From Generative to Agentic AI: A Practical Roadmap for Enterprise Transformation

Artificial intelligence is rapidly evolving, and the conversation among business leaders is shifting from the promise of generative AI to the transformative potential of agentic AI. While generative AI has delivered rapid wins in content creation, automation, and customer engagement, agentic AI is emerging as the next frontier—enabling autonomous decision-making and orchestrating complex workflows across the enterprise. For transformation leaders, CIOs, and COOs, the challenge is no longer about experimentation, but about scaling impact and operationalizing AI maturity. This guide provides a hands-on, actionable roadmap to help organizations move up the AI maturity curve, from generative to agentic AI.

Understanding the Shift: Generative AI vs. Agentic AI

Generative AI refers to machine learning models—such as large language models (LLMs) and generative adversarial networks (GANs)—that create new content by learning from vast datasets. These models excel at:

• Drafting marketing copy, product descriptions, and code
• Summarizing reports and automating documentation
• Powering chatbots and digital assistants

Generative AI’s lower deployment barriers and broad applicability have made it the dominant force in today’s AI market, delivering rapid ROI through automation and efficiency gains. Typical deployments require minimal integration and human oversight.

Agentic AI, by contrast, represents a leap forward in autonomy. These systems are designed to pursue complex goals independently, making decisions, planning, and executing multi-step workflows with minimal human intervention. Agentic AI is not a single technology, but an orchestration of multiple AI disciplines—including generative AI, natural language processing, machine learning, and systems integration—working together to:

• Act autonomously across interconnected systems
• Adapt to real-time data and unforeseen events
• Orchestrate end-to-end business processes

While agentic AI offers greater potential for business transformation, it also introduces complexity in integration, governance, and risk management.

The AI Maturity Curve: From Content to Autonomy

1. Experimentation with Generative AI
   • Use cases: Content creation, chatbots, automated documentation
   • Requirements: Clean, well-labeled data; basic governance; human review
   • Value: Quick wins, productivity gains, cost savings
2. Scaling Generative AI
   • Use cases: Personalized marketing, automated reporting, knowledge assistants
   • Requirements: Data integration, upskilling workforce, ethical guidelines
   • Value: Enhanced customer experience, operational efficiency
3. Piloting Agentic AI
   • Use cases: Workflow automation, supply chain orchestration, autonomous customer service
   • Requirements: Systems integration, real-time data flows, advanced governance
   • Value: Transformational automation, faster decision-making, new business models
4. Enterprise-Scale Agentic AI
   • Use cases: Dynamic pricing, real-time risk management, end-to-end process automation
   • Requirements: Modernized legacy systems, robust APIs, continuous monitoring, human-in-the-loop oversight
   • Value: Competitive differentiation, agility, scalable innovation

Technical and Operational Steps to Move Up the Curve

1. Data Readiness

• Clean, well-governed data is the foundation for both generative and agentic AI. Invest in data quality, integration, and governance to ensure your AI systems have reliable inputs.
• AI-ready data should be accurate, relevant, well-structured, and properly labeled. Implement feedback loops and quality controls to maintain data integrity over time.

2. Systems Integration

• Agentic AI demands deep integration with enterprise systems (ERP, CRM, supply chain, etc.), robust APIs, and real-time data flows. Modernize legacy systems and break down data silos to enable seamless orchestration.
• Start with targeted pilots in high-value workflows where integration complexity is manageable, then scale as capabilities mature.

3. Governance and Risk Management

• Establish ethical AI frameworks that address privacy, security, fairness, transparency, and accountability.
• Implement human-in-the-loop oversight for agentic AI, especially in high-stakes or regulated environments. Continuous monitoring and audit trails are essential.
• Balance innovation and risk: A zero-risk policy is a zero-innovation policy. Use a portfolio approach to balance quick wins with longer-term, higher-value investments.

4. Workforce Upskilling and Change Management

• Upskill employees at all levels to collaborate with AI, focusing on oversight, quality control, and creative problem-solving.
• Redesign roles and workflows to leverage AI strengths—shifting from manual execution to managing, validating, and optimizing AI-driven processes.
• Foster a culture of experimentation and continuous learning. Encourage teams to seek out innovation and disruption.

Industry-Specific Use Cases

• Retail:
  • Generative AI: Automated product descriptions, personalized marketing content
  • Agentic AI: Dynamic pricing and inventory agents that adjust stock and prices in real time based on demand signals
• Financial Services:
  • Generative AI: Summarizing financial reports, automating customer communications
  • Agentic AI: Real-time financial assistants that monitor spending, predict cash flow issues, and recommend personalized loan options
• Healthcare:
  • Generative AI: Medical scribe tools that transcribe and summarize patient visits
  • Agentic AI: Agents that automate prior authorization approvals, manage clinical trial registrations, and integrate with EHRs
• Supply Chain:
  • Generative AI: Automated reporting and documentation
  • Agentic AI: Agents that reroute inventory, optimize logistics, and respond to disruptions autonomously

Readiness Checklist: Preparing for Agentic AI

1. Data Readiness: Is your data clean, well-governed, and accessible?
2. Systems Integration: Are your legacy systems and APIs modernized for agentic workflows?
3. Risk Management: Do you have ethical AI frameworks, human-in-the-loop oversight, and continuous monitoring in place?
4. Workforce Upskilling: Are your teams trained to collaborate with AI, focusing on oversight, quality control, and creative problem-solving?
5. Portfolio Approach: Are you balancing quick wins from generative AI with targeted investments in agentic AI for transformational value?

When to Invest: Generative vs. Agentic AI

• Generative AI is ideal for:
  • Rapid content creation (marketing, documentation, customer communications)
  • Automating routine, low-risk tasks
  • Quick wins that don’t require deep system integration
• Agentic AI is best for:
  • Automating complex, multi-step workflows (e.g., supply chain, software development, claims processing)
  • Scenarios where real-time decision-making and action are essential
  • High-value, high-volume processes where efficiency and speed are critical
• Hybrid Approach:
  • Most organizations will benefit from a hybrid strategy: deploy generative AI for immediate returns, while piloting agentic AI in targeted, high-impact areas. For many, third-party agentic solutions can automate non-core tasks, while proprietary agentic platforms are reserved for mission-critical workflows.

Best Practices for Business Leaders

• Start with high-value, low-risk workflows: Pilot agentic AI where automation can deliver measurable value without high risk.
• Invest in customization: Off-the-shelf solutions may not offer the integration, security, or context required for enterprise-scale agentic AI. Consider proprietary platforms for your most critical workflows.
• Prioritize human-AI collaboration: Blend human judgment with AI-driven automation, using oversight and creative prompting to ensure quality and innovation.
• Measure and iterate: Use real-time metrics to track AI impact, gather feedback, and continuously refine tools, workflows, and training.

The Road Ahead: Operationalizing AI-Driven Transformation

The shift from generative to agentic AI is not a matter of if, but when. Early adopters who solve the integration and governance challenges first will gain a lasting competitive edge. The most successful enterprises will embrace a pragmatic, hybrid approach—leveraging generative AI for immediate gains while building the foundations for agentic AI to transform their most critical workflows.

Publicis Sapient stands at the forefront of this evolution, with proprietary agentic platforms, deep expertise in digital business transformation, and a proven track record of delivering measurable results. Our approach blends strategy, engineering, and change management to help clients modernize legacy systems, design robust AI governance, upskill teams, and accelerate innovation while managing risk.

Ready to explore the next frontier of AI-driven business transformation? Connect with Publicis Sapient to discover how generative and agentic AI can drive your business forward.

Relevant Links

• The Top 5 Generative AI Trends in 2025
• The Top 5 Generative AI Trends in 2025
• Generative AI in Regulated Industries: Navigating Compliance, Security, and Risk
• Générative IA et Agentic IA : Le nouveau duo de la transformation numérique en Europe (Europe)
• De la IA Generativa a la IA Agéntica: El Futuro de la Automatización Empresarial en América Latina (LATAM)