How to Scale Enterprise AI Decision-Making with Measurable P&L Impact and Governance-Ready Proof

Author(s): Manbir T

Originally published on Towards AI.

How to Scale Enterprise AI Decision-Making with Measurable P&L Impact and Governance-Ready Proof

If you’ve had multiple “promising” AI demos but nothing moving the needle, you’re not alone. Pilot purgatory is common in enterprise AI because teams build models before they define the decision. That makes success squishy-what metric, which owner, which P&L lever? Add data readiness gaps, scattered prototypes, and governance fears, and you get months of motion with no outcome.

The article discusses the challenges of implementing effective AI decision-making within enterprises, emphasizing the importance of defining decisions before developing models to avoid pitfalls like “pilot purgatory.” It outlines a framework for aligning AI initiatives with business priorities, highlighting key factors such as decision impact, governance, and the development of decision support systems. Steps include identifying priority decisions, designing metrics, and establishing architectural foundations to ensure measurable Profit and Loss impact.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI