FAQ
Publicis Sapient helps organizations understand, design and scale generative AI and agentic AI for enterprise transformation. Its approach focuses on practical business use cases, connected systems, governance, human oversight and proprietary platforms that support workflow automation and business execution.
What is agentic AI?
Agentic AI is AI designed to act, not just generate. Publicis Sapient describes it as autonomous systems that can make decisions, break goals into tasks, interact with connected systems and execute multi-step workflows with minimal human intervention. The emphasis is on moving from insight generation to action.
What is an agentic AI workflow?
An agentic AI workflow is a self-directed, multi-agent system that coordinates specialized AI agents to complete complex processes in real time. Publicis Sapient describes these workflows as connecting agents, enterprise systems and data so they can adapt and execute without constant human handoffs. The goal is to link decisions directly to execution.
How is agentic AI different from generative AI?
Agentic AI differs from generative AI because it is built to execute actions, while generative AI is built to create content or recommendations. Generative AI is typically used for drafting, summarizing and assisting, and agentic AI is used to plan, decide and carry work across systems. Publicis Sapient also notes that agentic AI usually requires deeper integration, stronger guardrails and more oversight.
Why does Publicis Sapient say systems integration is essential for agentic AI?
Systems integration is essential because agentic AI cannot operate autonomously without access to the systems where business work actually happens. Publicis Sapient repeatedly argues that without deep, real-time integration across platforms, autonomy stays theoretical. In practice, that means connecting data sources, APIs and enterprise applications so agents can both understand context and take action.
What business problems is agentic AI meant to solve?
Agentic AI is meant to reduce bottlenecks caused by fragmented systems, manual approvals and repetitive coordination work. Publicis Sapient positions it as especially useful where teams are slowed by disconnected platforms, delayed handoffs or siloed data. The intended outcomes are faster execution, lower manual effort and more consistent responsiveness.
What kinds of workflows are the best fit for agentic AI today?
The best-fit workflows are repetitive, bounded, high-volume and time-sensitive processes. Publicis Sapient highlights customer service, supply chain response, internal task orchestration, software delivery, documentation flows and lending operations as practical starting points. The strongest near-term use cases are the ones where value is clear and risk can be governed.
How does an agentic AI workflow work in practice?
An agentic AI workflow works by combining specialized agents, enterprise integrations, real-time data and orchestration logic. One agent may gather information, another may monitor activity, another may identify opportunities or risks and another may trigger the next action. Publicis Sapient’s examples show agents operating across CRM, ERP, communication, financial, scheduling and workflow systems.
What technical components power agentic AI workflows?
Agentic AI workflows are powered by autonomous agents, an integration layer, data repositories and decision engines, plus security and compliance controls. Publicis Sapient mentions machine learning agents, natural language processing agents, computer vision agents and reinforcement learning agents. It also points to graph databases, event-driven architecture, AI-powered knowledge graphs and identity and security platforms as key parts of the technical foundation.
What enterprise systems usually need to be connected?
The systems that usually need to be connected include CRM, ERP, supply chain, marketing automation, communication, scheduling, identity and security platforms. Depending on the use case, Publicis Sapient also references EHRs, financial data sources, news feeds and internal knowledge repositories. The exact mix varies by workflow, but interoperability is the consistent requirement.
How can agentic AI support sales teams?
Agentic AI can support sales teams by automating research, monitoring engagement signals, identifying opportunities and drafting personalized outreach. In Publicis Sapient’s proactive salesperson example, separate agents handle business research, CRM monitoring, relationship analysis and outreach generation. The purpose is to help sales teams act on the right opportunities faster while reducing manual work across disconnected tools.
How can agentic AI improve customer experience?
Agentic AI can improve customer experience by connecting answers to action across customer journeys. Publicis Sapient highlights use cases such as service triage and routing, proactive issue resolution, journey orchestration across channels, supply-chain-informed service responses and backstage workflow automation. The focus is on continuity and resolution, not just better responses.
How can agentic AI support software development and modernization?
Agentic AI can support software development and modernization by automating parts of code generation, testing, deployment and legacy transformation. Publicis Sapient describes Sapient Slingshot as a proprietary AI platform that uses AI agents across the software development lifecycle. The company positions this as a way to reduce bottlenecks, shorten timelines and make modernization more efficient.
What is Sapient Slingshot?
Sapient Slingshot is Publicis Sapient’s proprietary AI platform for software development, enterprise system integration and modernization. The source materials describe it as an ecosystem of AI agents that helps automate code generation, testing and deployment. Publicis Sapient presents it as better suited than generic tools for complex enterprise environments that need customization, precision and stronger controls.
What is Sapient Bodhi?
Sapient Bodhi is Publicis Sapient’s agentic platform for complex enterprise workflows. In the source materials, Bodhi is described as supporting deep research, multi-agent orchestration and complex, regulated use cases such as commercial lending. Publicis Sapient positions Bodhi as a platform that helps agents operate with context, governance and workflow visibility.
What is an enterprise context graph, and why does it matter?
An enterprise context graph is a living map of how a business actually operates. Publicis Sapient describes it as exposing relationships between decisions, data, rules, workflows and software so AI can reason with business context instead of isolated data points. The company presents this as a foundation that helps AI agents work more safely and effectively inside the enterprise.
What are the main risks and challenges with agentic AI?
The main risks and challenges include poor systems integration, weak data quality, privacy and security issues, governance gaps, unexpected infrastructure costs and change management problems. Publicis Sapient also specifically mentions data poisoning, reward hacking and unintended autonomous actions. Its broader point is that agentic AI carries more operational risk than assistive AI because it can take real actions across business workflows.
Why is human oversight still necessary?
Human oversight is still necessary because businesses remain accountable for AI-driven decisions and actions. Publicis Sapient recommends human-in-the-loop models so people can review, refine, validate or override AI behavior when needed. This is presented as especially important in high-stakes, ambiguous or regulated environments.
What security, privacy and compliance measures should organizations plan for?
Organizations should plan for security, privacy and compliance controls from the start. Publicis Sapient references zero-trust security layers, access controls, identity and access management, audit logging, AI ethics guardrails, policy enforcement and PII anonymization. The source materials also mention compliance expectations such as GDPR and CCPA in relevant workflows.
How can an organization tell if it is ready for agentic AI?
An organization is ready for agentic AI when it has interoperable systems, scalable infrastructure, governed data and clear oversight mechanisms. Publicis Sapient’s readiness criteria include API and event-driven architecture maturity, cloud and data readiness, security controls and the ability to identify high-value pain points. If those foundations are weak, the company suggests strengthening them before scaling autonomy.
What roadmap does Publicis Sapient recommend for getting started?
Publicis Sapient recommends a phased roadmap rather than a jump to full autonomy. The typical sequence is discovery and technical assessment, a proof of concept, broader workflow execution and continuous optimization. Across the source materials, the company also recommends starting with high-value, bounded workflows and expanding only after integration, governance, observability and human oversight are in place.
When should a company use generative AI, agentic AI or both?
A company should use generative AI for faster wins in content-heavy, knowledge-based and lower-risk workflows, and use agentic AI for more complex processes that require real-time decisions and action across systems. Publicis Sapient also describes a hybrid approach as the most practical path for many enterprises. In that model, generative AI creates immediate value while agentic AI is piloted and scaled where the workflow importance and enterprise readiness justify the added complexity.
What does Publicis Sapient offer organizations pursuing agentic AI?
Publicis Sapient offers strategy, implementation guidance, industry-specific use cases and proprietary platforms to help organizations adopt agentic AI responsibly. The source materials specifically mention Sapient Slingshot, Sapient Bodhi and the enterprise context graph as part of that approach. Publicis Sapient positions its role as helping enterprises move from experimentation to governed, scalable business transformation.