FAQ
Publicis Sapient helps enterprises understand where generative AI and agentic AI create business value, how those systems differ and what foundations are needed to scale them responsibly. Across these materials, the emphasis is on practical use cases, systems integration, governance, human oversight and selective investment rather than AI hype.
What is the difference between generative AI and agentic AI?
Generative AI is designed to create content, while agentic AI is designed to take action. Publicis Sapient describes generative AI as useful for producing text, images, audio, code and summaries based on patterns in training data. Agentic AI builds on capabilities like generative AI and applies them to goal-oriented workflows that can make decisions, coordinate tasks and execute multi-step processes across connected systems.
What is generative AI best used for in the enterprise?
Generative AI is best used for content-heavy, knowledge-based and assistive work. Publicis Sapient highlights uses such as drafting emails, articles and reports, summarizing information, generating product descriptions, creating marketing content and supporting customer communications. The common advantage is faster deployment and value without always requiring deep changes to enterprise systems.
What is agentic AI best used for in the enterprise?
Agentic AI is best used for workflows that require coordinated execution rather than just recommendations. Publicis Sapient positions it for repetitive, high-volume, time-sensitive and well-bounded processes such as customer service triage, scheduling, booking, documentation, supply chain response, enterprise task orchestration and software development support. Its value comes from connecting decisions to action across systems.
Why is generative AI being adopted faster than agentic AI?
Generative AI is being adopted faster because it is easier to deploy and scale. Publicis Sapient says generative AI can create near-term business value through marketing copy, customer service support and automation without always needing deep enterprise integration. Agentic AI may offer greater long-term upside, but it requires more customization, stronger guardrails and more complex workflow and systems integration.
Why is systems integration so important for agentic AI?
Systems integration is essential because agentic AI cannot operate autonomously without access to the systems where work actually happens. Publicis Sapient repeatedly states that true autonomy depends on deep, real-time connectivity across enterprise platforms, data sources and systems of action. If data and workflows remain fragmented, agentic AI adds complexity instead of removing it.
How does agentic AI work in practice?
Agentic AI works by combining context, planning, system access and execution. Publicis Sapient describes AI agents that gather information, break a high-level goal into subtasks, interact with external systems, trigger actions and continue until the broader objective is completed. The practical difference is that the system does not stop at suggesting the next step; it helps move the workflow forward.
What are the main business benefits of agentic AI?
The main benefits of agentic AI are faster execution, lower manual effort and better responsiveness across workflows. Publicis Sapient also ties agentic AI to cost reduction, operational efficiency, smarter customer interactions and improved speed in areas such as service, supply chain and software delivery. In the strongest use cases, the benefit comes from linking insight directly to action.
What are the biggest risks and challenges with agentic AI?
The biggest challenges include integration complexity, weak data quality, governance gaps and security concerns. Publicis Sapient also calls out risks such as data poisoning, reward hacking and unexpected infrastructure costs. Because agentic AI can take real actions across workflows, the company presents it as requiring more controls and accountability than assistive or content-generation tools.
Why does Publicis Sapient emphasize keeping humans in the loop?
Human oversight is essential because businesses remain accountable for AI outcomes. Publicis Sapient says both generative AI and agentic AI require human involvement in development, training, usage and review, with even stronger oversight for agentic systems. The intended model is not automation without control, but automation with judgment, escalation and intervention when needed.
When should a company choose generative AI instead of agentic AI?
A company should choose generative AI when the goal is to help people understand, draft, summarize, search or communicate faster. Publicis Sapient positions generative AI as a good fit when speed to value matters, human review is acceptable and deep system integration is not necessary. Typical examples include content creation, documentation, knowledge support and customer communication.
When should a company invest in agentic AI instead of generative AI?
A company should invest in agentic AI when the workflow is complex, essential to the business and dependent on fast action across systems. Publicis Sapient says the strongest case for proprietary or deeper agentic investment is when the process relies on analyzing large amounts of data quickly, must happen in real time and is central to business performance. In those cases, the added implementation effort may be justified by the value of automation.
When are third-party agent solutions a practical choice?
Third-party agent solutions are a practical choice for standardized, non-core workflows. Publicis Sapient says ready-made agent platforms can help with customer service chats, document processing and internal knowledge management with minor customization. They may not offer deep system integration, but they can still provide efficiency gains faster and at lower cost than a custom-built solution.
What industries and use cases does Publicis Sapient highlight for generative AI?
Publicis Sapient highlights generative AI use cases across retail, consumer products, financial services, energy and commodities, travel and dining, transportation and mobility, public sector and health. Examples include product descriptions, marketing copy, customer inquiry responses, ESG reporting, personalized travel itineraries, logistics email support, citizen service chatbots and medical scribing. These use cases are positioned as valuable because they improve speed, clarity and efficiency on a faster timeline.
What industries and use cases does Publicis Sapient highlight for agentic AI?
Publicis Sapient highlights agentic AI use cases across retail, consumer products, financial services, energy and commodities, travel and dining, transportation and mobility, public sector and health. Examples include dynamic pricing and restocking, demand-based production planning, personalized financial assistants, carbon credit trading, labor and supply scheduling, route and maintenance optimization, fraud detection and prior authorization automation. These examples are framed as higher-upside opportunities that depend on stronger integration and governance.
How can agentic AI improve customer experience?
Agentic AI can improve customer experience by helping connect insight to action across the customer journey. Publicis Sapient points to service triage and routing, proactive issue resolution, journey orchestration, supply-chain-informed service responses and backstage workflow automation. The focus is on reducing handoffs, improving continuity and helping customers get faster, more actionable resolutions.
How can agentic AI help software development and legacy modernization?
Agentic AI can help software development by automating parts of code generation, testing, deployment and modernization. Publicis Sapient describes this as a way to reduce bottlenecks, speed up the software development lifecycle and make legacy modernization more efficient. The materials repeatedly position software delivery and modernization as some of the clearest examples of agentic AI creating measurable enterprise value.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform for software development and enterprise system integration. The 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 Sapient Slingshot as an example of when a custom agentic platform is worth building because the workflow is core, complex and requires more precision, security and enterprise context than generic tools provide.
Why wasn’t generative AI alone enough for Sapient Slingshot?
Generative AI alone was not enough because system integration work requires precise execution that large language models cannot reliably enforce on their own. Publicis Sapient says Sapient Slingshot needs structured automation for code generation, testing and deployment, along with accuracy, security and performance controls. The company also says off-the-shelf code assistants lacked the customization, security and integration needed for enterprise-scale orchestration.
What does Publicis Sapient recommend as the practical roadmap from generative AI to agentic AI?
Publicis Sapient recommends a staged approach rather than an either-or decision. The suggested path is to start with high-impact generative AI use cases for faster returns, embed AI into workflows through copilots and conversational experiences, and then pilot agentic capabilities in selected high-value processes. In parallel, enterprises should strengthen data readiness, systems integration, governance, security and workforce adoption so autonomy can scale responsibly.