Agentic AI is changing enterprise automation. Unlike traditional predictive models or chat interfaces, AI agents can reason, plan, use tools, take actions and adapt across complex workflows. This creates significant opportunities for productivity and growth, but it also introduces new costs, dependencies and risks. Many organisations can build impressive pilots; far fewer can turn them into reliable, governed systems that deliver measurable business outcomes.
Agentic AI for Enterprise ROI provides a practical playbook for closing that gap. It introduces the Agentic Value Realisation Framework, a repeatable approach that connects use-case selection, agent architecture, enterprise integration, economics, governance, measurement and scaling. Readers will learn how to choose the right agent design patterns, connect agents to APIs and legacy platforms, evaluate token and action costs, establish runtime observability, and implement risk-based controls for autonomous systems.
Drawing on real-world experience across financial services, technology and regulated industries, the book shows how organisations can move from experimentation to production without treating governance as an afterthought. Cross-industry examples in banking, telecom, healthcare, retail, logistics and manufacturing demonstrate how agentic AI can reduce cycle times, improve decision quality, lower compliance effort and create new operating models. Clear, grounded and actionable, this book equips enterprise leaders, architects, product teams and governance professionals to build agentic systems that are valuable, reliable, safe and scalable.
What will you learn:
- How to identify agentic AI use cases with clear business value and measurable outcomes.
- How to apply the Agentic Value Realisation Framework from use-case selection through enterprise-scale deployment.
- How to evaluate the economics of agent workflows, including token usage, tool calls, retries, human intervention and cost per outcome.
- How to design agent architectures and integrate them with APIs, enterprise applications, data platforms and legacy systems.
- How to implement evaluation, observability, guardrails, risk tiering, human oversight and auditability for autonomous systems.
