Many organizations struggle with AI initiatives that fail to scale, suffer from data integrity issues, or deliver conflicting insights. The real issue? It’s not AI itself—it’s your organizational structure.
Without a deliberate design for AI effectiveness, companies fall into common traps:
🔹 Debunk, Defend, Dismiss – Resistance to AI insights due to misaligned analytics ownership.
🔹 Spaghetti-on-the-Wall Strategy – Scattered AI use cases with no enterprise cohesion.
🔹 Blame the Data – Siloed or inaccessible data preventing reliable AI outputs.
AI should function as an enterprise utility—as accessible and reliable as electricity. But achieving this requires deliberate organizational design, not just technology investments.
At Org.Works™, we advocate for the CFD Organization Model for Enterprise AI, a three-layer framework that ensures AI functions as an enterprise-wide utility:
✅ Center of Enablement (CoE): Standardizes AI tools, governance, and data strategy.
✅ Federated Data Science: Empowers functional teams to develop AI models that align with business goals.
✅ Democratized Data & Insights: Enables employees to leverage AI for better decision-making.
To succeed with AI, organizations must fix structural barriers—from leadership alignment to unified KPIs, enterprise-wide data access, and platform standardization.
AI isn’t just a technology challenge; it’s an organizational design challenge. If your AI efforts are falling short, the root cause may be your company’s structure.
Read the full article to learn how to fix it: https://www.linkedin.com/pulse/failing-ai-may-get-worse-dr-janet-sherlock-pdlzf/?trackingId=r85gOoGIQNeJYJpeW1SmGQ%3D%3D
Also available on Medium: https://medium.com/@janetsherlock/failing-at-ai-it-may-get-worse-5a5b09806867
