FAQ
Publicis Sapient helps organizations understand, design, and scale agentic AI workflows for enterprise transformation. Its content describes how agentic AI differs from generative AI, where it creates business value, what technical foundations it requires, and how enterprises can move from pilots to production responsibly.
What is agentic AI?
Agentic AI is an autonomous system, often made up of multiple AI agents, that can perceive context, make decisions, and execute multi-step tasks with minimal human intervention. Unlike tools that only generate content or recommendations, agentic AI can take action across connected systems. Publicis Sapient describes it as a move from insight generation to workflow orchestration.
What is an agentic AI workflow?
An agentic AI workflow is a self-directed, multi-agent system that coordinates AI entities to complete complex processes in real time. These workflows connect multiple agents, data sources, and enterprise systems so they can adapt and act without constant human handoffs. Publicis Sapient frames this as an enterprise nervous system that links insight to execution.
How is agentic AI different from generative AI?
Agentic AI differs from generative AI because it is designed to act, not just create. Generative AI is typically used for content creation, summarization, and suggestions, while agentic AI is built for autonomous decision-making and end-to-end execution. The source materials also note that generative AI usually requires less integration and carries lower operational risk, while agentic AI requires deeper enterprise integration, stronger governance, and more oversight.
What business problem does agentic AI solve?
Agentic AI helps reduce bottlenecks caused by fragmented systems, manual approvals, and disconnected workflows. It is intended to improve speed, responsiveness, and execution by linking data, decisions, and actions across systems in real time. Publicis Sapient positions it as especially valuable where human teams are slowed down by repetitive coordination work or delayed by system silos.
What kinds of tasks can agentic AI handle?
Agentic AI can handle multi-step workflows such as customer service resolution, supply chain response, sales outreach, claims processing, software delivery, and administrative coordination. The common pattern is that agents gather context, break work into steps, interact with enterprise systems, and trigger next actions automatically. The documents emphasize that the strongest near-term use cases are repetitive, high-volume, and tightly governed processes.
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 behavior, another may identify opportunities or risks, and another may trigger outreach or execution. Publicis Sapient’s examples show these workflows operating across CRM, ERP, communication tools, financial data sources, scheduling systems, and other connected platforms.
What technical components power agentic AI workflows?
Agentic AI workflows are powered by autonomous agents, an integration layer, data repositories and decision engines, and security and compliance modules. The source materials mention machine learning agents, natural language processing agents, computer vision agents, reinforcement learning agents, graph databases, event-driven architecture, AI-powered knowledge graphs, and identity and security platforms. These components work together so AI can understand context, act across systems, and operate with guardrails.
Why is systems integration so important for agentic AI?
Systems integration is essential because agentic AI cannot act autonomously without access to the systems where decisions and actions happen. Publicis Sapient repeatedly states that without deep, real-time integration across fragmented enterprise platforms, autonomy remains theoretical. In practice, that means connecting APIs, modernizing legacy systems, and enabling reliable data flows across tools like CRM, ERP, supply chain, EHR, and communication platforms.
What enterprise systems typically need to be connected?
The systems that typically need to be connected include CRM, ERP, supply chain, marketing automation, communication, scheduling, identity, and security platforms. Depending on the use case, this can also include EHR systems, financial data sources, news feeds, and internal knowledge repositories. The exact mix depends on the workflow, but the consistent requirement is cross-system interoperability.
What are common use cases for agentic AI by industry?
Common use cases include dynamic pricing and inventory optimization in retail, real-time risk management and personalized engagement in financial services, clinical and administrative automation in healthcare, and autonomous logistics in supply chain operations. Publicis Sapient also highlights software development, application modernization, customer service, and internal enterprise workflows as strong use cases. Across industries, the recurring value comes from faster decisions, lower manual effort, and better coordination across systems.
How can agentic AI support sales and customer-facing teams?
Agentic AI can support sales and customer-facing 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 goal is to help teams act on the right opportunities faster while reducing manual work across disconnected tools.
How can agentic AI support healthcare workflows?
Agentic AI can support healthcare by automating patient intake, prior authorization, claims management, discharge planning, and care coordination. The source materials describe agents that extract data from documents, verify insurance eligibility, integrate with EHRs, summarize patient histories, and trigger follow-up actions. Publicis Sapient also stresses that healthcare deployments require strong compliance, auditability, and human oversight.
How can agentic AI support software development and modernization?
Agentic AI can support software development by automating code generation, testing, deployment, and parts of legacy modernization. Publicis Sapient describes Sapient Slingshot as a proprietary platform that uses AI agents to accelerate the software development lifecycle. The source materials position this as a way to reduce bottlenecks, shorten timelines, and make legacy modernization more efficient.
What are the main risks and challenges of agentic AI?
The main risks and challenges include poor systems integration, weak data quality, security and privacy issues, governance gaps, unexpected infrastructure costs, and change management challenges. The materials also specifically mention risks such as data poisoning, reward hacking, and unintended autonomous actions. Publicis Sapient consistently recommends human-in-the-loop oversight, continuous monitoring, audit logging, and strong guardrails to manage those risks.
Why is human oversight still necessary?
Human oversight is still necessary because agentic AI can take actions with operational, financial, or regulatory consequences. Publicis Sapient recommends human-in-the-loop models so people can review, validate, refine, or override AI decisions when needed. This is presented as especially important in high-stakes environments such as healthcare, financial services, regulated workflows, and enterprise decision-making.
What security, privacy, and compliance measures should organizations consider?
Organizations should plan for access controls, audit logging, privacy protections, and policy enforcement from the start. The source materials reference zero-trust security layers, AI ethics guardrails, regulatory compliance engines, identity and access management, PII anonymization, and logging through SIEM solutions. Publicis Sapient also highlights compliance requirements such as GDPR, CCPA, and healthcare-specific privacy expectations where relevant.
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 cloud and data infrastructure, appropriate human oversight, and clear security and governance policies. Publicis Sapient’s readiness criteria also include data quality, API and event-driven architecture maturity, workforce upskilling, and the ability to identify high-value pain points. If those foundations are weak, the materials suggest addressing them before pursuing broader autonomy.
What is the recommended roadmap for getting started?
The recommended roadmap starts with discovery and technical assessment, then moves to a proof of concept, broader workflow execution, and continuous optimization. Publicis Sapient advises organizations to audit core systems, map data flows, assess security and compliance, and validate early agents in a controlled environment before scaling. The documents also recommend starting with high-value, low-risk workflows and expanding only after integration, governance, and observability are in place.
Should companies start with full autonomy right away?
No, companies should not start with full autonomy right away. The source materials recommend starting with bounded, high-value use cases where risk is manageable and outcomes can be measured clearly. Publicis Sapient repeatedly argues for a pragmatic, phased approach that blends automation with human oversight rather than pushing immediately into fully hands-off execution.
When should a company choose generative AI, agentic AI, or both?
A company should choose generative AI for fast wins in content-heavy, lower-risk workflows and choose agentic AI for complex, high-value 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 delivers immediate value while agentic AI is piloted and scaled in more critical workflows as data, integration, and governance mature.
What does Publicis Sapient offer for organizations pursuing agentic AI?
Publicis Sapient offers strategy, implementation guidance, industry-specific use cases, and proprietary platforms designed to accelerate agentic AI adoption. The source materials specifically mention Sapient Slingshot and, in some documents, Bodhi as proprietary platforms that support integration, workflow automation, security, and compliance. Publicis Sapient positions its role as helping organizations move from experimentation to scalable, governed business transformation.