Strategic advice & alerts to leverage data analytics & new technologies
Leverage the latest advances in areas ranging from data analytics to IoT, social/mobile to wearables, Agile data warehousing to data architecture, with a continuous flow of written and multimedia research from Cutter’s global team of thought leaders.
In this Update, I delve deeper into the importance of the level at which analytics are performed — in particular, the need to pay attention to two keywords: granularity and context. In the absence of awareness of granularity and context, analytics may not provide the necessary value to the business and, as a result, increase business risks.
Building AI systems is a huge undertaking. Therefore, most companies should focus on helping employees adjust to the new world of AI, curating the right data and leaving the mechanics of building AI systems to vendors.
This Executive Update discusses the thinking behind employing artificial intelligence (AI) in an organization through “augmentation.” It presents a case study on how a superregional bank implemented a cognitive contact center by using an AI framework called HALO — Human Augmented Learning Organization — and showcases the meaning of “AI as a practice” within an organization.
Whether AI eventually lives up to all the hype obviously remains to be seen; however, I expect that we are going to witness some innovative and disrupting applications in the not-too-distant future.
Designing cognitive computing systems (CCSs) requires a strong case for the investment into those systems. Organizations must not only be able to justify the initial investment into developing a CCS, but also think through the investments that will be needed to ensure it can be refined and enhanced over time.
This Executive Update presents a framework in the form of processes, techniques, measurement metrics, and best practices to guide you toward successful API monetization.
Based on responses so far, an ongoing Cutter Consortium survey on the adoption and application of AI technology provides some insight into the issue of enterprise AI adoption trends.
Keng Siau and Weiyu Wang examine prevailing concepts of trust in general and in the context of AI applications and human-computer interaction in particular. They discuss the three types of characteristics that determine trust in this area: human, environment, and technology. They emphasize that trust building is a dynamic process and outline how to build trust in AI systems in two stages: initial trust formation and continuous trust development.