Many AI practitioners and those exploring AI solutions expect to reap the benefits of AI technologies simply by adopting licensed API services in the cloud. However, in many cases, an out-of-the-box solution is not enough to solve ongoing business challenges. A three-month pilot implementation is usually only enough to demonstrate the value of the concept within a narrow scope. A scalable, business-driven solution requires significant work beyond training a high-accuracy AI model. Application development and delivery pros should understand the seven myths we summarized and adjust their expectations. Collecting experimental data, cooperating on system design, and training employees on new workflows are long-term iterative processes — all the more reason not to push an underperforming pilot AI project to production.
To realize the value of AI technology at your company, Forrester suggests that you imbue your AI project with industry knowledge from four perspectives: performing joint technical and business research; preparing data; training employees; and establishing trust.
China Pacific Insurance (CPIC) worked with Baidu to transform its claims process with computer vision. CPIC’s story reveals the mindsets and implementation details needed to successfully embed computer vision capabilities into insurers’ systems and processes. It can also serve as a case study for firms in other industries.
If you’re interested in learning more, please take a look at our recent report, “Case Study: China Pacific Insurance Transforms Claims Management With Computer Vision.” And if you’d like to share some insights about your company’s strategy for emerging technologies, feel free to reach out to me directly or set up an inquiry by emailing email@example.com.