The Eventuality of the AI Lifecycle
When it comes to AI, machine learning, and advanced analytics, there is one undeniable conclusion:
The biggest risk in AI today is not implementing AI.
Data can stream from devices, channels, exchanges, and other points of origin (e.g., phones, drones, homes, vehicles, inspectors, vendors, adjusters, customers, etc.) both continuously and on demand, including two-way and multi-party interactions. This makes AI more of a pipeline than a point decision.
Data pours into the pipes and forms streams of information via data identification, transformation, verification, and authentication, and is combined with additional data to permission decisions across the insurance value chain. Chaining decisions into end-to-end experience pathways is common.
But many companies are struggling with ”early days” issues: data governance, privacy, security, cloud management, upskilling staff, model risk management, and AI operations lifecycle management.This is a natural consequence of viewing AI initiatives as small projects rather than a product requiring ongoing maintenance and long-term investments.
Getting AI from the sandbox to production means upping the readiness of IT teams to provision, stream, protect, and operate AI systems as they transition from an analytic project and proof of concept into a product requiring lifecycle management. Steady governance and a cultural maturity for data-driven decisions will help you become successful and remain successful.
Sunsetting “project-ism” is the new call to action for AI success and emerges as essential to exceptional experiences with data-driven decisioning.
Investing to succeed with AI as a lifecycle is the way to succeed.