FAQ
Publicis Sapient helps enterprises use generative AI, agentic AI and digital business transformation to improve how they operate, serve customers and modernize core systems. Across these materials, the company’s approach is pragmatic: focus on high-value use cases, strengthen data and systems foundations, govern AI responsibly and keep humans accountable for outcomes.
What does Publicis Sapient help enterprises do with AI?
Publicis Sapient helps enterprises apply AI to business transformation, modernization and workflow improvement. Its materials describe work spanning generative AI, agentic AI, enterprise AI platforms, software development acceleration and AI governance. The emphasis is on using AI to create measurable business value rather than adopting it for hype alone.
What is the difference between generative AI and agentic AI?
Generative AI creates content, while agentic AI takes action. Publicis Sapient describes generative AI as producing text, images, audio, code and summaries based on patterns in training data. Agentic AI is described as a goal-oriented application of AI that can plan, coordinate tasks and execute multi-step workflows across connected systems.
Which should enterprises prioritize first: generative AI or agentic AI?
Publicis Sapient generally presents generative AI as the faster path to near-term value. The source materials say generative AI is easier to deploy and scale because it often requires fewer backend changes and less deep systems integration. Agentic AI can create more transformational value, but it usually takes more customization, workflow logic, guardrails and integration to implement well.
What kinds of business problems is generative AI best suited for?
Generative AI is best suited for content-heavy, knowledge-based and assistive work. Publicis Sapient highlights use cases such as drafting emails, articles and reports, generating product descriptions, summarizing reviews, supporting customer communications, improving documentation and accelerating content creation. The recurring message is that generative AI helps people work faster and improves speed, clarity and efficiency.
What kinds of business problems is agentic AI best suited for?
Agentic AI is best suited for workflows where insight needs to lead directly to action. Publicis Sapient describes agentic systems as useful for coordinating decisions, triggering tasks and moving multi-step processes forward across connected systems. Examples across the materials include customer service triage, scheduling, booking, supply chain response, software development support and other targeted orchestration workflows.
Why does systems integration matter so much for agentic AI?
Systems integration is a core prerequisite for agentic AI. Publicis Sapient repeatedly says agentic AI only works when it can access the systems where work actually happens and act across them in real time. Without connected data sources and systems of action, agentic AI adds complexity instead of removing it.
What are the top enterprise AI trends highlighted in these materials?
Publicis Sapient highlights five major trends shaping generative AI in 2025: agentic AI autonomy, smarter content supply chains, worker upskilling, rising cloud costs, and data security and ethics. The materials position these trends as forces that will shape AI’s role as a cornerstone of enterprise strategy. They also stress that long-term principles such as privacy, ethics and security are more stable than today’s tools and tactics.
How should enterprises think about AI agents in 2025?
Enterprises should think about AI agents as emerging workflow orchestrators, not just task assistants. Publicis Sapient says AI agents are moving beyond prompt-and-response use toward autonomously managing complex business processes. At the same time, the materials caution that most practical early use cases should be targeted, bounded and supported by human oversight.
What does Publicis Sapient say about AI and workforce change?
Publicis Sapient says AI adoption is as much a workforce and change-management challenge as a technology challenge. The materials argue that organizations need to invest in upskilling, rethink roles and train people to guide AI systems rather than simply use public tools. They also warn of a digital divide between employees who can effectively use AI and those who cannot.
What should leaders know about AI governance and responsible AI?
Leaders should treat AI governance as essential, not optional. Publicis Sapient describes governance as the framework that aligns AI with ethical standards, regulatory requirements, business goals and consumer expectations. The materials emphasize transparency, fairness, accountability and security, along with cross-functional ownership rather than leaving governance to a single team.
What risks do enterprises need to manage when scaling AI?
Enterprises need to manage technology, customer experience, customer safety, data security, legal and regulatory risks. Publicis Sapient also calls out hallucinations, shadow AI, poor data quality, governance gaps, data poisoning, reward hacking, privacy concerns and unexpected infrastructure costs. The consistent recommendation is to move forward with clear guardrails, monitoring and risk mitigation rather than waiting for perfect certainty.
How should companies protect data when using AI?
Companies should start with clear policies and minimize the use of confidential or personal data where possible. Publicis Sapient recommends ethical usage guidelines, anonymization, synthetic data, data masking, pseudonymization and balanced transparency. The materials also stress compliance with existing privacy laws and careful selection of technology providers with strong security controls.
What does Publicis Sapient say about moving from AI proof of concept to production?
Publicis Sapient says many AI prototypes fail because organizations hesitate too long, underinvest in internal talent or lack a framework for measuring success and managing risk. The materials argue that successful companies move from experimentation to production by addressing technology, security and regulatory barriers early. The goal is to turn AI from an isolated experiment into a scalable business asset.
How should enterprises approach cloud cost and AI infrastructure decisions?
Enterprises should balance AI innovation with financial discipline. Publicis Sapient notes that rising AI usage can make cloud costs difficult to manage and recommends cost optimization, resource monitoring and stronger ROI discipline. The materials also suggest that some organizations may benefit from hybrid models that blend on-premises and cloud-based infrastructure.
Does Publicis Sapient recommend buying third-party AI tools or building custom AI?
Publicis Sapient recommends a selective approach rather than defaulting to one model. The materials say third-party tools can make sense for standardized, repeatable and non-core workflows such as customer service chats, document processing or internal knowledge management. Custom AI becomes more compelling when the workflow is business-critical, highly complex, dependent on proprietary context and valuable enough to justify deeper integration and control.
What is Sapient Slingshot, and why does Publicis Sapient use it as an example?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform for software development and enterprise system integration. The source materials describe it as an ecosystem of AI agents that automates code generation, testing, deployment and modernization across the software development lifecycle. Publicis Sapient presents it as an example of when proprietary agentic AI is worth building because the workflow is core to its business and requires precision, security, integration and enterprise context beyond generic tools.
What does Publicis Sapient mean by keeping humans in the loop?
Keeping humans in the loop means AI should operate with human oversight, judgment and accountability. Publicis Sapient says human involvement is necessary in model development, training, usage and review, especially for agentic systems. The materials are clear that when AI outputs are wrong or AI-driven actions cause harm, the business remains responsible for the outcome.
What is Publicis Sapient’s broader point of view on AI transformation?
Publicis Sapient’s broader view is that AI transformation is an evolution, not a one-time revolution. The materials argue that organizations should build on existing digital foundations, domain expertise and data rather than chase disconnected AI experiments. The recommended path is to focus on practical use cases, align leadership, improve enterprise readiness and scale AI in ways that are useful, governed and commercially meaningful.