Dynamic Process Landscape: A Strategic Guide to Successful AI Implementation
Released: 06/02/2025
Artificial Intelligence (AI) adoption in business and manufacturing is failing at least twice as often as it succeeds. Companies are trying to integrate AI into outdated process structures that lack transparency, adaptability, and real-time data integration. Without a clear understanding of business processes, data flow, and regulatory requirements, automation efforts lead to fragmentation, inefficiencies, and compliance risks.
This publication by the CSA AI Governance and Compliance Working Group presents the Dynamic Process Landscape (DPL) framework. The DPL aligns AI-driven automation with business strategy, compliance, and real-time adaptability. Unlike traditional rule-based process houses, the DPL provides a modular and flexible approach. This ensures that companies use AI technology where it truly adds value.
Using the DPL can transform AI from a risky experiment into a resilient, explainable, and scalable strategic asset.
Key Takeaways:
- How to establish process transparency. AI-driven automation is only as good as the processes it supports.
- How to integrate data management into business processes. Without proper data governance, AI agent decisions can be unreliable, non-compliant, or even detrimental.
- The drawbacks of traditional process management frameworks. These frameworks present significant limitations when integrating AI-driven automation into modern, data-intensive workflows.
- How to implement the Dynamic Process Landscape. The DPL framework allows businesses to prioritize and adapt processes dynamically based on data and operational needs, ensuring resilience in fast-changing environments.
Best For:
Companies planning to adopt AI tools
