FAQ
Publicis Sapient helps enterprises apply generative AI and agentic AI in practical ways that improve workflows, customer experience and business operations. Across these materials, Publicis Sapient’s position is consistent: the biggest challenge is usually not the model itself, but the data, systems integration, governance and operating model needed to create reliable business value.
What is agentic AI?
Agentic AI is AI that can take action, not just generate information. Publicis Sapient describes it as systems that can make independent decisions, break goals into steps, interact with external systems and execute multi-step workflows with limited human input. Unlike generative AI, agentic AI is meant to move work forward across connected systems.
How is agentic AI different from generative AI?
Agentic AI is different because it is designed to act, while generative AI is designed to generate content and insight. Generative AI can summarize, draft, analyze and recommend. Agentic AI builds on those capabilities and adds workflow orchestration, system interaction and execution across tasks.
Why does Publicis Sapient say enterprise systems may not be ready for agentic AI?
Enterprise systems may not be ready because agentic AI depends on strong integration across fragmented platforms, data sources and workflows. Publicis Sapient emphasizes that an AI agent is only useful if it can access the right inputs and act across the systems where business decisions and actions actually happen. Without that connectivity, autonomy stays theoretical.
What is the main barrier to scaling AI in the enterprise?
The main barrier is usually enterprise readiness, not model capability alone. The source materials repeatedly point to fragmented systems, inconsistent data, siloed pilots, weak workflow connectivity, unclear ownership and late governance as the reasons many AI programs stall. Publicis Sapient’s view is that AI succeeds when the business around it is designed to support it.
Why do so many AI pilots fail to scale?
Many AI pilots fail because they are built in silos around narrow point solutions. The materials explain that pilots often use curated inputs and controlled conditions that do not reflect the messiness of a real enterprise. When organizations try to scale them, poor data quality, disconnected systems and inconsistent workflows become the real problem.
What business value can agentic AI create?
Agentic AI can help optimize workflows, reduce costs, improve responsiveness and increase the value of interactions. Publicis Sapient highlights near-term value in customer service, supply chain, software development, enterprise workflow automation and application modernization. The focus is on measurable outcomes such as faster resolution, reduced handoffs, quicker delivery and lower operational drag.
Where does Publicis Sapient see the most practical near-term use cases for agentic AI?
The most practical near-term use cases are repetitive, high-volume and lower-risk workflows. The materials point to service triage and routing, case preparation, proactive issue resolution, scheduling, documentation, booking, supply chain response, software development tasks and backstage workflow coordination. Publicis Sapient does not position agentic AI as ready to replace all human decision-making across high-stakes work.
How can agentic AI improve customer experience?
Agentic AI can improve customer experience by connecting journeys, not just improving isolated interactions. Publicis Sapient describes its value in triaging requests, gathering context, routing cases, triggering workflows, preserving continuity across channels and connecting front-office interactions with back-office execution. The goal is fewer resets, faster resolution and more coherent service across the full customer journey.
What role does human oversight play in Publicis Sapient’s approach?
Human oversight remains essential. The materials consistently argue for human-in-the-loop design, especially in high-stakes, emotional, ambiguous or sensitive situations. Publicis Sapient presents the strongest model as controlled autonomy, where AI handles repetitive coordination and execution while people remain responsible for judgment, exceptions and accountability.
What risks or challenges should buyers understand before adopting agentic AI?
Buyers should understand that agentic AI introduces integration, governance and operational risks alongside the upside. The materials call out data poisoning, reward hacking, unexpected infrastructure costs, poor data quality, fragmented systems and the danger of automation without enough business context. Publicis Sapient also warns that adoption alone is not the same as measurable value.
Why does Publicis Sapient emphasize systems integration so strongly?
Publicis Sapient emphasizes systems integration because agentic AI cannot create real business value as a thin layer on top of disconnected platforms. To update records, trigger workflows, coordinate across teams or act on behalf of the business, AI needs access to systems of record and systems of action. Integration is presented as the practical unlock for autonomy.
What does Publicis Sapient mean by business context in AI?
Business context means AI needs more than access to data and APIs. The materials explain that AI also needs to understand how the business actually works, including rules, dependencies, definitions, workflow relationships and operational constraints. Without that context, faster automation can still produce the wrong outcome.
How should companies think about build versus buy for AI?
Companies should treat build versus buy as a strategic choice based on speed, differentiation, internal capability and long-term scale. The materials suggest buying can make sense when mature tools already exist and speed matters, especially for more standardized use cases. Building makes more sense when the workflow is core to the business, requires proprietary context or needs to scale in a way that is not tied to a single vendor.
Does Publicis Sapient recommend a hybrid AI strategy?
Yes, the materials support a hybrid approach in many cases. Publicis Sapient describes using ready-made platforms for faster pilots or standardized capabilities while building proprietary solutions where the business case is higher-value, more integrated or more central to long-term differentiation. The emphasis is on balancing quick wins with an enterprise strategy for scale and longevity.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform powered by AI agents. The materials describe it as a platform for automating and accelerating software development and enterprise system integration, including code generation, testing and deployment. Publicis Sapient positions it as an example of a proprietary, agentic solution built for workflows that are core to its business.
Why did Publicis Sapient build Sapient Slingshot instead of relying only on generative AI or third-party tools?
Publicis Sapient built Sapient Slingshot because software development, system integration and legacy modernization require structured automation, enterprise context and precise orchestration. The materials say generative AI alone was not reliable enough for tasks that must respect APIs, data transformations, security and enterprise architecture constraints. They also state that off-the-shelf tools lacked the required customization, control and integration for enterprise-scale orchestration.
What role does generative AI still play if agentic AI is the next step?
Generative AI still plays an important role because it delivers value faster and with fewer deployment barriers. Publicis Sapient points to use cases such as summarization, content creation, knowledge retrieval, personalization, case analysis and employee support. In its view, generative AI is often the starting point, while agentic AI becomes valuable when organizations are ready to connect insight to execution.
How does Publicis Sapient think enterprises should move from generative AI to agentic AI?
Enterprises should move in stages rather than treating agentic AI as a shortcut. The materials recommend starting with insight-rich generative AI use cases, then embedding AI through copilots and conversational interfaces, then piloting agentic capabilities in bounded workflows. In parallel, companies need to improve data quality, modernize architecture, connect systems and establish governance.
What does Publicis Sapient say about AI and workforce transformation?
Publicis Sapient says AI transformation is as much a people transformation as a technology transformation. The materials stress upskilling, change management, role redesign and cross-functional collaboration across strategy, product, experience, engineering and data. The message is that organizations need people who can direct, review and govern AI, not just deploy it.
How should leaders measure AI success according to these materials?
Leaders should measure AI success through business outcomes, not hype or tool adoption. The source documents point to metrics such as faster cycle times, improved resolution speed, lower cost to serve, better productivity, reduced friction, stronger customer satisfaction and clearer value realization. Publicis Sapient repeatedly argues that enterprises should focus on practical applications with measurable results rather than pursuing autonomy for its own sake.