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.

This article discusses the challenges of implementing AI in enterprise settings, termed “pilot purgatory,” where organizations struggle to derive tangible benefits despite promising AI demonstrations. It emphasizes defining decision-making processes and aligning AI initiatives with corporate financial goals to overcome barriers such as vague metrics and governance issues. The authors propose a framework for scaling AI effectively by prioritizing decisions, measuring impacts, and ensuring governance while focusing on creating decision support systems that can provide accountability and clarity in AI-driven outcomes.

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Published via Towards AI