February 2, 2021
Rise of ‘Product-ism,’ Fall of ‘Project-ism’
Firms struggle because they view AI initiatives as small projects rather than a product requiring continuing maintenance and investment.
When it comes to AI, machine learning and advanced analytics, there is one undeniable conclusion: You need to get there now. 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, adjusters, etc.) both continuously and on demand.
This makes AI more of a pipeline than a product. Data pours into the pipes and forms streams of information via data identification, transformation, verification and authentication and is combined with additional data to permit decisions across the insurance value chain.
Sometimes, a process can be fully digital and self-serviced. Sometimes, an AI assist happens. Other times, a human is brought into the loop. Often, the human in the loop is also assisted with AI and analytics. New ways to let a customer do tasks remotely are expedited by AI.
Most companies today seem to implement AI solutions across their organizational chart use cases with three strategies: buy before build, collaborate with vendors and customize and invest in internal AI building efforts. Leading-edge companies have progressed from pilots and experimentation sandboxes all the way through the analytics operations pipeline journey — where data and AI operations engineers route data streams to fusion engines, then decision engines, then user endpoint actions in real time.
But many companies are struggling with ”early days” issues: data governance, privacy, security, cloud management, upskilling, 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 continuing 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 move from an analytic project and proof of concept into a product. 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 and emerges as essential to exceptional experiences with data-driven decision making.
You can find the full report here.