Learn how to move from AI hype to measurable outcomes with domain‑specific, responsible agentic AI that actually works in HCM, SCM and Finance environments.
This paper shows how to close the gap between AI ambition and operational reality.
Why generic AI fails in production
Understand why general‑purpose LLMs struggle with data chaos, and enterprise grade compliance and how this leads to hidden risk, rework, and stalled programs.
How domain‑specific, agentic AI succeeds
See how models trained on enterprise application concepts, tasks, and change patterns deliver higher inference accuracy on impact analysis, testing, and optimization.
3 steps to responsible agentic AI adoption
Learn a practical path to:
1. Build a shared understanding of agentic AI
2. Adopt domain‑specific agents with end‑to‑end native functionality
3. Follow a clear implementation playbook.
5 best practices for safe, responsible AI adoption and governancey
Get actionable guidance on choosing purpose‑built agents, demanding high inference accuracy, staying stack‑agnostic, keeping humans in the loop, and following responsible AI principles.
What year one really looks like
Follow a quarter‑by‑quarter roadmap—from initial governance and pilot use cases to cross‑stack automation and measurable gains in quality, speed, and control.
This guide is designed for leaders responsible for both innovation and risk: