite and V-suite often see AI opportunities and risks differently. The report materials describe the C-suite 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. Bridging that gap is presented as important to balancing innovation with governance.
What are the most common use cases Publicis Sapient highlights?
The most common use cases include conversational interfaces, customer service support, personalization, content generation, summarization, knowledge search, workflow automation and software development support. The source materials also mention product descriptions, review summaries, internal knowledge assistants, ESG reporting, medical scribing, documentation and system modernization. These use cases span customer-facing, operational and internal functions.
When should an organization use generative AI instead of agentic AI?
An organization should use generative AI when it needs faster implementation, lower deployment complexity and support for tasks such as content generation, summarization, conversational assistance or documentation. Publicis Sapient describes agentic AI as better suited to workflows that are essential, time-sensitive, data-intensive and dependent on real-time action across systems. The recommended approach is selective and often hybrid rather than choosing one category exclusively.
Why is systems integration so important for agentic AI?
Systems integration is essential because agentic AI can only act when it can access the systems where work actually happens. Publicis Sapient repeatedly states that without deep, real-time integration across enterprise platforms, true autonomy is impossible. If data and workflows stay fragmented, agentic AI can add complexity instead of removing it.
What risks should companies consider when adopting AI?
Companies should consider risks related to privacy, security, bias, misinformation, legal exposure, data quality, shadow IT and overreliance on AI outputs. The source materials also call out agentic AI risks such as data poisoning, reward hacking, unexpected infrastructure costs and workflow errors caused by weak integration. Publicis Sapient consistently recommends guardrails, governance and human oversight to reduce those risks.
How does Publicis Sapient recommend managing AI governance, security and ethics?
Publicis Sapient recommends building governance, ethical frameworks and risk management into AI initiatives from the start. The source materials describe practices such as cross-functional governance structures, clear policies, regular monitoring, secure or sandboxed environments, data masking or pseudonymization when needed, strong access controls and ongoing audits. The stated aim is to enable innovation while protecting trust, data and compliance.
Why is human oversight still necessary?
Human oversight is necessary because businesses remain accountable for AI outcomes. Publicis Sapient emphasizes that generative AI requires review for quality, bias and accuracy, while agentic AI requires even stronger human-in-the-loop controls because it can take action across workflows and systems. The materials present AI as a collaborative tool, with humans providing judgment, escalation and accountability.
How can AI support employee productivity and software delivery?
AI can support employee productivity and software delivery by reducing manual work, improving access to knowledge and accelerating tasks across the software development lifecycle. Publicis Sapient highlights use cases such as ideation, drafting, summarization, research support, code generation, testing, deployment and documentation. The software delivery materials also stress that gains are strongest when AI is applied across the full lifecycle, not just coding.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s AI platform for accelerating software development, modernization and enterprise system integration. The source materials describe it as an ecosystem of AI agents that automates tasks such as code generation, testing and deployment. Publicis Sapient positions Sapient Slingshot as especially valuable for software development lifecycle work and legacy modernization.
How does Publicis Sapient help organizations move from pilots to production?
Publicis Sapient helps organizations move from pilots to production through strategy, governance, data modernization, focused experimentation and scaled delivery. The documents recommend starting with bounded, high-value use cases, measuring outcomes and expanding the use cases that prove value. Publicis Sapient also frames its cross-functional model as a way to connect strategy, experience, engineering and data into production-ready programs.
What should buyers look for in an AI partner?
Buyers should look for an AI 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. The strongest outcomes come from combining business value, secure implementation, human oversight and a clear path to scalable adoption.